Effective Business Intelligence Systems 9780313001192, 9781567203707

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EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS

EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS Robert J. Thierauf

QUORUM BOOKS Westport, Connecticut • London

Library of Congress Cataloging-in-Publication Data Thierauf, Robert J. Effective business intelligence systems / Robert J. Thierauf. p. cm. Includes bibliographical references (p. ) and index. ISBN 1–56720–370–1 (alk. paper) 1. Management information systems. 2. Business enterprises—Computer networks. I. Title. T58.6.T457 2001 658.4'038'011—dc21 00–062529 British Library Cataloguing in Publication Data is available. Copyright  2001 by Robert J. Thierauf All rights reserved. No portion of this book may be reproduced, by any process or technique, without the express written consent of the publisher. Library of Congress Catalog Card Number: 00–062529 ISBN: 1–56720–370–1 First published in 2001 Quorum Books, 88 Post Road West, Westport, CT 06881 An imprint of Greenwood Publishing Group, Inc. www.quorumbooks.com Printed in the United States of America TM

The paper used in this book complies with the Permanent Paper Standard issued by the National Information Standards Organization (Z39.48–1984). 10 9 8 7 6 5 4 3 2 1

Typically, information systems for decision makers have focused on selected data within a certain context to produce information. In addition, they have taken information accompanied by experience over time to generate important knowledge. Business intelligence systems move decision makers to the next level by providing them with a better understanding of a company’s operations so that they can outmaneuver competition.

Contents Figures Preface Abbreviations

ix xi xv

PART I Improving Decision-Making Effectiveness Using Business Intelligence Systems

1

1. Introduction to Effective Business Intelligence Systems

3

2. Creativity Underlies Effective Business Intelligence Systems PART II Underlying Structure of Effective Business Intelligence Systems

35 63

3. Effective Decision Making in a Business Intelligence Environment

65

4. Effective Systems and Software Found in Business Intelligence Systems

91

5. Data Warehousing and Computer Networking Found in Business Intelligence Systems

117

PART III Building Effective Business Intelligence Systems

155

6. Development and Implementation of Successful Business Intelligence Systems

157

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Contents

PART IV Effective Business Intelligence Systems Found in a Company’s Functional Areas

189

7. Strategic Intelligence in Corporate Planning

191

8. Tactical Intelligence in Marketing

233

9. Operational Intelligence in Manufacturing

269

10. Financial Intelligence in Accounting Selected Bibliography Index

309 351 357

Figures 1.1

An Effective Business Intelligence System Framework for a Typical Company

6

1.2

Relationship of Intelligence to Various Levels of Summarization

2.1

A Checklist of Important Traits Found in a Creative Manager

51

3.1

A Framework for a Decision-Processing System

68

3.2

Management Effectiveness in a Proactive Mode versus a Reactive Mode as Related to a Basis for Decision Making Using Data, Information, and Knowledge (i.e., Intelligence) for Understanding a Company’s Operations Thoroughly

74

3.3

A Comparison of Steps in the Problem-Solving Process: The Quantitative-Centered Approach and the Decision-Centered Approach

77

3.4

A Comparison of Steps in the Problem-Finding Process: The ProblemCentered Approach and the Opportunity-Centered Approach

82

4.1

A Listing of Popular Business Intelligence Software Vendors and Their Web Sites

104

A Listing of Popular Data Mining or Knowledge Discovery Software Vendors, Their Products, and Their Web Sites

110

A Listing of Popular Data Warehouse Vendors, Their Products, and Their Web Sites

133

5.2

The Building Blocks of an Intranet and Its Relationship to the Internet

141

5.3

The Utilization of a Three-Tiered Architecture for the Internet

146

7.1

Corporate Planning Principles Underlie Strategic Intelligence

201

7.2

From Executive Visioning to Corporate Objectives and Measurable Goals to Critical Success Factors (CSFs) to Key Performance Indicators (KPIs) and Financial Ratios within a BIS Environment

208

4.2 5.1

8

x 7.3

Figures (a) Total Sales, Cost of Goods Sold, Gross Margin, Selling and General Expenses, and Profits under Good, Average, and Poor Economic Conditions Five Years Hence and (b) Graph That Compares the Above Amounts Five Years Hence

219

A Pie Chart Comparison of Cost of Goods Sold, Selling and General Expenses, and Profits under Good and Poor Economic Conditions Five Years Hence

220

(a) Profits of Six New Products in the Forthcoming Six Months and (b) Graph That Compares a Company’s Six New Products in the Forthcoming Six Months

224

Pie Charts for a Company’s Best and Worst Profits in Terms of Six New Products over a Six-Month Period

225

8.1

Marketing Principles Based on Knowledge of Customers

241

8.2

Total Sales History for One Geographical Area of the United States over the Last Five Years

255

8.3

A Graphical Comparison of a Company’s Total Sales Amounts for One Geographical Area of the United States over the Last Five Years

256

(a) Return on Investment for a Company’s Five Proposed Products over Five Years and (b) Graph That Compares a Company’s Five Proposed Products over Five Years

261

9.1

Manufacturing Principles with Emphasis on Employing Knowledge

277

9.2

(a) Index Values for Monthly Performance of Five Buyers and (b) Graph That Compares the Five Buyers for This Month

292

9.3

A Pie Chart Comparison for a Company’s Best and Worst Buyers for This Month in Terms of Purchase Performance Index (PPI)

294

A Monthly Manufacturing Planning System That Manages the Error in Demand Planning for Individual Products

300

Analysis of Overheating Exception Code at the (a) First, (b) Second, and (c) Third Levels Using KnowledgeSEEKER

304

10.1

Accounting and Financial Principles Based on a Company’s Total Operations

318

10.2

(a) Direct Costing Amounts for a Company’s Five Principal Products and (b) Graph That Compares a Company’s Five Principal Products

334

10.3

A Pie Chart Comparison for a Company’s Best and Worst Products in Terms of Percent of Sales

335

(a) Amounts for a Company’s Current Year and Past Years and (b) Graph That Compares a Company’s Current Year to Past Years

339

A Pie Chart Comparison for a Company’s Best and Worst Years in Percent of Sales

340

7.4

7.5

7.6

8.4

9.4 9.5

10.4 10.5

Preface In these fast-changing times, decision makers are utilizing a wide range of information systems to improve their decisions. The prominent ones include decision support systems (DSSs), executive information systems (EISs), on-line analytical processing (OLAP) systems, and knowledge management systems (KMSs). DSSs allow decision makers to be at the center of their decisionmaking activities, while EISs provide the capability to assist decision makers at the highest level to get a better grasp of their operations. OLAP systems enable decision makers to build and work with analytical models easily and view the output in multiple dimensions where the focus is on showing them what has happened in their business. KMSs go beyond relationships found in information allowing decision makers to extract patterns, trends, and correlations that underlie the interworkings of a company currently and over time. In contrast, the latest thrust in information systems are business intelligence systems (BISs), which take decision makers to a higher level by providing them with a thorough understanding of a company’s operations. Business intelligence systems are capable of leveraging a company’s assets to optimize their value and provide a good return on investment. Even though BISs are “the new kids on the block,” their underlying concepts have been in place for at least a decade. Data warehousing, data mining, the Internet, and the World Wide Web, to name a few, have revolutionized the ability of decision makers to find, accumulate, organize, and access business intelligence. The bottom line is that effective BISs give decision makers the ability to keep their fingers on the pulse of their businesses every step of the way. The growth of business intelligence (BI) can be linked to the fact that BI software is getting better and cheaper to use on a day-to-day basis, not to mention lowering of hardware costs. Business intelligence that is developed con-

xii

Preface

stantly, renewed where necessary, and applied where needed is an important source of competitive advantage for a company’s decision makers. The more a company’s decision makers make use of business intelligence, the more they contribute to a company’s overall well-being. Companies that really succeed have a greater understanding of their operations than their competition. By using some form of internal and external computer networking, including the Internet and the World Wide Web, for buying and selling just about anything, a company can enhance the intelligence of its decision makers. For business intelligence systems to be successful, there is need to create an appropriate infrastructure to capture and create data, information, and knowledge, and store them, improve them, clarify them, and disseminate them to decision makers so that there can be a fuller understanding of a company’s operations for actionable results. This text is designed not only for company managers, but also for information systems professionals. Company managers (i.e., decision makers) will be particularly interested in installing their own systems or assisting in their installation somewhere in their companies. End users in the various functional areas of a typical company can also benefit from the text. Information systems professionals will find the text helpful in understanding one of the most important developments in systems for decision makers—BISs—and how to build them. It should be noted that the text is suitable in an academic environment—that is, an undergraduate or graduate-level course covering the fundamentals of business intelligence systems. The text is quite capable of serving the needs of most managers and information systems professionals in a typical organization as well as in academia. The text’s structure is logical for a complete treatment of business intelligence systems. The topics which are illustrated by real-world applications where appropriate, are as follows: PART I: IMPROVING DECISION-MAKING EFFECTIVENESS USING BUSINESS INTELLIGENCE SYSTEMS In Chapter 1, the emergence of BISs, which are an outgrowth of data processing and management information systems, is explored in some depth. The utilization of BI to help gain competitive advantage for a typical company is also discussed from an E-commerce perspective. The creativity that underlies effective BI is the subject of Chapter 2. There is a quiz to test the reader’s level of creativity at the chapter’s end. PART II: UNDERLYING STRUCTURE OF EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS Chapter 3 centers on effective decision making in which the thrust is on being proactive—that is, engaging in problem finding. Problem finding also includes uncovering appropriate opportunities for the organization to pursue today and

Preface

xiii

tomorrow. Chapter 4 sets forth information systems that can be upgraded to effective BISs for assisting decision makers. The employment of BIS software is also explored at some length. The types of databases needed to house aged data and real-time data with emphasis on data marts and data warehouses are explored in Chapter 5. This is complemented by the use of data mining to discover new knowledge about a company’s operations. This chapter also covers the sharing of BI through the use of its own network communications infrastructure (i.e., intranets, extranets, the Internet, and the World Wide Web) and tie-in with E-commerce. PART III: BUILDING EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS In Chapter 6, the four essential elements of an effective BIS operating mode are brought together as a basis for building an effective BIS. The tie-in with Ecommerce is stressed. The key steps in implementing successful BISs are detailed along with their continuing support. PART IV: EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS FOUND IN A COMPANY’S FUNCTIONAL AREAS In Chapter 7, strategic intelligence in corporate planning is examined as it ties in with other levels of intelligence. Important factors that are useful in understanding strategic planning at the highest management level are set forth. Corporate planning areas that lend themselves to strategic intelligence are explained along with typical applications. The remaining chapters follow a similar format. That is, Chapters 8, 9, and 10 explore significant factors that are related to tactical, operational, and financial intelligence for marketing, manufacturing, and accounting, respectively. In turn, representative applications of BISs are presented to demonstrate their use on a continuing basis. Due to the far-ranging aspects of this project, I wish to thank the many professionals who have assisted me. First, I would like to give thanks to the many vendors who have supplied materials directly or indirectly that have been included throughout the text. Second, my graduate students over the years at Xavier University, who have expressed a need for a book of this type, are to be commended for their helpful suggestions. For the most part, these students, who are employed full-time by a wide range of organizations in various industries throughout the Midwest, are computer professionals. Third, I am especially thankful to Mr. James Hoctor of the Kroger Company for his professional assistance in developing some of the text’s illustrations. Fourth, but not last, a special note of appreciation is in order to Eric Valentine, publisher of Quorum Books, for his comments on this important project, which impacts the information systems field today.

Abbreviations ABC

activity-based costing

ABM

activity-based management

AI

artificial intelligence

APS

advanced planning and scheduling

ASAP

as soon as possible

ASRS

automated storage and retrieval system

ATM

asynchronous transfer mode

BI

business intelligence

BIS

business intelligence system

BISDN

broadband integrated services digital network

BLOX

objects

B2B

business-to-business

CAD

computer-aided design

CAM

computer-aided manufacturing

CAMS

Computer-Aided Maintenance System

CAPE

computer-aided production engineering

CART

classification and regression trees

CASE

computer-aided software engineering

CBI

Collaborative Business Intelligence

CBT

computer-based training

CD

compact disk

CEO

chief executive officer

CFO

chief financial officer

xvi

Abbreviations

CGI

common gateway interface

CIM

computer integrated manufacturing

CIO

chief information officer

CKO

chief knowledge officer

COGS

cost of goods sold

COL

Center for Organizational Learning

CORBA

Common Object Request Broker Architecture

CP

corporate portal

CPA

certified public accountant

CRM

customer relationship management

CSF

critical success factor

CSRP

Customer Synchronized Resource Planning

DBMS

database management system

DM

data management

DMAPS

digital manufacturing process system

DOS

disk operating system

DP

data processing

DSS

decision support system

EAI

enterprise application integration

E-commerce

electronic commerce

EDI

electronic data interchange

EIP

enterprise information portal

EIS

executive information system

EOQ

economic ordering quantity

EP

enterprise portal

ERP

enterprise resource planning

ETL

extraction, transformation, and load

EVA

economic-value-added

FAQ

Frequently Asked Question

4GL

fourth-generation language

GB

gigabyte

GDSS

group decision support system

GUI

graphical user interface

HBO

Home Box Office

HDTV

high definition television

HOLAP

hybrid on-line analytical processing

HP

Hewlett Packard

Abbreviations HR

human resources

HTML

Hypertext Markup Language

HTTP

Hypertext Transport Protocol

ICO

inventory chain optimization

IPS

idea processing system

IS

information system

ISDN

integrated services digital network

ISP

internet service provider

IT

information technology

JAD

joint application development

JIT

just-in-time

KB

kilobytes

KISS

keep it simple, stupid

KMS

knowledge management system

KPI

key performance indicator

L&D

logistics and distribution

LAN

local area network

LP

linear programming

MAN

metropolitan area network

MB

megabytes

MDDB

multidimensional database

MES

manufacturing execution system

MIS

management information system

MOLAP

multidimensional on-line analytical processing

MPP

massive parallel processing

MRP

material requirements planning

MRP-II

manufacturing resource planning

NAS

network attached storage

NCA

network computing architecture

NEON

New Era of Networks Inc.

ODIN

Operations and Development Information Network

OLAP

on-line analytical processing

OLKM

on-line knowledge management

OLTP

on-line transactional processing

OO

object oriented

OODBMS

object-oriented database management system

OOP

object-oriented programming

xvii

xviii

Abbreviations

Pawws

Portfolio Accounting World-Wide Service

PC

personal computer or microcomputer

PIMS

Profit Impact of Marketing Strategies

PMI

plus, minus, and interesting

PPI

purchase performance index

PQL

Pattern Query Language

R&D

research and development

RAD

rapid application development

RAID

redundant array of independent/inexpensive disks

RAS

reliable, available, and scalable

RDBMS

relational database management system

ROI

return on investment

ROK

return on knowledge

ROLAP

relational on-line analytical processing

SAM

Strategic Analysis Model

SAN

storage area network

SAS

Statistical Analysis System

SCM

supply chain management

SEM

Strategic Enterprise Management

SMP

symmetric multiprocessing

SQL

structured query language

TCP/IP

transmission control protocol/Internet protocol

3-D

three-dimensional

TQM

total quality management

VAN

value added network

VLAN

virtual local area network

VLDB

very large database

VR

virtual reality

VRML

Virtual Reality Modeling Language

WAN

wide area network

WMS

wisdom management system

XML

eXtensible Markup Language

PART I Improving DecisionMaking Effectiveness Using Business Intelligence Systems

1 Introduction to Effective Business Intelligence Systems FAST-CHANGING TIMES DEMAND MORE INNOVATIVE APPROACHES TO BUSINESS SYSTEMS Within the ever-changing work environment, the need for newer business systems has become more evident than ever before. Currently, these newer systems are manifested as business intelligence systems (BISs). These systems, not technical expertise, are the important means to growing a business organization. Business intelligence systems require vision, money, and patience in their development and implementation. Essentially, BISs center on a full understanding of information and knowledge that is derived from data. The current increase in available data is useless without an effective way to access and synthesize vast amounts of information and knowledge. To get a handle on the tie-in of data to past system approaches, reference can be made to recent developments. Data mining tools allow organizations to capture all the fundamental particles about customers, suppliers, internal transactions, and so on that make up the real picture of a business’s life. Also, data warehouse applications are used to catalog, index, and cross-reference these raw materials. For both approaches, the focus is on dealing with raw data—billions of facts that, individually or collectively, have little intrinsic significance. What are needed today and in the future are new business intelligence tools that enable decision makers to organize, analyze, and communicate about corporate data. Related to these tools are the dynamic Web sites for business-to-business electronic commerce, or Ecommerce. These BI tools not only show trends based on historical data but also provide the capability to project different futures based upon changed inputs and provide for a thorough understanding of results. In essence, there is need for the alchemy that transforms basic business data into actionable business

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intelligence. Data becomes business intelligence when it is in the hands of decision makers who know what to do with it. It should be noted that there are a number of information systems that are related directly to business intelligence systems. They include: (1) knowledge management systems, (2) on-line analytical processing systems, (3) decision support systems, and (4) executive information systems. For the most part, these systems are helpful in making comparisons, analyzing trends and patterns in business, and presenting historical and current information to decision makers. Essentially, these systems assist decision makers in making better informed decisions that affect all aspects of a company’s operations. In contrast, business intelligence systems go a step further by providing decision makers with a thorough understanding of their operations today and tomorrow. In a few words, business intelligence systems give decision makers the ability to keep their fingers on the pulse of their businesses every step of the way. Concerning the need for actionable business intelligence, management expert Peter Drucker saw this a long time ago. Because most information systems have been and still are, to a large degree, built on accounting data, they tend to be inwardly focused. If led by these systems, decision makers are consumed with what they already know a lot about—costs. In contrast, decision makers need to focus on what they find more difficult: the creation of value and wealth. Drucker’s related belief is that newer system approaches must provide decision makers with more pertinent external data. There are certainly technology-based efforts to capture market and customer data and to make sense out of it. But the information that is created does not seem to be helping decision makers make the really tough and important decisions. What is needed are real business intelligence systems that are capable of providing decision makers with much more than queries, report generation, and warehousing. Rather, business intelligence systems are the culmination of major system technologies that provide the ability to take a proactive stance rather than a passive or, at best, a reactive approach to a company’s operations. As will be seen throughout this text, a BIS operating mode centers on the ability to learn and understand what is necessary to keep the organization alive and well today and tomorrow. This means dealing with new and, many times, difficult situations along with the skilled use of reasoning. The net result is a better understanding of data, information, and knowledge to manage a typical organization more effectively. Needless to say, the successful accomplishment of managing a growing enterprise requires a very creative approach in today’s global economy. In this chapter, a background is given on the essentials of business intelligence systems that will be examined in more detail in Chapters 4 and 5. There is a tie-in of business intelligence systems with past and current management information systems. Additionally, there is a discussion of what lies beyond business intelligence systems.

Introduction to Effective Business Intelligence Systems

5

Change Is the Only Constant as Organizations Embrace E-Commerce Today, change seems to be the order of the day. Not only is computer technology changing more rapidly each day but so are business requirements. Business managers are being pressed to respond to customer needs and competitive threats in days and weeks instead of months or years. Products and projects that could linger for six to 12 months just four years ago now need to get out the door in three months. And it is not just multinationals or global corporations that are being faced with shortening time frames. Almost any company, from a small office supply store up to the world’s largest corporation, is at risk of being “Amazoned” by a more nimble, E-business-enabled competitor. From this changing perspective, the success of companies will be measured by how well they have leveraged E-business applications to differentiate themselves from the competition. To do so, companies of all sizes will have to alter the way they define “customer,” how they serve the customer, and the product and value they deliver. E-business, or E-commerce, is not just for established companies, such as IBM or Charles Schwab. Every business should be thinking about how to integrate its existing and future resources and production cycles to respond to the time-to-market constraints and competitive nature of the Ecommerce world. Traditional corporate resources of marketing, manufacturing, finance, and human resources as well as corporate infrastructure can no longer be isolated from the production cycles (marketing through support), as every aspect needs to be integrated. With the increased pace of business change, companies are under tremendous pressure to integrate formerly disparate systems and applications to meet business needs. But the complexity of implementing integration solutions can be daunting because these business drivers mandate the data flow across legacy systems, packaged applications, custom applications, diverse technical platforms, and multiple databases with synonyms, homonyms, and different data representations. The combination of great need, an acute lack of skilled computer professionals, and short implementation times makes all enterprise application integration solutions that abstract complexity and increase adaptability highly attractive. To assist in integrating business solutions for decision makers, a recommended approach is to develop a business intelligence framework. Reference can be made to Figure 1.1. Although the architectural components and tools to integrate data, information, and knowledge with business intelligence are still evolving, the basic underlying structure for a shift toward even more Ecommerce is currently in place. As shown in Figure 1.1(a), corporate planning (refer to Chapter 7) oversees the major functional areas of a typical company (i.e., marketing) (Chapter 8), manufacturing (Chapter 9), and finance (Chapter 10) within an effective BIS framework. Business intelligence software (b) is

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Improving Decision-Making Effectiveness

Figure 1.1 An Effective Business Intelligence System Framework for a Typical Company

used to process aged and real-time data within some type of computer networking environment. The end result (c) is some type of analysis, understanding, discovery, rethinking, and new ideas for the area under study. Although not shown per se in Figure 1.1, the underlying structure for the typical company is E-commerce. Essentially, companies are becoming more connected by the Internet and the World Wide Web, while customers, suppliers, and business partners are directly connected by intranets and extranets. Because E-commerce is a very important part of an effective BIS framework, E-commerce centers on applying computer technology to create new channels of distribution and communication with business partners and suppliers, to establish new contact points for delivering products and services to customers, and to support and interact with those customers. Complementary to Ecommerce is business intelligence which involves collecting, managing, mining, and analyzing the data generated by an enterprise, resulting in information and knowledge with strategic value to those who possess it. Increasingly, companies are coming up with creative ways to link those efforts, such as found in Ecommerce. For example, an effective business-to-business electronic exchange is the linkage of Daimler-Chrysler, Ford, and General Motors, which has resulted in an auto industry mega-marketplace for all involved in the selling and buying of automobiles. At the core of this huge electronic auto exchange is one fact:

Introduction to Effective Business Intelligence Systems

7

unprecedented access to information and knowledge for consumers and businesses. Knowing the dealer’s invoice price when buying a car is one of the bestknown examples, but the car may also have been delivered faster to the dealership because these three car manufacturers shared factory production data on line with their transportation partners. Applying a Business Intelligence Approach to an Organization’s Opportunities and Problems Fueling the interest in business intelligence systems is going beyond the traditional approach in finding out what went wrong. In the past, a management information system told the decision maker what had happened, but not why and what should be done. Today, the focus is on finding out why and what can be done so that there is a thorough understanding of the proper direction to take. While “what had happened” provides only the decision maker with an opportunity to solve today’s superficial problems, the “why and what should be done” helps the decision maker to get at the root of the problems in order to prevent future occurrences. To place business intelligence systems in their proper perspective, consider an investment bank. In the world of investment banking, there are many sources of power, such as a large amount of available investment capital or the ability to be all things to all people. Another important source of power is understanding its operations better than its competition. By focusing on its substantial resources in a number of rapidly changing industry sectors, like computer technology, health care, financial services, and transportation, the investment bank strives to maintain an in-depth industry intelligence that gives it a competitive advantage. Probably the most important source of power for a bank is the ability to keep its finger on the pulse of its customers every step of the way by understanding thoroughly their needs and wants. From this latter perspective, this superior intelligence is helpful in understanding its customers’ needs and in providing creative ideas and effective solutions to its current and future opportunities and problems. RELATIONSHIP AMONG DATA, INFORMATION, KNOWLEDGE, AND INTELLIGENCE The relationship of data, information, knowledge, and intelligence, which is found in Figure 1.2, shows important factors about each of these levels. At the beginning level, data represents the unstructured facts and figures that have the least impact for the typical manager. It is the “data soup” of information processing at the lowest level. Due to the proliferation of data it is sometimes referred to as “data glut.” Many decry the data glut as distracting, confusing, and not helpful to the typical manager. Generally, the ability to prioritize and rank data decreases in direct relationship to the amount of data being used. While there

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Improving Decision-Making Effectiveness

Figure 1.2 Relationship of Intelligence to Various Levels of Summarization

is still some work to be done at the data level, the typical manager has moved well beyond data tabulation to the next levels. At this next level, information is structured data that is useful to the manager in analyzing and resolving critical problems. In the past, a typical company is said to have had five major resources: men (i.e., people), machines, money, materials, and management (the five Ms). More recently, information has been added as the sixth resource. However, within the context of information technology, many companies regard such technology as an overhead expense, not as a valued asset. It is time that these misconceptions be turned around and that organizations can actually lower their costs, increase profits, or enhance their market image through the latest information technology (IT) advancements. For the most part, it is recognized that quality and timely business information is a manager’s important resource. It is an important business asset that has been generally undervalued, underestimated, and underused. A major problem facing a typical manager is the volume of information crossing his or her desk. It can be so voluminous as to be almost unmanageable; yet good planning and control over operations via effective decisions must be based on a steady flow of goodquality, up-to-date information. Given these conditions and the accelerating pace of business changes, there arises a definite need to change working habits. A human-computer dialogue should not impede the manager’s thought processes, but rather it should augment his or her capabilities and become an extension of his or her mind. From this view, the computer is an important means of providing essential information to the manager. It should be noted that the wealth of information available today can, at times, impede a manager’s decisionmaking capabilities rather than simplify them. From this broadened perspective, there may be the need to move on to the next level.

Introduction to Effective Business Intelligence Systems

9

At the next higher level, there is knowledge, which is obtained from experts based upon actual experience. As such, there is a need to integrate a range of information in order to see patterns and trends that enable a manager to make the transition to insight and prediction. Essentially, this is the function of broadbased knowledge management systems that go beyond expert systems as found in the past. For an example of this type system, reference can be made to a consulting firm or a CPA firm where knowledge can be gathered and disseminated to its clients. From a historical perspective, Francis Bacon in 1620 published in Latin Novum Organum or “new tool of reasoning.” He proposed a new paradigm for what was thought of as “knowledge” and gave the world the starting point for what would become empiricism and modern science. Bacon’s paradigm shift established knowledge as a cumulative process of discovery, propelled by processing data about the external world through the reasoning powers of the human brain. Bacon said that knowledge is rooted in an increasing grasp of external reality. Going further, he analyzed the psychological barriers to knowledge that went far beyond the cold fallacies of Aristotelian logic—an analysis that appears today, attributed in nearly every paper on the barriers to implementing knowledge management in an organization. Today, information basically becomes knowledge in the hands of an expert. A body of information organized into a coherent framework forms the basis for the creation of knowledge. For example, an annual report forms the body of knowledge for financial accounting. Typically, knowledge about a company’s financial position is impossible without a framework to organize ratios derived from accounting data and information in the balance sheet, income statement, and cash-flow statement. Knowledge comes from a comprehension of the underlying structure of financial information. It requires expertise to interpret financial results in a creative way. Overall, while information is data about the data, knowledge is basically information about the information. Shifting Paradigm from Knowledge to Intelligence In a typical organization, there is valuable information and knowledge out there just waiting to be tapped about what customers like about a company’s goods and services, what non-buyers do not like, and how customers view a company’s competitors. Today, this goes beyond just information and knowledge. This represents a shifting paradigm to intelligence (i.e., a comprehensive understanding of a company’s customers and its own operations). Basically, business intelligence examines the distilled essence of customers’ and employees’ personal experiences and needs as well as a company’s operations that are interrelated with external sources. Such smarts can benefit an organization vastly more than just information and knowledge. In the past, this type of business intelligence was rarely shared, even among peers. Also, typically such insights to understanding important relationships never reached top executives. However, more companies today are getting involved in the full utilization of business

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intelligence in terms of their everyday operations. Business intelligence has come a long way—from servicing a limited number of decision makers in a company to supplying hundreds of thousands of customers, employees, and partners throughout an enterprise and from systems that draw on most internal databases and data warehouses to those that access external data sources as well. Broad-Based View of Intelligence—Understanding a Company’s Total Operations From this enlightened perspective, the ability to understand the interrelationships of presented facts—whether they involve data, information, and/or knowledge—in such a way to guide action toward one or more desired goals is called intelligence. At the intelligence level (per Figure 1.2), there are a lot of interactions and interrelationships among the levels. These interactions and interrelationships can be complex, protracted, and creative. Typically, these complexprotracted-creative dimensions are items that require human interactions and that need to tie in with the capabilities of computers. Essentially, intelligence centers on insight and understanding of the area or problem under study. It should provide the decision maker with the capability of meeting most situations, whether unstructured or semi-structured. As with knowledge, the focus of intelligence is on the higher levels that are strategic to the success of an organization. In contrast, the focus of the data and information levels is typically tactical and/or operational and tied in with financial intelligence. For example, salespersons’ call reports are the “data soup” of sales management (i.e., the operational level). In turn, a monthly summary of these salespersons’ call reports is information to the various sales managers (i.e., the tactical level). Knowledge about these reports over time (this year versus the last two years) provides the highest sales management level with an overview of what has occurred in the past (i.e., the strategic level). Insightful analyses can help the vice president of marketing to determine what sales areas need to be evaluated for more, fewer, or the same number of salespersons in the coming year. Hence, there is need for marketing business intelligence at the highest level to give clear direction to a company’s future. This is particularly important in today’s environment where salespeople face more and better competition, shorter product life cycles, and more sophisticated and demanding buyers. Essentially, a broad-based view of intelligence centers on developing and examining appropriate interactions and interrelationships as they affect a company’s customers and its own operations on a periodic basic, including a daily basis. It centers on detecting important trends and patterns that have relevance to the organization for a better understanding of a company’s total operations. In some cases, this means identifying important milestones that affect the direction that an organization should take today and tomorrow. From this broad

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perspective, intelligence centers not only on identifying important changes that are related to important opportunities facing a typical organization but also helping the company’s decision makers identify important problems (i.e., trouble spots) that need to be addressed immediately for solution. As will be seen in Chapter 3 and subsequent chapters, this takes the direction of problem finding (forward looking) as opposed only to problem solving (backward looking). Beyond Intelligence—Wisdom and Truth At the second highest level in Figure 1.2 is wisdom, which is the ability to judge soundly. This high level of understanding involves such philosophical attributes as the awareness that the models constructed will not always hold true. Hence, a beginning point for wisdom can be a humble assertion: “I don’t know.” This assertion can be the real beginning of wisdom. Because business transactions per se do not inspire much wisdom for decision makers, wisdom grows from making the connections of these transactions to one another and their change over time. Wisdom requires and intuitive ability, born of experience, to look beyond the apparent situation in order to recognize exceptional factors and anticipate unusual outcomes. Largely untapped today, wisdom is a vital organizational resource, accumulated through experience and applied to everyday learning at work. Essentially, wisdom is a personal capacity acquired through experience and thinking. As such, there is a tendency to replace past hierarchical and functional roles with learning relationships that focus on wisdom as the foundation of the new organization. Wisdom can be used as an organizational strategy to develop human potential in organizations. Typically, a wise manager knows what knowledge and intelligence is needed in a given situation and how to renew both by working with others to solve a problem or engage in problem finding. Truth, the highest level in Figure 1.2, is conformance to fact or reality and represents the lofty pinnacle of understanding. It comes from understanding the way that points of wisdom come together. Although its place in the typical organization is being debated at this time, it is safe to say that certain truths centering on ethical and environmental issues are always useful to the typical manager for guiding a company’s direction at all times. A violation of basic truths held by the general public can only jeopardize a company’s standing in the community. Going beyond the truth found in the business community is that of the religious community. To the ultimate degree, truth is equivalent to God. Basically, an analysis of Figure 1.2 emphasizes that, generally, decisions with the most significant ramifications for the operations of a company tend to have characteristics that are most suited to a participatory decision-making process. The decisions that are best made by an individual manager do not appear, in general, to have as wide-ranging or overall an organizational impact. Hence, the implication of this figure is that group decision making is the way to go for

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quality decision making in many business situations. As the environment becomes more complex, the typical decision maker will have to call upon more resources if effective decision making is to prevail. Studies have shown a relationship between characteristics of decisions and the nature of the decisionmaking process. Decisions were found to be more participatory when decision quality and user acceptance was important. Participation was also preferable when the problem was complex or unstructured. Studies have shown that decisions were found to be less participatory when the manager had all the necessary facts, the problem was routine or structured, or time was limited and immediate action was required. Although the foregoing exposition centered on the relationship of intelligence to its other levels (Figure 1.2) for business decision makers, it should be noted that the Antichrist can make great use of these levels to fulfill his needs. That is, the Antichrist and his followers can employ appropriate computerized systems to produce the desired level required by the current situation. As will be seen later in the chapter, virtual reality systems and wisdom management systems can be utilized to have the world believing that he is the Messiah. Of course, a variation of business intelligence systems can be used, such that the Antichrist will give the outward appearance of being able to predict the future accurately. The Antichrist who is alive today is expected to be a European who will lead the Economic Union of Europe (the last of the Roman Empires) in the very near future. Need for a Chief Officer to Oversee a Company’s Knowledge and Intelligence To assist in building and maintaining a learning organization infrastructure for knowledge and intelligence in a typical company, it is necessary for a chief executive officer (CEO) to place knowledge and intelligence at every worker’s fingertips. After all, the CEO is the person who is at the pinnacle looking over everything, providing the vision and building on a network of internal and external relationships that requires computer technology to sustain it. Due to his or her time limitations, the job is relegated to another person who has the time and expertise to carry out the duties for a learning organization. Typically, corporate titles, such as chief learning officer, chief intelligence officer, chief knowledge officer, and even chief transformation officer are given to the person who oversees a company’s business knowledge and intelligence. Among the corporate giants that have named such officers are Coca-Cola, Monsanto, and IBM. The premise behind the boom in harnessing business knowledge and intelligence is this simple fact: employees possess a wealth of knowledge and intelligence that is backed up by experience about their company, from its products, customers, and competitors to its production processes and internal technology. But much of that is held in bits and pieces by various individuals or sections

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of a company. If these bits and pieces can be gathered and distributed throughout the entire company, this shared knowledge and intelligence can be a powerful force. Workers have the capability to create competitive advantages and thereby increase revenue. A company, for example, might discover that a process used in one section can have applications in another. Or a company representative using all of the company’s knowledge and intelligence about its customers could make a superior presentation to the client, helping to secure a contract. The chief officer overseeing a company’s knowledge and intelligence must take into account that change is the only constant as organizations embrace Ecommerce to the fullest extent possible (refer to earlier discussion in the chapter). In the future, the individual’s success will be measured by whether ideas derived from knowledge and intelligence of E-commerce are bubbling up when the person is not around and whether organizational personnel can turn to this individual to assist in making changes to accommodate expanding E-commerce. In addition, a company’s knowledge and intelligence will allow this chief officer to assist decision makers in the various functional areas of a typical company. That is, decision makers will need to know what their customers want even before the customers realize it. UTILIZING BUSINESS INTELLIGENCE TO IMPROVE CORPORATE PERFORMANCE As will be demonstrated in this text, business intelligence can improve corporate performance. Companies have the potential to create exciting customer and supplier relationships, improve the profitability of products and services, and better manage risk, among many other gains. Through business intelligence applications such as target marketing, customer profiling, and product or service usage analysis, businesses can finally begin understanding patterns and trends. Having the right intelligence means having answers to such key marketing questions: • Which of the company’s marketing campaigns have been most successful? • Which of a company’s customers provide minimal profit or cost the company money? • Which products and services can be cross-sold most effectively and to whom? • Which of the company’s sales channels are most effective for which products? • Which of a company’s customers are most profitable and how can relationships with them be expanded? • How can the caliber of a company’s customers’ overall experience be improved?

Having answers to these questions signifies that a company is not smarter, but rather is better informed through an intensive understanding of its operations. Although most companies have the raw data to answer these questions, today’s operational systems generate vast quantities of product, customer and mar-

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ket data from point-of-sale, reservations, customer service, and technical support systems. The major challenge is to extract business intelligence from them in order to reap their full potential. Many companies take advantage of only a small fraction of their data for strategic analysis. The remaining untapped data, often combined with data from external sources, such as government reports, trade associations, analysts, the Internet, and purchased information, is a gold mine waiting to be explored, refined, and shaped into vital corporate intelligence. This intelligence can be applied in a number of ways, ranging from charting overall corporate strategy to communicating personally with customers, vendors, suppliers, and employees through call centers, kiosks, the Internet, and other touch points that facilitate one-to-one marketing on an unprecedented scale. Other business intelligence areas that are useful to improve corporate performance are given below. Using a Learning Organization to Leverage a Company’s Business Intelligence Currently, many experts state that failing to make use of business intelligence can have harmful long-term effects on corporate health. The basis of a new and often revolutionary model for organizational growth and survival is one that seeks to gain competitive advantage from intellectual capital the way earlier models drew profit from investment capital, and later from information technology. Whatever it is called, the learning organization promises to sweep away structures and assumptions long ingrained within the top-down, bottom line– driven organizations of the 20th century. This newer direction of the learning organization for gaining competitive advantage will be a major driving force well into the 21st century. Knowing what the company does well, learning from that to do it better the next time, and continually looking for improvement are the hallmarks of a learning organization. Although there is disagreement among the experts concerning its various aspects, most view a learning organization as one that develops over time and is linked with business intelligence. In addition, its performance improves over time, which can be linked to improved financial performance. A learning organization is skilled at acquiring and utilizing intelligence and, at the same time, at modifying its operations to reflect new patterns and insights. Peter Senge, who popularized learning organizations in his book, The Fifth Discipline, described them as places “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together.”1 Typically, leveraging intellectual capital or assets or providing learning on demand is an important trait of a learning organization. Treating the organization as a brain—a repository and processor of assorted know-how, information and knowledge rather than a machine—deepens a respect for both the individual

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and the team as sources of a company’s innovation. All this, of course, is part and parcel of a learning and intelligent company, and the stakes are larger than just meeting budgets and timetables. Today, a typical company uses approximately 20 percent of its intellectual capital. If, through a learning organization approach, a company can raise that to 30 percent, that represents a 50 percent gain, in some of the company’s assets, say the experts. As an example, the Corporate Strategy & Alliance Group of Digital Equipment uses a team learning process, called “deep diving” that leverages knowledge and intelligence of Digital employees, knowledge that they have had for years but never truly shared. In effect, the power of the organization is now being more effectively realized than in the past. Another example is the Polaroid Corporation, where teams are asked to pretend to be working for competing firms in order to reveal Polaroid’s own potential weaknesses. Outside dealers, customers, and industry friends were also invited into some of the exercises. People became so involved in this process that they were asked to enact what they knew and felt. In other words, they were told not to give suggestions, say in the way that the chief executive officer would. Needless to say, this process demands openness. If people are brought together to share what they know and describe what should change, they have to feel comfortable throughout the process.2 A true learning organization is one that can create its own future by proactively seeking out and mastering change. Leaders of learning organizations are adept at communicating a shared vision and helping others gain accurate views or reality. Well-known organizations, such as AT&T, Intel, EDS, Herman Miller, Ford Motor Company, National Semiconductor, and Merck & Company, are exploring learning organization practices as members of MIT’s Center for Organizational Learning (COL) in Cambridge, Massachusetts. While a learning organization advocates leave no doubt as to the importance of the free expression of ideas, organization experts note that the exchange must be tempered for maximum efficiency. The value of intelligence is in its use and not in its collection per se. That is, it is important not to collect and save everything but to relate what is known and not known to what needs to be known to fulfill organization objectives and goals. In this manner, business intelligence can be leveraged by a learning organization for best results. Gaining and Improving Competitive Advantage Business intelligence systems, as will be seen throughout the text, are very useful to decision makers to gain competitive advantage. Business intelligence that is constantly renewed and enhanced can be looked upon as an important source of gaining and improving competitive advantage. Similarly, E-commerce is changing the parameters of competition in every industry, manufacturing or service oriented. Formerly, computer technology was directed toward being a “storekeeper” of data and information. In today’s fast-changing world, business

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intelligence derived from data, information, and knowledge has the capability to facilitate change through a better understanding of the times so that the organization can remain competitive. The importance of business intelligence technology, including E-commerce, as a competitive weapon furthering the organization’s objectives, goals, and strategies is no less applicable to the smallto medium-size company as it is to the large-size company. Currently, many companies do not view business intelligence or computer technology investments as creating a competitive advantage. In any industry, competitors have access to the same technology and can make investments at the same relative level. The difference in performance lies in the way the technology is used to enable and support competitive advantage. Additionally, many companies believe that the period of cost advantage as the basis for competitive advantage no longer applies. Companies now must look at core competencies and use technologies to get a hold of their business intelligence assets and make use of them. For example, because Wal-Mart can squeeze many dollars out of its distribution costs, it can sell products at a lower price than Sears. In a similar manner, if the Japanese can make their manufacturing even more efficient, they can get the edge on the Americans or Europeans. On the other hand, if the Americans and Europeans can out-innovate the Japanese and add some interesting and needed new features, consumers around the world will buy American and European. The bottom line is that a typical company needs to capture and deploy its resources effectively to gain and improve competitive advantage. Employing Intellectual Capital Gives a Company a Competitive Advantage Employees’ brainpower has always been an important asset of a typical company. It has never been as important, however, as it is today. Every company depends increasingly on management skills, all types of newer technologies, patents, processes, intelligence about customers and suppliers, and everyday experiences. Added together, this leads to a company’s intellectual capital. Even Pope John Paul II recognized this in his recent encyclical, writing of a new, important form of ownership: “the possession of know-how, technology, and skill.” Hugh MacDonald, futurologist for ICL (the large British computer manufacturer), calls intellectual capital that which is useful to create differential advantage. In other words, it is the total of everything everybody in a company knows that gives a company a competitive advantage in the marketplace. Typically, intellectual capital is a most viable and valued asset that is associated with the leading edge of science. To understand the importance of intellectual capital, reference can be made to the Polaroid Corporation, Pioneer Hi-Bred International, and American Express. Helios, a new medical imaging system from Polaroid, will reach the market after just three years in development. That is twice as fast as predicted. The basic reason was the interdisciplinary teamwork in the laboratories. The company’s researchers are not any

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smarter, but by working together, they get the value of each other’s intelligence almost instantaneously. As a second example, Pioneer Hi-Bred International scientists breed special strains of corn for disease resistance, high yield, or specific attributes like oil content. A decade ago, such work ate up hundreds of acres of farmland and consumed untold numbers of man-hours. These days they can do it by manipulating the plant’s DNA directly, using a petri dish. Apart from the cost savings, the company expects to reduce by two years the seven- to 10-year time it takes to develop a new hybrid. Moreover, Pioneer can now focus on individual customers, breeding strains rich in cornstarch for industrial users or rich in specific oils for food processors. The third example is IDS Financial Services, the financial planning subsidiary of American Express. IDS codified the expertise of its best account managers in a software program called Insight. Now even the worst of its 6,500 planners is better than the average planner used to be. One result is that in four years the percentage of clients who leave has decreased by more than one half. Basically, these three companies have learned to exploit their intellectual capital. In Pioneer Hi-Bred’s case, brainpower is replacing land—the elemental form of wealth. IDS has turned the talent of a few employees into an asset available to all its planners. Polaroid is managing its knowledge to shorten development time significantly.3 Understanding a Company’s Critical Success Factors (CSFs) More Thoroughly In order to expand the manager’s view of how problems can be solved and how new opportunities can be identified for implementation, problem finding is highlighted in this text, especially in Chapter 3. In the process of making a wide range of decisions, it is necessary for the manager to take into account the organization’s critical success factors (CSFs)—that is, those factors that are critical in making or breaking the organization. The process to identify CSFs was originally defined by John Rockart of MIT in the late 1970s. (A rich discussion of these CSFs will be given in Chapter 7.) At this point, it is sufficient to say that there are a specific and limited number of areas in which satisfactory results will dramatically affect the competitive performance of an organization. Typically, these areas are the ones to be measured and evaluated. As the old saying goes, “If you can’t measure, you can’t manage.” Similarly, “What you measure wrong, you manage wrong.” Establishing the wrong measures will lead to far worse results than establishing no measures at all. Depending on the strategic direction of an industry and the specific organization, there are different ways of managing change and reshaping a business. The CSF process identifies what these specific needs are. In turn, various types of information processing systems can be used to monitor these CSFs. Within the context of business intelligence systems, CSFs can be evaluated more thor-

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oughly over time so that their analysis leads to new and different ways to exploring opportunities for a company. This enlightened approach can result in leveraging a company’s CSFs for its betterment. Thus, by using a CSF process, business intelligence systems can provide access that allows company managers and their staffs to direct their present and future operations in a more effective manner. Improving Managerial and Non-Managerial Productivity Today, it is recognized that a combination of intellectual capital, technology of all types, including E-commerce, and evolving know-how are responsible for improving productivity in the United States. The icons of America’s earlier might (i.e., steel mills, petrochemical complexes, automobile assembly plants, and the like) are being replaced with “economic value” that is best symbolized by complex integrated circuits, computer software, new ideas, new insights, and the like. Most of what is currently perceived as value and wealth is intellectual and impalpable. Essentially, all types of information technologies have and are continuing to change the essential nature and process of business. The availability of real-time information has reduced the degree of uncertainty confronting business managers. This has enabled companies to remove unnecessary inventories and dispense with much programmed worker and capital redundancies. In light of the shift in today’s business operations, this also includes a move to focus more on managerial productivity versus improving productivity only on the factory floor. Generally, in the past, attention has focused on assemblyline robots or the implementation of the paperless office, whereby the productivity of individual workers at the lower levels of an organization is increased. However, when viewed from a strictly financial perspective, the productivity of labor and clerical workers is only one element in achieving organization productivity. The financial community measures productivity in terms of return on stockholders’ equity, return on capital, and other financial tests. If the management of an organization decides to launch a new product that the customer will not buy, it is irrelevant whether the workers actually assembling the product are performing their jobs efficiently. Having the right product at the right time, however, has a much greater impact on organizational productivity than gaining an incremental improvement in labor or clerical productivity. Because a manager makes decisions and not products per se, his or her productivity is measured by the quality and timeliness of those decisions. Accepting the fact that management decisions are generally more important to organizational productivity than the automation of lower level work leads to the conclusion that managerial productivity is worthy of a great deal of time, attention, and money. As will be seen later in the chapter, information systems focused initially on data and then on information to support decision making. When data and information are broadened over time, it becomes useful knowledge to sup-

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port managerial productivity at all levels. In turn, data, information, and knowledge, along with the resulting intelligence derived from business intelligence systems, are extremely useful for improving managerial and non-managerial productivity in the short run to the long run. ESSENTIAL ELEMENTS OF EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS An underlying framework for effective business intelligence systems begins with taking a creative approach to a company’s operations. An extensive discussion of creativity is given in Chapter 2. Creative computer software can greatly help decision makers get a grip on a company’s problems and the development of appropriate strategies and opportunities for the company to pursue. (Further discussion of this area will be left to that part of the text.) Related to creativity is the whole area of decision making that centers on solving a company’s problems. But more importantly, decision making is related to the use of problem finding and its related techniques (i.e., determining present and future problems along with identifying future opportunities that come from uncovering problems). In Chapter 3, these problems are identified and specific problemfinding techniques are set forth. In turn, the two approaches to the problemfinding process (i.e., the problem-centered approach and the opportunitycentered approach) are given. These two approaches tie in with the traditional problem-solving process for solving structured, semi-structured, and unstructured problems. Planning and Preparing for a BIS Operating Mode Before presenting the four essential elements that underlie a BIS environment, it should be noted that proper planning and preparation are necessary before a business intelligence system can become fully operational. Although there is the need to create and populate a data mart(s), a data warehouse(s), and an operational data system of aged and real-time data, typically the data is of uncertain quality and is derived from resources not architected with business intelligence as a priority. The traditional data source is the operational database, although documents from the marketing, sales, customer service, and advertising departments can be included. Data about competitors in terms of their products, prices, competitive positioning, and availability should be included. Marketing information that can be purchased from third parties can be helpful in creating and populating the appropriate databases. Once the database(s) has(have) been created and populated, the initial phase of the process centers on the preprocessing of data and information—that is, the selection of the appropriate data and information along with its necessary cleaning. In the next phase, there is the processing of data and information to produce knowledge. Appropriate tools (i.e., knowledge discovery or data mining tools)

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are used to discover business intelligence that assists decision makers in gaining an understanding of the area under study. A wide range of other software is typically employed, which includes business intelligence software, knowledge extraction tools, knowledge management software, knowledge management intranet search engines, on-line analytical processing software, and statistical analysis tools. For example, KnowledgeSEEKER, a knowledge discovery tool, enables users to analyze knowledge quickly and understand the patterns and important relationships. It is also useful as an accurate predictive tool. The bottom line is that KnowledgeSEEKER allows decision makers to look at all of the pertinent facts and tells them what are the most significant trends, patterns, and relationships not understood previously. More details about business intelligence tools and knowledge discovery or data mining tools will be found in Chapter 4. In the final phase, there is the interpretation and dissemination of business intelligence to the proper parties. Based on the foregoing elements, a business intelligence system makes great use of the organization, codification, and dissemination of appropriate information, knowledge, and intelligence in an organization. As such, it represents a collaborative work environment in which all three are collected, structured, and made acessible organization-wide to facilitate better and faster decision making. Basically, a business intelligence system is designed to capture appropriate information and knowledge and produce business intelligence in order to better understand a company’s operations. Because a long time frame is taken into consideration, it provides a broader view of the organization than was possible with past systems. The end result is that there is a focus on a total understanding of a company’s operations so as to improve the effectiveness of a company’s decision makers over time. Four Essential Elements in Developing and Implementing Business Intelligence Systems The four essential elements in developing and implementing business intelligence systems center on shifting through data, information, and knowledge in order to find trends, patterns, and relationships that can lead to a better understanding for more focused business solutions. In other words, the focus is on understanding a company’s operations in a manner that was not possible before. To accomplish this desired modus operandi, the essential elements found in these types systems consist of: (1) employing current information systems as a way to upgrade to business intelligence systems; (2) utilizing knowledge discovery or data mining and business intelligence methods and software to better understand a company’s total operations today and in the future; (3) building effective data warehouses and real-time computing systems where the focus is on a multitude of factors that relate to the Internet, intranets, and extranets; and (4) making the greatest use of computer networking that is related to E-commerce as a

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way of doing business with a company’s customers and suppliers. All of these essential elements are noted below briefly and are covered in depth in Chapters 4 and 5. In terms of the first element, current information systems that are capable of being upgraded to a business intelligence operating mode include decision support systems, executive information systems, on-line analytical processing systems, and knowledge management systems. Essentially, these systems enable decision makers to access data, information, and knowledge in ways they never could before to obtain a better understanding of a company’s operations. This simple fact is the main thrust behind the growing popularity of business intelligence. A mix of these systems (first element) with the other three elements below provide the typical decision maker with insights and understanding necessary to meet global competition today as well as tomorrow. Information systems relating to business intelligence systems are discussed later in the chapter as well as in Chapter 4. The second element of business intelligence systems is the utilization of appropriate software to collect data, information, and knowledge; to develop the intelligence that is needed; and to share the results with other people. This subject matter is covered in some depth in Chapter 4. It is sufficient to say that there are a wide range of software packages that meet most decision makers’ needs within a BIS operating mode. The attendant circumstances will dictate which software package is best suited to meet a decision maker’s needs. The third element centers on building the appropriate data, information, and knowledge infrastructure that are related to data marts, data warehouses, and operational databases. Typically, before the appropriate business intelligence system can function effectively using the selected software, it is necessary to have a massive amount of aged and/or real-time data, information, and knowledge to solve the present problem(s) or future problem(s) under study. The same can be said for current and future opportunities. A detailed discussion on building effective data marts, data warehouses, and operational databases that relate to the Internet and E-commerce within a BIS environment is found in Chapter 5. The fourth element—the use of computer networking that ties in with a company’s intranets, extranets, the Internet, and the World Wide Web as well as Ecommerce—has the capability of changing the way companies deal with their customers, suppliers, and employees. If applied properly, sophisticated computer networking can help companies make their operations a lot simpler. In effect, computer networking provides a road to the future by allowing companies’ information systems to talk to one another. E-commerce enables businesses to streamline their everyday operations. At the same time, computer networking is allowing the dissemination of important business intelligence to the appropriate parties, whether they are within or outside the organization. This important area of computer networking is addressed in Chapter 5.

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BUSINESS INTELLIGENCE SYSTEMS DEFINED Fundamentally, business intelligence systems make great use of data marts and data warehouses as well as operational databases for the purpose of measuring historical activity. Over time, however, business intelligence activities have expanded to include other kinds of data, information, and knowledge that are future oriented. For example, software developers and their clients are integrating data mining tools to anticipate the future based on historical data, information, and knowledge or visualization tools to scan quickly large amounts of relevant information and knowledge. Other companies are integrating text and images with data marts and data warehouses, using collocated document management systems or object relational databases. Also, there is a movement to “push” relevant information and knowledge to users in real time based on predefined business rules or collaborative arrangements among company personnel. From this broad perspective, companies are looking at the organization holistically for a thorough understanding of its operations within a BIS operating mode. This generally means extending a company’s functions, processes, and technology via E-commerce to its trading partners (i.e., customers and suppliers). A business intelligence system centers on managing internal and external information, knowledge, and their resulting intelligence in a proactive manner in order to create a competitive advantage that is linked to a company’s achievable objectives and its measurable goals. It should be noted that an effective BIS operating mode centers on organizing and displaying business intelligence about important topical areas rather than trying to tell everything that is known. A business intelligence system can be looked upon as a set of tools and applications that allow decision makers to gather, organize, analyze, distribute, and act on critical business issues, with the goal of helping companies make faster, better, and more informed business decisions. By contrast, knowledge management systems (KMSs) tend to focus on unstructured data, such as text, documents, personal notes, Web pages, and experiential knowledge. Key knowledge management tools are text retrieval engines and linguistic analysis and artificial intelligence tools for searching large bodies of unstructured data for specific trends or patterns or relationships. Knowledge management applications tend to support previously ad hoc business processes, such as gathering competitive intelligence, creating personalized newspapers, managing skills inventories, documenting “lessons learned” from business projects, and synthesizing “best practices” from previous decisions and programs. Essentially, knowledge, like information, helps decision makers get a handle on the whys and wherefores of a company’s operations. However, for a thorough understanding of what is really going on, a business intelligence operating mode is generally needed to complement a KMS approach. That is,

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it may be necessary to utilize all kinds of systems and related software to look at problems from different perspectives. In light of the essentials of business intelligence systems as discussed in this chapter, they can be defined as systems for business that turn selected data, information, and knowledge into desired intelligence for business gain by decision makers. The type of system and software used is situational. Business intelligence systems employ various analytical and collaborative tools and utilize a database infrastructure—all within a global computer networking architecture. Overall, business intelligence systems provide decision makers with the ability to understand (i.e., the intelligence to gain insights into) the relationships of presented facts in the form of data, information, and knowledge in order to guide action toward a desired actionable goal. They provide decision makers with timely data, information, and knowledge for problem solving and, in particular, problem finding. Convergence of Business Intelligence Systems with Knowledge Management Systems In the near future, business intelligence systems and knowledge management systems are expected to converge. From a business perspective, companies will continually look for ways to leverage business intelligence for competitive advantage. In the current business climate, companies are recognizing that data, information, and knowledge are most valuable resources. Consequently, companies will regularly realign processes and reshape organization structures to expedite the acquisition, utilization, and application of data, information, and knowledge as well as the resulting business intelligence. From a technology perspective, all critical information and knowledge will be stored in relational and/or object-oriented databases and various engines will be used to create a giant publish-and-subscribe architecture that can provide business intelligence based on predefined preferences or business processes. In a BIS environment, decision makers are not forced to shift paradigms to access and analyze business intelligence. Query/reporting and analysis tools are embedded into applications that support core business processes. Access is intuitive, analysis is immediate, and little training is required. Decision makers can take action within the same context as their analysis. All analyses and actions are documented and shareable with co-workers, creating a continuous feedback loop. From this view, there is considerable overlap between knowledge management and business intelligence. The basic difference is that business intelligence implies the existence of databases and data warehouses, whereas knowledge management does not always. Also, knowledge management is used to extract, document, and catalog written and unwritten business rules as well as contextual information based on experience that individuals store in their minds.

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TYPICAL EXAMPLE OF AN EFFECTIVE BUSINESS INTELLIGENCE SYSTEM A typical example of an effective business intelligence system that embodies the four essential elements set forth above is Northern Trust, a Chicago-based banking and financial services company. To make informed decisions, company personnel need strategic business intelligence and not great quantities of statistical data. That requires a reporting solution that could consolidate millions of pieces of data and distill historical trending information into a half-dozen computer screens that contain the most critical data. The company’s clients include large institutions that hire managers to invest in different kinds of capital markets, including equity, fixed income, real estate, and other asset classes. The company tracks how well managers invest those assets relative to indexes such as the Standard & Poor’s 500 and the Dow Jones. All of this data is multidimensional because both financial analysts at Northern Trust and clients are looking at investments in different countries over various periods, such as five to 10 years. That requires examining the characteristics of the markets in which managers are investing and determining why they underperformed or overperformed. To accomplish this monumental task, Northern Trust began a series of pilot programs to build a business intelligence solution using Visible Decisions’ In3D three-dimensional data visualization tools. The system allows financial analysts to examine how well managers are investing over time by drilling down into the consolidated data and quickly extracting all the historical information that reflects the performance, patterns, and trends of their investments. This data is stored on an IBM RS/6000 server running Sybase System 11. The bottom line is that their clients save time by using a business intelligence solution.4 PRIOR INFORMATION SYSTEMS LEADING UP TO BUSINESS INTELLIGENCE SYSTEMS Past and current information systems that influenced today’s business intelligence systems indirectly are integrated management information systems,5 realtime management information systems,6 and distributed management information systems.7 In turn, continuing developments of information systems will be explored in the next sections of this chapter. For the most part, past and current systems center on the processing of a company’s mission-critical applications. The term critical generally refers to applications without which the business could not continue to operate. Essentially, integrated, real-time, and distributed management information systems center on producing periodic reports, not only to recap past operations with an accent on exception items but also to pinpoint possible control problems about current and upcoming operations for lower and middle management. Although these factors represent improvements over prior information systems, they can be viewed from a

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two-dimensional framework—that is, the computer gives a periodic answer that is indicative of what should have been done or what should be done to control operations. However, in this fast-changing world, there is a need to bring in a third-dimensional viewpoint—namely, that of the decision maker, who brings personal judgment, expertise, and the like to bear on the whole process of the problem. From this broadened perspective, the manager is able to get a macroview of the problem and its solution. CONTINUING SYSTEM DEVELOPMENTS THAT ARE USED FOR BUSINESS INTELLIGENCE Because the preceding systems are rather narrow in their perspective (twodimensional framework), a much broader perspective (three-dimensional framework) is found in such systems as decision support systems (DSSs).8 A DSS allows managers and their staffs to be at the center of the decision-making process as changes occur through the use of computer query capabilities to obtain requested information. This is in contrast to relying on periodic control reports, for the most part, as found in previous information systems. Decision support systems can be viewed from an individual and a group perspective as well as through their tie-in with executive information systems (EISs).9 Additionally, on-line analytical processing (OLAP) systems are considered to be an extension of DSS and EIS.10 Many times, these current systems are publicized as the DSS/EIS/OLAP continuum, which can be related to idea-processing systems (IPSs).11 No matter what their orientation, these systems can be used to complement a BIS operating mode. Complementary to these current information systems are knowledge management systems (KMSs).12 Fundamentally, these systems center on managing the vast knowledge of a company’s resources for competitive advantage. Such systems make a company smarter and more effective by retaining and growing the knowledge of how it operates today and tomorrow. Business intelligence systems build upon KMSs by giving decision makers the means to understand customers better than competitors can. These systems also give decision makers the opportunity to understand their own operations that may be linked to external sources on a global basis. In Chapter 4, KMSs, OLAP systems, DSSs, and EISs will be covered in more detail and their tie in with business intelligence systems on a day-to-day basis will be discussed. Similarly, idea processing systems will be covered in the next chapter. Due to the importance of utilizing knowledge within a BIS environment, it would be helpful to look at past and current developments. These include expert systems and neural networks, which are covered below. Basically, these system approaches to knowledge are focused on taking a micro approach to problem solving versus a macro approach that is used in knowledge management systems. An expert system arrives at intelligent solutions to user queries by using the

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rules contained in the system’s knowledge base. A knowledge base consists of If-Then rules, mathematical formulas, or some other knowledge representation structure to represent the knowledge of experts in a certain domain. An expert system scans through its knowledge base to find the appropriate rules, formulas, or some other knowledge structure to apply. Knowledge for such a system is extracted from human experts (i.e., domain experts) on the subject in which the expert system is expected to specialize. The knowledge in the form of rules is then stored in the expert system’s knowledge base for use when needed. Expert systems are designed to mimic the problem-solving abilities of experts in a particular domain. Expert systems can be thought of as knowledge transfer agents. Because an expert system works roughly the way human experts do, it combines factual knowledge with rules that experience teaches (i.e., heuristics) and then makes inferences about the situation at hand, whether it is diagnosing the financial portfolio of a client or the cost of a new product. In light of these facts, an expert system can be defined as a computer program that embodies the expertise in a specific domain that would otherwise be available only from a human expert. It represents a codification of the knowledge and reasoning used by human experts. Once operational, it can be copied and distributed at little marginal cost to assist users, whether they are experts or inexperienced personnel. From this perspective, expert systems can help company employees to be more productive when assisting users, who may be inside or outside the organization.13 Since an expert system contains expertise in the form of a knowledge base, the system goes through its knowledge base and picks out the most appropriate response. The real problem is that if a person queries the expert system about something outside of its knowledge domain, it cannot respond. This is where neural networks come into play. The key distinction between expert systems and neural networks is that neural nets do not involve specific recordings or transcriptions of someone else’s thinking. Neural networks can learn from experience; they can be used in medical testing and financial applications. Because a neural network does not require a complete series of rules and extensive programs to interpret them, it learns the human decision-making process by example. By exposing a network repeatedly to the problems that must be solved, the system internally develops the proper algorithms for problem solving on its own. In a neural network, each input signal is directed by the weighting factor that determines the extent of its influence on the output. The weighting factors are adjusted by the processing nodes as data is processed. Hence, there is no need for information to be stored as a set of values in specific memory locations. On the other hand, expert systems require that large amounts of memory be used to store the knowledge base to be interpreted and processed according to rigidly specified sets of If-Then-Else instructions. They depend on accurate and complete data to check against hundreds and even thousands of stored rules. Typically, a neural network provides a limited explanation as to

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how it arrived at a decision, while an expert system offers a great deal of explanation to justify the decisions they make. Neural networks are very effective in handling fuzzy, incomplete, and distorted data, thereby making them suitable for supporting decision making under conditions of uncertainty. This feature makes them an excellent candidate for financial applications, such as stock market analysis, economic forecasting, and insurance underwriting, but not for standard transaction processing applications, such as accounts receivable and payable, payroll, and inventory updating. In summary, neural networks are not good alternatives to traditional information processing or to expert systems. But rather, they provide some of the missing pieces of the overall corporate information systems environment. Currently, neural networks are extremely useful in data mining and other database specialized tasks (as will be pointed out in future chapters). It should be noted that many other types of information systems that tie in with business intelligence systems could have been included in the text. These could have included multimedia systems, visual information retrieval systems, geographic information systems, fuzzy systems, and chaotic systems. Space limitations preclude their inclusion. VIRTUAL REALITY (VR) SYSTEMS COMPLEMENT BUSINESS INTELLIGENCE SYSTEMS Although business intelligence systems have their roots in prior information systems, this does not mean that the capability of BIS technology need end at this point. Rather, there is a migration to something that makes the output from broad-based business intelligence systems more meaningful and interesting to its users—namely, virtual reality (VR). The transition to a combined BI/VR environment must be carefully managed if it is to benefit the typical company. This combination will bring new levels of involvement by users, increased flexibility in their analyses, and unprecedented amounts of new intelligence to users. If the transition is not properly managed, this new era will result in a widening gap between those empowered with newer technology and those without. The integration of business intelligence systems with virtual reality has the capability to help users, particularly decision makers, think from a different perspective to enhance their skills. Experience via VR What Business Intelligence Systems Display One way to experience what business intelligence systems display is to enter virtual worlds. In a VR experience, sophisticated interactive computer programs put a person inside a world of computer graphics. This allows the person to treat system-generated objects almost as if they were real things. The person interacts with the environment using special clothing and fiberoptic sensors, if deemed necessary, that interpret body positions as computer commands. In the-

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ory, the person can create a world limited only by his or her imagination and programming capabilities. Currently, designers of VR systems are moving more toward computer models that give superior depth perspective—even greater than that provided by the human eye. For example, Computer Associates International has acquired two key players in 3-D media technology. 3Name3D is an experienced creator of 3-D frameworks that model various real-world and imaginary environments. Viewpoint DataLabs International has developed a massive collection of the mathematical descriptions that capture the appearance and behavior of 3-D objects. Designers, instead of building physical models, are building them out of light, which gives the designers the flexibility to change things fast and easily. Daimler-Chrysler, for example, uses this approach to reduce engine bay space and create larger passenger compartments. It also uses virtual reality to fine tune its cars before they reach the clay model stage. Preliminary air flow and crash tests can be conducted without using cars or expensive wind tunnels and crash sleds. Similarly, in a manager’s world, virtual reality provides the capability to experience what a business intelligence system displays. For example, a sales manager could fly over a simulated landscape of sales by specific areas in terms of sales dollars and time periods (past four quarters versus budgeted four quarters). The color red could be used to indicate where sales efforts have been and will be lagging and the color green could indicate increased sales. Also, the color yellow could be used to indicate that sales have changed less than 5 percent from their budgeted amounts. The end result is better quality analysis in less time. Some virtual reality experts predict that VR will eventually have an impact as great as the invention of writing. If they are wrong, it will not be for any want of ambition or confidence among pioneers in the field. The major drawbacks are the ability to write complex software programs and the speed of light—that is, the limitations of the physical world.14 Going a step further, virtual reality has the potential to enhance the actions and performance of the Antichrist and his followers. Because virtual reality is equal to or sometimes better than reality, the Antichrist will give his followers many amazing demonstrations of his power to change things. Specialized and powerful VR programs will give the Antichrist the outward appearances of unusual capabilities. To some, these demonstrations will be miracles which are actually far from the truth. Thus, virtual reality systems along with wisdom management systems (to be discussed later in the chapter) have the capability of demonstrating power that can be attributed only to God himself. WHAT LIES BEYOND BUSINESS INTELLIGENCE SYSTEMS To better visualize what lies beyond business intelligence systems, an initial look at the future of computing power would be helpful. Ray Kurzweil, the author of The Age of Spiritual Machines and a prominent inventor and business

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leader in the field of artificial intelligence, believes by the year 2030 that a $1,000 personal computer will achieve the full capacity of the human brain. This prediction is predicated on the ongoing exponential growth of computing power. Currently, computers remember trillions of facts faultlessly and are also much quicker than the fastest intellect. They are able to search a database with billions of records in a fraction of a second. Kurzweil predicts that 30 years into the future computers will have this capability and others, including consciousness and the ability to have emotional and even spiritual experiences. To accomplish this feat, computers will make use of intelligent agents, which are available today. Fundamentally, intelligent agents are software routines designed to retrieve automatically the information the user needs and perform actions based on that information. Also called bots (as in robots), intelligent agents are interesting in their potential. Although bots today can perform research, chat with the user, gather news, play games with the user, and track stocks, many of the most popular ones are used by consumers and businesses for comparison shopping over the Web. In short, bots are not yet extremely intelligent, although it is expected that they will get smarter, as will information technology as a whole. In terms of the more immediate future, reference can be made to the following: speech recognition systems, smart technology systems, and wisdom management systems. All of these systems are covered below. Speech Recognition Systems Speech recognition technology, which has been around for over 25 years, only recently has borne some fruit. It has several components—namely, noise canceling input, a recognition engine, vocabularies, application interfaces, and rudimentary natural language processing. There are two classes of speech recognition technology: speaker dependent, in which the user has to train the system to recognize his or her voice, and speaker independent. In addition, there are two principal categories of speech recognition: keyboard and keypad. Keyboard applications allow users to speak directly to their computers, complementing or replacing the computer keyboard. Keypad applications use speech to replace the telephone keypad as input for accessing voice mail and navigating a telephone system’s menus. Also, they allow the telephone to act as a remote computer peripheral. Keypad applications tend to use limited vocabularies because they are focused on fairly narrow subjects. Limited vocabularies make it easier for these applications to be speaker independent. The limited space also allows for some elements of natural language processing. In contrast, keyboard applications, particularly full dictation programs such as IBM’s ViaVoice and Dragon Systems’ Naturally-Speaking, tend to use large vocabularies that, for now, require them to be speaker dependent. The recent breakthrough that has jump-started the speech recognition market is the release of products based on large vocab-

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ularies and continuous speech recognition engines. Until recently, large vocabulary systems were limited by discrete speech recognition engines that required users to pause between each word. In a similar manner, natural language technology is the capability of a computer to decipher the meaning in ordinary, everyday speech, rather than requiring users to speak in prescribed patterns. A very limited application of the technology, which relies on the computer to decipher meaning from keywords, allows users of IBM’s ViaVoice Gold to format Word documents. Currently, financial services companies appear to be at the forefront of adopting speech recognition technology, both in call center applications for customers and desktop applications for workers. Chase Manhattan has literally removed the computer keyboards in the office of its Global Trust Services that processes bearer bonds. The company uses speech recognition technology to boost efficiency, thereby reducing the time it takes to process a single bond from seven minutes to less than one minute. Because the application is a narrow one—only processing one type of bond—the bank has been able to develop a speakerindependent, natural language application based on 200 keywords.15 In the future, speech recognition will allow natural language to be used to interact with information technology. Learning a specific programming language or syntax limits the use of speech recognition to a select portion of the world’s population. The storage demand generated by speech is not as great as other applications, as one hour of telephone quality recording generates about 22MB of storage. With a large lexicon of words (tens of thousands of words), meaning can often be ascertained only by contextual reference, thereby making universal speech recognition several years away. The technological advances in microprocessors and improvements in mathematical models are making the task of speech recognition systems easier, if only by employing more computing power. Speech recognition systems will be fundamental to taking information technology to the general population. Smart Technology Systems Smart technology systems, also known as data or information fusion, are fanning out to applications as diverse as systems that predict earthquakes or traffic jams and robots that display hand-eye coordination. Some researchers see information fusion one day surpassing artificial intelligence and fuzzy logic in the breadth of real-world problems it can address. But others caution that the technology, which had its origins in military targeting systems, is still too new to come to any conclusions. Just as the human brain assimilates input from all five senses to savor, say, a tangy strawberry, information fusion unifies multiple data streams in computers. For example, automatic target recognition—the seminal application—melds input from several sources into perceptions of “friend” or “foe.” By adding generalized object recognition, information fusion is equipped for

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many more object types, ranging from solder joints to the next likely circuit board to fail. Many of the researchers who pioneered neural network algorithms have turned to information fusion as a more practical alternative to problem solving. Even the theoretical developments of information fusion are more powerful than current neural network theory because fusion researchers focus on information processing rather than on exactly how real neurons work in the brain. At the highest level, a few researchers want to uncover high-level types of fusion in the human mind in the hopes of someday writing algorithms to emulate them. For instance, Gordon Shaw (professor emeritus at the University of California, Irvine) has delved into why listening to Mozart increases scores on spatial reasoning tests. Shaw claims that the answer lies in the mechanism of information fusion in the human brain.16 From another perspective, smart technology can be related to knowledgebased or smart products that filter and interpret information, enabling the user to act more effectively. For example, a coat that heats or cools in response to temperature changes or a tire that tells the driver of its air pressure are versions of smart products. These products can be identified by such characteristics as being interactive and smarter the more they are used. Also, they are capable of being customized to fit a customer’s changing needs and can be tied in with the capability of preventive maintenance where deemed appropriate. The bottom line is that a person’s use of knowledge-based products may be critical to his or her everyday operation and economic success. Companies that know how to convert information into knowledge will be more successful than those that do not.17 Wisdom Management Systems Wisdom management systems (WMSs) can be looked upon as an expansion of knowledge management systems and business intelligence systems, which inspire the loyalty and trust of a company’s customers and its employees. A comment from Aristotle—one of the great masters of the past—is relevant here: “Action without knowledge is folly. Knowledge without wisdom is perilous.” Company managers operating within a wisdom management system environment will encourage customers and employees to generate ideas for new products and services. Similarly, wise managers will assist their customers and employees to anticipate problems and solve them before they happen as well as helping both achieve their objectives and goals such that there is a “win-win” situation. Wise managers will also help both identify opportunities that come out of problem finding (which will be covered in Chapter 3). Wisdom management systems will have the capability of assisting decision makers in new thinking about their companies and themselves. The conventional wisdom, such as “If it ain’t broke, don’t fix it,” will facilitate decision makers in their new thinking, such as, “There’s gotta be a better way.” Another bit of conventional wisdom states that there is a time and a place for everything. A

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useful wisdom management system will help decision makers redefine the space/ time continuum. Still another conventional wisdom item is that you get what you pay for. A newer direction from a wisdom management system might be that one particular combination gives the company a lot more for its money (i.e., a real bargin, all things considered). Essentially, an effective wisdom management system provides decision makers with the ability to recognize when old rules no longer apply and new ones need to be created, even on a daily basis, if necessary. Building upon the earlier comments concerning the Antichrist, it should be noted here that he and his followers will employ the basic elements of wisdom management systems to prove their authenticity. Their ability to forecast the future, especially with great accuracy, will lead many people away from God. The rationale is that people have a tendency to find inspiration and strength in the wisdom of such a person, even though the Antichrist is using computer technology in devious ways to enhance their perception of him. In addition, it should be noted that the Antichrist recognizes how often and how deeply people can be changed by whom they have said yes to. This may be a frightening thought, but the manipulation of wisdom by the Antichrist can have far reaching consequences for all of mankind. At this point, the reader can justifiably ask the question: Why should this text concentrate on current business intelligence systems when what is really needed today is greater wisdom in running a typical business organization? The answer lies in the fact that wisdom management systems are just a concept today. It may well be in the near future that these types of systems will be taken seriously and the appropriate computing infrastructure will enable their implementation. Needless to say, time will be the real judge of this new approach to assisting decision makers in meeting the challenges of today and tomorrow. SUMMARY Initially, the chapter focused on the need for a broad-based approach to business intelligence in an ever-changing business environment. In the discussion that followed, the relationship of data, information, and knowledge to intelligence was explored. Next, the employment of business intelligence to improve corporate performance was set forth as a way to leverage a company’s position. The second half of the chapter focused on the essential elements of business intelligence systems as well as different approaches to processing missioncritical applications that have preceded business intelligence systems and continuing system developments. For the most part, previous information systems assisted managers first and provided accounting results second. Although this evolving development of information systems is still continuing, it should be recognized that business intelligence systems are not the end state. The next thrust in information systems is expected to be called “wisdom management systems.”

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NOTES 1. Peter Senge, The Fifth Discipline (New York: Doubleday, 1990), p. 1. 2. Walter A. Kleinschrod, “In Business, Knowledge Is Power,” Beyond Computing, March–April 1995, pp. 36–38, 40. 3. Thomas A. Stewart, “Brainpower,” Fortune, June 3, 1991, pp. 44–45. 4. Peter Ruber, “Getting Smart with Business Intelligence,” Beyond Computing, June 1998, pp. 19–22. 5. Robert J. Thierauf, Systems Analysis and Design of Real-Time Management Information Systems (Englewood Cliffs, NJ: Prentice-Hall, 1975). 6. Ibid. 7. Robert J. Thierauf, Distributed Processing Systems (Englewood Cliffs, NJ: Prentice-Hall, 1978). 8. Robert J. Thierauf, Decision Support Systems for Effective Planning and Control: A Case Study Approach (Englewood Cliffs, NJ: Prentice-Hall, 1982); User-Oriented Decision Support Systems: Accent on Problem Finding (Englewood Cliffs, NJ: Prentice-Hall, 1988); and Group Decision Support Systems for Effective Decision Making: A Guide for MIS Professionals and End Users (Westport, CT: Qurorum Books, 1989). 9. Robert J. Thierauf, Executive Information Systems: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1991). 10. Robert J. Thierauf, On-Line Analytical Processing Systems for Business (Westport, CT: Quorum Books, 1997). 11. Robert J. Thierauf, Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1993). 12. Robert J. Thierauf, Knowledge Management Systems for Business (Westport, CT: Quorum Books, 1999). 13. Robert J. Thierauf, Expert Systems in Finance and Accounting (Westport, CT: Quorum Books, 1990). 14. Robert J. Thierauf, Virtual Reality Systems for Business (Westport, CT: Quorum Books, 1995). 15. Kimberly Patch and Eric Smalley, “Speech Recognition Makes Some Noise,” InfoWorld, February 2, 1998, pp. 69, 74. 16. R. Colin Johnson, “Data Fusion New Darling in Smart Technology,” Electronic Engineering Times, June 14, 1999, pp. 1, 16. 17. Stan Davis and Jim Botkin, “The Coming of Knowledge-Based Business,” Harvard Business Review, September–October 1994, pp. 165–170.

2 Creativity Underlies Effective Business Intelligence Systems THE NEED FOR CREATIVITY WITHIN AN EFFECTIVE BIS ENVIRONMENT In this 21st century, creative agility is essential for decision makers, since many businesses are at a crossroads. Businesses can either go forward or backward. Global events are reshaping business at a very rapid pace. According to creativity expert Edward de Bono, American chief executives are not creative enough to meet the challenges facing their companies today or in the future. A sweeping generalization to be sure, but de Bono is convinced that executives lack creativity even though creative-thinking techniques have been taught and promoted in 45 countries for many years. He even goes as far as to state that U.S. executives are more uncomfortable with “concept” than executives in most other developed countries. Thus, he feels that creativity has taken a back seat in the information age. Executives and managers below these top executives need “to rethink the policy of not fixing it until it’s broken.”1 In other words, they should think in terms of be willing to try something new. The focus should be away from “what is” to “what can be.” Today, almost any person within a company using a modem can gain access to almost any piece of information and/or knowledge using some type of system. When everyone can download the same information and/or knowledge, the competitive edge goes to those companies that have taken a creative approach to their everyday activities. This creative orientation can be stated in a series of questions. How many new ideas do a company’s employees generate? How well does the company carry them out? Which of the company’s traditions, policies, and rules get in their way? Who are the company’s most creative people? How does a company go about hiring them? Needless to say, creative people need

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to be nurtured in the workplace so that a company’s creative assets (i.e., its intellectual capital) will more than pay for themselves over the long term. It is counterproductive to talk about creativity and then retain a working environment that deadens the imaginative and the creative spirit of employees. Fundamentally, this chapter sets the tone for the remaining chapters of the book by stressing the need for creativity within a BIS operating mode. The emphasis is on both the traditional approach to teaching creativity and one demonstrating how computer software packages within a collaborative computing environment can help the typical manager to be more creative on the job. To ensure that creativity does not get blocked in companies today because of a mindset that overemphasizes one style (i.e., a steady incremental change), it is often necessary to shift gears and adopt a free-wheeling experimental approach using newer creativity tools. Overall, a BIS environment can be extremely helpful to the typical company in promoting creativity, especially helping a company determine what products or services are desired by its customers before customers are aware of them or desire them. Creativity as Part of a Company’s Corporate Philosophy In the past, the conventional suggestion box in many companies served as employee input for improving operations. Fortunately, this uncreative, assemblyline mentality has given way to employee involvement and more participatory decision making via the creative process. Today, progressive managers realize that most employees want to be involved in the process of change, to have input in streamlining procedures, and brainstorming suggestions to help the company progress. Most importantly, these managers are realizing that employees who do the actual work, regardless of their relative status or educational level, are usually the ones with the most interesting and progressive ideas. Front-line employees ask the right questions and offer relevant suggestions because they have hands-on experience and are the ones who are most affected by change. To state it simply, these employees frequently know what will work and what will not. For example, a manufacturing company decided to computerize its operations to maximize efficiency. The project was an immense undertaking because the company had subsidiaries in 10 states. On the surface, the computer linkage would greatly enhance communications and standardize procedures on a companywide basis. However, this company made one bad mistake. Without asking for any employee input, the company hired an outside consultant who had ample computer knowledge but little knowledge of the company’s idiosyncrasies to install the network. Not one employee who would actually use the system was consulted. Twelve months and many thousands of dollars later, the company is still backtracking to correct the mess. Needless to say, the company had to resort to its employees for answers.

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Various Ways of Viewing Creativity Inasmuch as there are several ways of viewing creativity (although the common thread seems to be making associations of dissimilar things), each creative mode is almost a discipline entirely unto itself. The first is theoretical creativity, associated with profound and abstract concepts—the material found in the superpower intellect. Curiously, the Albert Einsteins and Sir Isaac Newtons start with almost childlike questions, primal thoughts such as “What is the universe?” “What are air and light?” and “Why is fire hot?” From this simple beginning, Einstein envisioned himself riding on a beam of light through time. It is so basic, but it is the starting point for brilliant and creative thought. On the other hand, applied creativity is the scientist and thinker. Marie Curie, Henry Ford, Alexander Graham Bell, and Thomas Edison are excellent examples of this type of creativity. They created by taking sensory experiences and things already in existence and translating them into a technological process. Essentially, applied creativity is the main thrust of this text and should be an integral part of a company’s corporate philosophy. Artists and writers fit into two categories that are closely related: inspired and imaginative. Although their work may be deliberate, inspiration often comes from the subconscious, an almost dreamlike state from which new things suddenly appear. Scientists have for some time been talking about the left and right halves of the brain. The left side controls logic and reason, while the right side contains the intuitive material. Creativity is at its height when there is a flow between the two halves. The artist, for example, may become totally immersed in his process, feeling almost a part of what he is painting, to his mind becoming light, shadow, color, and texture himself. Similarly, the writer of good prose feels a certain meter in the use of words and phrases, literature that not only tells a story and imparts information but reads easily and flows like a melody. Other ways of viewing creativity include prescriptive and natural creativity. Prescriptive creativity is what intellectuals concerned with social and philosophical matters apply to describe the role of man in relation to the universe and his peers. From Plato to Machiavelli and religious thinkers, these people come up with new observations—creative observations—of how humans can live. Natural creativity deals with the body and has been most often associated with great dancers, singers, and performing musicians. It is more than physical ability, for intellect must also go into the performance. In this same vein, professional basketball stars are as creative as any dancer when they appear to float through the air, executing moves never seen before. AN EXPANDED GLOBAL PARADIGM TO REPLACE THE PAST VIEW OF THE WORLD Due to the many mistakes made in the past by managers of U.S. businesses, AT&T, Procter & Gamble, and DuPont are offering employees personal growth

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experiences of their own, hoping to spur creativity, encourage learning, and promote ownership of the company’s results. A handful of visionary leaders, such as General Electric’s chairperson, Jack Welch, are going beyond training seminars to a fundamental reordering of managerial priorities. Meanwhile, a small network of consultants, managers, and academics are working to transform businesses. Believing that the world is undergoing major change, they call for a new paradigm (i.e., a new framework for seeing and understanding business) that will carry humankind beyond the industrial age. The result is a convergence of managers seeking ways to reverse America’s fall from dominance, with thinkers drawn to business, perhaps as the most powerful institution in a global society. The new framework might be described as New Age without the glazed eyes. The word “paradigm” comes from the Greek for “pattern,” and the new paradigm is just that: a new pattern of behavior that stems from a new way of looking at the world. The old world view—Newtonian, mechanistic, and analytical—is present in everything from the Constitution, with its clockwork system of checks and balances, to the assembly lines devised by Henry Ford. The new paradigm takes ideas from quantum physics, cybernetics, chaos theory, cognitive science, and Eastern and Western spiritual traditions to form a world view in which everything is interconnected, in which reality is not absolute but a byproduct of human consciousness. This paradigm is not promising universal enlightment in the near future. However, it is an attempt to deal with a very difficult period of time in business. What has emerged so far is a host of management theories and practices befitting an age of global enterprise, instantaneous communication, and ecological limits. Some are familiar: hierarchical organizations being replaced by more flexible networks; workers being empowered to make decisions on their own; organizations developing a capacity for group learning instead of waiting for wisdom from above; and national horizons giving way to global thinking. Others may still seem a little far out: creativity and intuition joining numerical analysis as aids to decision making; nurturing and caring being recognized as motivators in the workplace; even the importance of the profit motive being questioned by those who argue that the real goal of enterprise is the mental and spiritual enrichment of those who take part in it. Currently, each of these developments individually is just one manifestation of progressive management thinking. Together, they suggest the possibility of a fundamental shift in management thinking. Applied to business, the old paradigm held that numbers are all-important, that professional managers can oversee any company, that control can and should be held at the top. In contrast, the new paradigm puts customers and employees at the center of the business universe and replaces the rigid hierarchies of the industrial age with a network structure that emphasizes interconnectedness. To illustrate this new paradigm, Levi Strauss chairperson Robert Hass sketched his idea of the corporation of the future: a global enterprise relying on employees who are able to tap their

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fullest potential and managers who act not as authority figures but as coaches, facilitators, and role models. Levi Strauss is striving to transform itself along those lines because it needs creative thinking and rapid response to satisfy a fashion-conscious public. The point of the new paradigm is to encourage people to think for themselves. Another company that is using the new paradigm is General Electric. Having streamlined General Electric organizationally with a number of sales, acquisitions, and plant closings, chairperson Jack Welch has now turned to this new framework. However, GE’s moves bear the twin hallmarks of new-paradigm thinking; that is, the systems view—seeing everything as interconnected—and the focus of people. Welch’s goal is fast turnaround, and to get it he intends to create what he calls the “boundaryless organization”—no hierarchical boundaries vertically, no functional boundaries horizontally.2 Bringing Home Tomorrow’s Bright Ideas Today The need for employing a creativity approach is evident in the fast-changing information systems field. Since it is difficult to spot the next big technology trend every time, investors look at those vendors (partners) that exhibit agility in an environment where there are no easy answers. Identifying vendors that can handle truly revolutionary technology shifts (as opposed to those that make useful but incremental improvements to their old products) is an imprecise science. Seemingly obvious measures, such as banking on a vendor’s marketleading position, does not work. For example, who would have thought a decade ago that IBM, Digital, and Wang could have been toppled from their lofty perches? While some market leaders, due to their entrenched positions, tend to shun disruptive technology changes, still other high-tech leaders adapt well to wholesale technology shifts and promise to provide appropriate products and technologies well into the future. The best way to identify them is to consider their dedication to research and development (R&D) and to examine their ability to incorporate innovations into products—either through internal R&D programs or timely investments in startups. In fact, experts say such an examination should be a key part of any forward-thinking company manager’s procurement process. With the arrival of “Internet time,” in which time to market is everything, and with a seemingly bottomless supply of venture capital to fund high-tech startups, major companies such as Cisco Systems and Microsoft have been out shopping for innovation during the past few years. Company managers need to keep an eye on their key suppliers’ technology road maps and development processes as well as stay abreast of new startups. In both cases, the skills to get a read on a supplier’s ability to change with the times are crucial. Typically, a company’s ability to meet its customer demands is not necessarily an indication of its ability to innovate. According to Clayton Christensen, author of The Innovator’s Dilemma, successful, customer-focused vendors deliver prod-

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ucts that customers currently demand. Although this is a good thing, too much customer focus leads them to neglect the disruptive technologies in which customers are not yet interested. A disruptive technology is one that may underperform an established technology but be cheaper, simpler, and more convenient to use. In the late 1980s, PCs versus mainframes was a good example of disruptive versus traditional technologies. Currently, Internet telecommunications versus switched-circuit technologies is another example. By the time corporate customers are interested in disruptive technologies, the customer-focused vendor has missed the boat. For the most part, the customer is just as clueless as the vendor is about the impending disruption. Since there are no true quantitative measures for identifying a supplier’s ability to adapt, experts point to practices that may show a vendor’s innovative abilities. Among them is prototype-driven development versus a traditional development model that is specification driven. This latter model means that the developing company researches needs, writes, and freezes a specification, and gradually develops increasing detail in the product. This is also called a topdown approach. By contrast, a prototype-driven approach is much more adaptive since a developer builds a prototype quickly and evolves it along the way. In environments that are rapidly changing, the prototype-driven model tends to be a lot better. Virtually everyone in Internet space has gone to this model over the traditional method. Yahoo and Microsoft both provide good examples of this practice. Microsoft has used a prototype-driven R&D approach to see how Office 2000 could better interact with Web servers. Office 2000 is the desktop application suite that enables users to store documents directly to Web servers in HTML (Hypertext Markup Language) and XML (eXtensible Markup Language) formats. New Approach Needed to Teach Creativity In comparison to managers in the United States, the Japanese are more willing to risk direct-market tests, according to creativity experts such as Edward de Bono. For example, they test hundreds of soft drinks a year. Typically, negativity is far higher in the United States than in Japan. Basically, negativity is being enhanced by the current movement in U.S. colleges and universities to teach “critical thinking.” By definition, critical thinking is analytical. It focuses on “what is,” and presumably such emphasis in U.S. schools of higher learning is designed to turn out future managers with more of the same analytical skills. Certainly, the recent flap in academe over “political correctness” has produced an orgy of analysis—much of it negative and backward looking. Creativity experts feel that U.S. schools of higher learning should be teaching constructive, creative-thinking skills—focusing on “what can be.” They believe what is needed is a whole change of attitude toward creativity in this country. Even competition so dear to most chief executive officers comes in for a knocking, since competition too is mostly hung up on “what is” and the way things are in the present marketplace. It is not enough to move information around, no

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matter how many personal computers are strung together. Their essential message is that a company’s success will be fueled by new ideas and concepts and by chief executive officers eager to embrace them. Overall, creativity experts call for a change in attitude toward creativity if U.S. business firms are going to reap the benefits from available global markets, available capital, personnel effectiveness, and business competence. Success in global markets is going to need a baseline of competence on which to build. This is related to the development of new ideas and concepts that eminates from serious creative thinking. As noted above, the concept of negativity, which can be interpreted as conservatism and complacency, is the biggest enemy of creativity. To think that the company is doing well or is creative enough is fatal in the long run. CREATIVE THINKING DEFINED Numerous definitions of creativity are recorded in the literature. Creativity is often defined by use of synonyms. These include productive thinking, divergent thinking, originality, imagination, and lateral thinking. Because there is no generally accepted definition of creativity, it would be helpful to look at some defintions to date. Parnes, Noller, and Biondi have defined creativity as “the association of thoughts, facts, ideas, etc. into a new and relevant configuration, one that has meaning beyond the sum of its parts—that provides a synergistic effect.”3 Arnold defines the creative process as “that mental process in which past experience is combined and recombined, frequently with some distortion, in such a fashion that one comes up with new patterns, new configurations, that better solve some need of mankind.”4 Adding a decison-making viewpoint, Ackoff and Vergara define creativity in problem solving and planning as “the ability of a subject in a choice situation to modify self-imposed constraints so as to enable him to select courses of action or produce outcomes that he would not otherwise select or produce, and are more efficient or valuable to him that any he would otherwise have chosen.”5 Although this sampling of definitions reveals diverse elements found in creativity these definitions do suggest some common thread found in creative thinking—that is, a new way of doing things. From this overview standpoint, the author defines creative thinking as a process that solves a problem in a new and original way that is useful to those involved in the creative undertaking. It should be noted that the elements of uniqueness of solution and value of results are incorporated into the definition. Both of these elements are essential in defining creative thinking. EFFECTIVE TECHNIQUES TO UNDERTAKE CREATIVE THINKING Currently, creativity techniques have become important tools in the executive suite—corporate America’s reaction to global competition and runaway tech-

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nology. The challenge for an organization personnel is to think up ideas that are novel and innovative and, at the same time, pragmatic. This type of creativity requires being open to new ideas, while avoiding the problem of coming up with the same answers. If company personnel continually come up with the same solutions, it could be that those are the only answers or that they are only looking at the market from one perspective. Companies are buying into the notion that rather than being the province of artists and inventors, creativity is a learnable skill that can be enhanced through practice and training. Traditional creativity techniques and creativity games as well as the newer areas of creative computer software, electronic meeting rooms, and electronic collaborative creativity are covered below. Traditional Creativity Techniques Traditional techniques for undertaking creativity include the following: (1) the creative process, (2) brainstorming, (3) synectics, (4) accurate problem definition, (5) rapid prototyping, and (6) other creativity techniques. All of these areas are covered below, with examples where deemed appropriate. Some of these techniques can assist managers and their support staffs in getting around the need to be right all of the time, which can be a significant barrier to developing new ideas. Thus, it is better to have some new ideas based on knowledge of a company that may prove to be wrong. Or to state it another way, managers and their support staffs will always be right without having any new ideas at all. An early descriptive model of the creative process useful to an individual thinker was formulated by Graham Wallas in 1926. This process appeared in his book, Art of Thought.6 He specified four phases of the creative process: (1) preparation, (2) incubation, (3) illumination, and (4) verification. In the first phase, preparation consists of gathering facts, information, and knowledge that may be applicable to some problem under study. Typically, the second and third phases of this creative process involve initially a pause (incubation), in which some unconscious sifting, sorting, and/or relating of information gathered during the first phase takes place, followed by a sudden awareness or recognition of a new relationship that has importance to the decision maker (illumination). Essentially, incubation is related to the search and identification of ideas and/or problem solution alternatives at a subconscious level, which means that this stage can be seen as the actual creative process itself. The illumination stage is the conscious awareness of new ideas and/or solutions to the problem under study by the decision maker. Finally, in the fourth stage the insight gained from the illumination stage is tested and seen to be either viable and acceptable or not acceptable, in which case it must be reworked until the insight becomes acceptable. The decision maker imposes closure—that is, accepting or rejecting the validity of the idea and/or solution for the problem under study. Although several variations of depicting this process exist in the literature,

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they are all basically the same. It should be noted that there may be a lengthy period between stages. An example of the gap between discovery and use is illustrated by the invention of cellulosic materials by the Swiss chemist George Andeman. And almost 50 years have elapsed before any practical use occurred with artificial silk under the name of rayon by Comte Hilaire de Chardonnet. Although there can be long time lags between the four stages in the creative process, this does not have to be true as will be evident in the newer approach to creativity found later in the chapter—namely, the utilization of creative computer software within a collaborative computing environment. Probably, the best-known creativity technique is brainstorming, which was developed by Alex F. Osborn (cofounder of BBD&O) to help solve advertising problems. It is used to improve problem analysis by providing more possible solutions and unusual approaches to the problem under study. Osborn suggests four rules necessary for the utilization of brainstorming: (1) Judgment is withheld; ideas may be criticized and evaluated later. (2) Wild ideas are encouraged; ideas are easier to modify than to originate. (3) Numerous ideas are desired; more ideas increase the possibility of obtaining an excellent idea. (4) The participants are encouraged to utilize the ideas of others to develop additional ideas. Other recommended procedures include the following: sessions should be recorded because some ideas may be missed during a meeting; the problem must be manageable, even if it requires breaking large problems into smaller parts; and samples should be available if products are being discussed. After the brainstorming session, the group must set up the criteria for evaluation. Then, all the ideas are evaluated based upon the criteria and the best two or three possible solutions are chosen. In the end, it may be necessary to modify any idea in order to make it more closely meet the desired criteria. In this way, a seemingly wild notion can be transformed into a workable solution. No brainstorming process is complete until the most promising solutions are put into practice. Although not as well known as brainstorming, synectics is based on the assumption that creativity can be described and taught. Its purpose is to improve the quality of creative output from those assigned to a synectics team. Essentially, the synectic process involves two steps: (1) making the strange familiar and (2) making the familiar strange. The first step requires that the problem be understood and that the ramifications be considered. The mind tends to emphasize one’s own experiences and to force strange ideas into an acceptable pattern. Thus, it is necessary to reorient these strange ideas into familiar ones. The second step, making the familiar strange, involves distorting, inverting, and transposing the problem in an attempt to view it from an unfamiliar perspective. For more detailed information on synectics, reference can be made to a book by W. Gordon.7 Many problem-solving failures occur because efforts are directed at solving the wrong problem or only parts of a problem. For example, an inability to identify accurately what is going on can lead to inaccurate problem identification. To assist in accurate problem definition (i.e., defining the real problem), a

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cause-and-effect diagram is recommended. Because problems cause other problems, a whole complex of symptoms and problems emerges in need of a solution. A problem-diagramming procedure can help to isolate root causes. First, list all the problems, symptoms, and related problems. Number each one. Next, write the numbers at random on a piece of paper and draw a circle around each. Then draw arrows to show what causes what. For example, if problem 1 causes problem 2, draw an arrow from circle 1 to circle 2. Consider each circled number, asking: Which of the other problems causes or helps cause this one? After all the arrows have been drawn, the root problems become clear. They are represented by the circles with arrows leading only away from them. The real root problem is the farthest one to the left. A newer creativity technique is rapid prototyping. The concept behind rapid prototyping is that it is much easier to discuss a model of something, no matter how primitive, than to talk about a number of ideas. If a picture is worth a thousand words, a prototype can be worth several thousand. Basically, rapid prototyping consists of three Rs: Rough, Rapid, and Right. The first two Rs are fairly self-explanatory—make the model rough and make it rapidly. In the early stages, perfecting a model is a waste of time. The final R, Right, does not mean the model has to work. Instead, it refers to building lots of small models that focus on specific problems. The creator is not trying to build a complete model of the product but rather the individual is focusing on a small section of it.8 There are also other creativity techniques that are oriented toward the individual or the group. A creativity technique that is group oriented uses the five Ws and the H—that is, the technique asks “who, why, where, when, what, and how” to ensure that all alternatives are considered. Other techniques for groups include the following: assumption reversals, metaphors, relational algorithms, and symbolic representations. On the other hand, techniques for the individual include the following: component detailing, force fit, stimulus analysis, and systematized directed induction. A particularly helpful creativity technique has been devised by Edward de Bono (mentioned earlier in the chapter), the British author of more than 30 books and a well-known creative luminary. De Bono’s “Six Thinking Hats” technique is used by Nippon Telephone and Telegraph as well as many other corporations. For this technique, people in a group discussion actually put on different hats that represent different modes of thinking. It is an effective way to switch thinking without offending anyone. The white hat represents information and data, while the red hat legitimizes feelings, intuition, and hunches without the need for justification. In contrast, the black hat is the logical negative—that is, why something will not work. The yellow hat is the logical positive that talks about benefits, feasibility, and the like. The green hat is the creative hat, which allows someone to ask for creativity. Finally, the blue hat is the process control hat that thinks about and plans the thinking itself. Another useful technique for creative thinking is PMI. In this approach, the Plus, Minus, and Interesting points of a decision are listed in separate columns.

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Creativity Games As a starting point to creativity, reference initially can be made to playing with games. Generally, playing with creativity games comes close to sanctioned goofing off—that is, the best ideas emerge when people loosen up and act a little crazy. There are a variety of low-cost creativity games. Circles of Creativity (from New Product Development) is a brainstorming tool that helps ideas, objects, or images fit together in interesting, nonobvious ways. Arrows on the chalkboard device rotate, pointing to dozens of phrases arranged in six concentric circles. The “try to” arrow, for instance, can be turned to suggestions like “moisten it,” “jiggle it,” or “freeze it,” yielding responses from other arrows like “failure,” “gears,” and “cold.” When the director of research and development for Hershey Foods in Hershey, Pennsylvania, was putting together a business analysis, he gave Circles a whirl and landed on “bag it.” After mulling that for a moment, he pictured himself putting whole pieces of the company into a bag. That gave rise to the notion of breaking the company’s Canadian hardcandy market, which had been simply defined as either “sugar” or “sugar free,” into smaller categories such as breath mints, mini-mints, and individually wrapped candies. By looking more closely at specific areas, the company was able to pinpoint growth potential in mint candies that it had never noticed. Hershey now keeps a Circles in every conference room for moments when executives feel “blocked.” Another creativity game is the Creative Whack Pack (from consultant Roger von Oech). It is nothing more than a deck of 64 cards with bold labels, like “Be Whacky,” “Do Something to It,” and “Break the Rules”—all aimed at making people move off the well-worn and predictable corporate path. “Reverse Your Viewpoint” rang a bell with the chairperson of Metaphor Computer Systems (Mountain View, California) as he flipped through the Whack Pack during a meeting with his managers about quality problems. What would they do if they were competitors who had infiltrated executive ranks to hamstring the company? It turned out that the managers were guilty of some of the very sins they listed. For instance, by letting the product engineers dot every “i” on a new design proposal before getting feedback from manufacturing or marketing specialists, they had complicated the route from idea to marketplace and wasted time and money.9 Creative Computer Software Since the main thrust of this section is on a computerized approach to creative thinking, there is a need to go beyond the manual creative games presented above. Games often work best when people meet as a group. However, that is not always necessary with creative computer software (although the focus of this text will be on using a group approach for creative thinking). In addition, it is comfortable to know that a person’s electronic brainstorming partners will

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not laugh at the most idiotic suggestions. Three typical current creative computer software packages are given below. IdeaFisher 4.0 (from Fisher Idea Systems) is a kind of brainstorming thesaurus with two databases that work together. The “QBank” is a collection of nearly 6,000 questions that nudge the user to define the job at hand. The user might be quizzed, for example, about the engineering of a proposed product, its timeliness, and the history of similar products. The “IdeaBank” contains more than 700,000 cross-referenced words and phrases organized into 28 “major categories” and 387 “topical categories.” Typing “blue” triggers a spurt of associations that offer fodder for everything from advertising slogans to new product names (i.e., midnight blue, blue whale, etc.). For example, if a marketer were trying to name a new laundry detergent and wants to convey the message that the product is efficient and environmentally sound, the individual could enter such terms as “environment,” “clean,” and “detergent” and the IdeaBank would give back suggestions that included Breathe, Back to Nature, and Purify. Another example would be a financial planning company wishing to devise an advertising metaphor for safe money. The result could be to think of a person’s money as wearing a seat belt. Another creative computer software package is the Idea Generator Plus (from Experience In Software). It makes the user examine the problem from unexpected angles by having the person respond to such questions as: What similar situations have you been in? What is the opposite of what you want to achieve? Can you think of metaphors that apply? What does the pessimist in you think? Who are the people affected by your decision and what solutions might they offer? A creativity consulting company (Mattimore Communications) used this package while devising an adult board game in which the players pretend to be inventors. When trying to figure out what advice his mother-in-law would give, Bryon Mattimore imagined that she would urge him to “keep it simple.” He created a game good enough that it is now under development at Game Gang, the New York toy company that sells Pictionary and Balderdash. Still another package is MindLink (from MindLink), which gets at the creative quandary in a seemingly roundabout way, sending the user on improvisational tangents called “idea triggers.” The user might be instructed to pick up the nearest magazine at hand and extract two ideas from each of five articles. Or the user may have to jot down 10 objects in view around the room. Perhaps the user will have to work these observations into a short story. Other triggers urge the user to conjure up images by pairing words such as “time” and “confetti,” or “fun” and “cowlike.” After each exercise, the user is asked to figure out how these thoughts might be brought together to help the individual deal with the creative dilemma.10 Overall, the power of the computer, with the assistance of the appropriate creative computer software, can be an important force, assisting managers to do their jobs better. As seen in the above discussion, the use of creative computer software can add a new dimension by enlarging managers’ capabilities to vi-

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sualize and solve present and future problems as well as develop new opportunities for their organizations. To further emphasize this approach, reference can be made to a prior publication on creative computer software by the author.11 Electronic Meeting Rooms Typically, creativity computer systems use electronic meeting rooms that contain networked workstations, large computer-controlled public displays, and audio/video equipment. Some of these facilities require a specially trained operator; others assume operational competence among the group members. A well-known example is the PlexCenter Planning and Decision Support Laboratory at the University of Arizona. The facility provides a large U-shaped conference table with eight personal workstations: a workstation in each of four break-out rooms; a video disk; and a large-screen projection system that can display screens of individual workstations or a compilation of screens. The conference table workstations are recessed to enhance the participants’ line of sight and to encourage interaction. They communicate over a local area network and run software tools for electronic brainstorming, stakeholder identification and analysis, and issue analysis.12 A lower-cost version of an electronic meeting room is one that, for example, utilizes OptionFinder (an idea generator). William Law, president of Law & Associates, often works with small to medium-sized businesses on strategic plans—that is, he helps them sort through priorities as a prelude to the entire planning process. A sales force might be queried as to the success of a certain product in the field, using criteria developed beforehand, such as reliability, delivery time, technical support, and training. The OptionFinder software package pairs the questions to rank their importance, asking participants whether they value reliability or delivery time more, for example. Participants press numbers on the keypads to record their opinions and the computer analyzes the results, weighting and ranking the criteria. It also shows how the votes were clustered—for example, on which issues most participants agreed. Those can then be put aside so that the discussion can center around where there is disagreement. If there is diversity, it should be discussed, since one person may know something that the others do not. In addition, the system can divide the participants into subgroups, showing how middle managers voted in comparison to senior managers. It should be noted that the keypad system allows for anonymity.13 Electronic Collaborative Creativity From the standpoint of electronic collaboration, Smart Technologies’ Smart Idea 2.0 brainstorming tools marry Internet conferencing with graphical diagramming tools to create highly flexible collaboration software. Smart Ideas is one of a number of new graphical groupware applications, including MindJet’s

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MindManager 3.5 (as noted below), that allows collaboration using Java-based clients linked via TCP/IP. Smart Ideas is one of the better products for larger groups of users because it taps a server to centrally store collaborative data. Available as either a stand-alone Java application or a plug-in to a Web browser, Smart Ideas has a client-user interface that is easy to use. It produces diagrams known as concept maps that divide complex ideas into components and display the relationships between ideas. Each idea can be placed in its own box, and related concepts are linked with lines. Because Smart Ideas’ server stores maps and related documents, participants can work on a project at their convenience or use the server for collaboration. During a conference, changes that other participants have made in the map can be viewed and a record of the session can be made that others can play back to see how the map evolved. MindJet’s MindManager 3.5, however, is a better choice for more controlled collaboration and has a good range of information sharing options. MindManager is capable of producing concept maps to display ideas and the relationships between concepts. It produces lavish illustrated maps and has more controlled conferencing and information publishing options than its rivals. Since it lacks a server, it is more appropriate for small groups. MindManager 3.5 produces highly detailed concept maps with images and symbols. Its wide variety of information publishing options lets users share these maps with other MindManager users, publish maps on Web servers as HTML images, and send them via E-mail as bit maps. Like Smart Ideas, MindManager is available as a stand-alone Java client or as a plug-in to a Web browser. COMMON BARRIERS TO CREATIVE THINKING Barriers to creative thinking can cause a well-designed creativity session to be a waste of time. A company’s most resourceful employees may wind up contributing little or nothing to a company in transition. Common barriers to creative thinking are attitudinal, behavorial, and environmental oversights that often occur during staff meetings, training or idea generation sessions, and in less formal meetings where employees meet over time to solve the company’s problems. Alexander Graham Bell studied the interworkings of the inner ear before designing the first telephone. It struck him that the bones of the ear were large compared to the delicate eardrum that moved them. The thought occurred to him that if a membrane so thin could move such relatively heavy bones, “why should not a thicker and stouter piece of membrane move my piece of steel?” Bell, therefore, derived the solution to his complex electromechanical problem from a seemingly unrelated, anatomical frame of reference. Like Bell, creative individuals possess an uncanny ability to integrate principles and information from a wide variety of sources to solve problems. Unfortunately, these employees are often perceived as being out in left field, disengaged from the topic at hand. Their suggestions are usually discounted

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immediately because they do not seem to fit the discussion’s precise frame of reference. Creative problem solving at its best occurs only when judgment is suspended, at least during the initial stages of the process. New ideas and solutions blossom only when individuals are left unencumbered by practical considerations and habitual lines of corporate thinking. During the initial phase of any problem-solving process, it is critical that employees be allowed to contribute ideas and suggestions without feedback or criticism of any kind. Judgment, disapproving glances, power intrusions, or practical considerations must be suspended until the idea-generating session has concluded. An unusual suggestion from one individual may serve as the springboard for other, more useful perspectives, and this crucial interplay of ideas can occur only if there is an environment devoid of mental obstacles. Another principal reason for suspending judgment of possible solutions is that obvious answers are not necessarily the best answers. There is also need to be wary of solutions that surface quickly. If accepted too readily, these answers may stifle the output of even more rewarding results. All too often, an employee who makes an insightful contribution to the problem under study is assigned the task of implementing the idea. The task is assigned for expediency’s sake and the intent is an understandable one—that is, the group leader wants to have one neatly packaged meeting. For effective problem solving, this approach can be disastrous and represents one of the most common barriers to creativity. The group leader or supervisor who immediately assigns responsibility to the idea generator will never get another good idea from that person. Also, the other participants who were asked to generate ideas will all suddenly develop severe cases of laryngitis or will not be forthcoming with their best ideas. Typically, group participants need to know in advance the exact nature of the idea-generating meeting—that is, what is hoped to be accomplished. The manager should not send a cryptic memorandum the day before and expect an avalanche of ideas. For example, Albert Einstein was unable to formulate his theory of relativity until he stepped away from his equations and blackboards and went home for a good night’s rest. When he awoke the next morning, he vividly remembered an image from a dream in which he was astride a rocket speeding away from earth. The remembrance of the dream, a seemingly irrational occurrence, prompted Einstein to return to his blackboard with the solution to his mathematical puzzle. Another common barrier to creativity is failing to give credit where credit is due. The first time a manager takes a subordinate’s good idea and passes it off as his or her own, company creativity will suffer significantly. Tactics like these tend to be commonplace. The news of this plagiarism spreads quickly via the grapevine. In a few weeks, employees perceive management as a self-serving pack of company wolves. In contrast, a good approach when an employee contributes an idea for the betterment of the company is to have the employee write and sign a memorandum explaining the new approach. The individual should

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be assured that the memo will reach the right person and that a report will be sent back as soon as possible with a response. If the idea is incorporated as a new policy or procedure, the employee should be recognized at a formal awards ceremony or luncheon. The award should also be announced in the company newsletter. Rewarding creativity is a wise investment that will pay high company dividends for many years.14 Additional common barriers to creativity include emotional blocks. These center around a fear of making mistakes or taking risks, a desire for security and order, a preference for judging ideas rather than generating them, the desire to succeed quickly, a lack of control over one’s imagination, and an inability to distinguish reality from fantasy. Another barrier appears when managers undermine employees’ creativity by continually changing goals and interfering with a company’s priorities and its mode of operation. In some situations, new ideas are met with an open mind but with time-consuming layers of evaluation. Also, deciding how much time and money to spend on a team or project is a judgment call that can either support or kill creativity. In today’s global-driven economy, companies that employ managerial practices that kill creativity may well find it difficult to survive in the short to long run. It is far better for a company to let its personnel decide how to achieve individual goals (versus corporate goals) using the tools, methods, and procedures they know best.15 A CHECKLIST OF TRAITS FOUND IN A CREATIVE MANAGER To assist the reader in getting a handle on attributes of a creative manager, a checklist of these important traits are found in Figure 2.1. This checklist of behavioral and personality traits offers important insights into the makeup of a creative manager. It can also be used by the reader to see where improvements can be made to on-the-job and off-the-job behavior. A thorough understanding of this checklist indicates that a creative manager is restless—that is, he or she is irritated by the status quo and refuses to be restricted by habit and the environment. Creative managers are looking for ways to improve not only their own performance but also the performance of the people around them. There is a relentless search for reaching important short- to long-range goals. It should be noted that the typical creative manager does not possess each and every one of these attributes, since people do vary in terms of behavioral and personality traits. However, a person must have a sufficient number of these traits to qualify him or her as being creative. A QUIZ TO ASSESS THE MANAGER’S CREATIVITY An approach to determine where managers are in terms of their creativity is to use a quiz. The author and a colleague (Dr. Robert C. Klekamp) developed such a quiz several years ago which is set forth below.16 For the most part,

Figure 2.1 A Checklist of Important Traits Found in a Creative Manager

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Figure 2.1 (continued)

many of the statements found in the quiz are based upon the prior discussion on the characteristics of a creative manager. Creativity Quiz In this quiz, there are 20 statements to test one’s level of creativity. The reader should circle the appropriate answer based upon his or her mode of operation. 1. When under stress, you trust what has worked in the past rather than experiment with new ideas. a. Very seldom. b. Once in a while. c. Occasionally. d. Fairly often. e. Often 2. Many of your successful strategies resulted from playing around on paper with the components of a problem. a. Very seldom. b. Once in a while. c. Occasionally. d. Fairly often. e. Often. 3. You have brainstorming sessions with others. a. Almost never. b. Once in a while. c. More often than not. d. Fairly often. e. Frequently.

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4. You leave a present problem that stumps you in order to face an earlier problem that remains unresolved. a. Never. b. Seldom. c. Occasionally. d. Often. e. Always. 5. When faced with an important issue, believing you have made the right decision is more important to you than winning over the opinions of peers and bosses. a. Strongly disagree. b. Mildly disagree. c. Indifferent. d. Agree. e. Absolutely agree. 6. You use scraps of time, such as in waiting rooms and at lunch, to work on simple problems. a. Never. b. Seldom. c. Occasionally. d. Often. e. Always. 7. You list ideas as they occur to you for possible future thought. a. Very seldom. b. Once in a while. c. Occasionally. d. Fairly often. e. Often. 8. To assure rational decision making, you disregard your sense of intuition. a. Almost never. b. More often than not. c. Unaware of intuition. d. Often. e. Always. 9. You turn to publications within your field to help you identify new solutions. a. Usually not. b. Occasionally. c. More often than not.

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10. You use others as sounding boards to separate the important from the unimportant. a. Not at all. b. Comparatively little. c. Occasionally. d. More often than not. e. Fairly often. 11. You count on successful ideas rather than unsuccessful ones to lead you to more promising ideas. a. Once in a while b. Doesn’t matter. c. Often. d. Very often. e. Always. 12. You devote as much effort to defining the problem as to determining its solution. a. Never. b. Seldom. c. Occasionally. d. Often. e. Always. 13. You schedule your projects according to what comes to you. a. Never. b. Seldom. c. Occasionally. d. Often. e. Always. 14. You permit all subordinates to use their own judgment in solving problems. a. Seldom. b. Sometimes. c. More often than not. d. Often. e. Always. 15. Concerned about motivation, you praise good performance rather than admonish poor performance. a. Seldom. b. Occasionally. c. You comment on neither.

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d. Often. e. Always. 16. You prefer quarterly performance appraisals rather than annual ones. a. Strongly disagree. b. Mildly disagree. c. Indifferent. d. Agree. e. Strongly agree. 17. The best-dressed and best-mannered subordinates generally have the most success in completing projects. a. Once in a while. b. More often than not. c. Often. d. A great deal. e. Consistently. 18. You have found that the presence of external consultants diminishes your managerial aura. a. Strongly disagree. b. Mildly disagree. c. Indifferent. d. Agree. e. Strongly agree. 19. Your job gives you a real sense of achievement. a. Never. b. Seldom. c. Occasionally. d. Fairly often. e. Often. 20. If you had a good friend looking for a managerial position, you would recommend the company for which you work. a. Never. b. Seldom. c. Occasionally. d. Fairly often. e. Often.

Upon completion of this quiz, proceed to the Appendix at the end of this chapter and score your answers. Next, add up the results for the 20 statements. To determine your level of creativity refer to the grading scale below:

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Category 1, 87–100—Creative Manager of the Future Category 2, 73–86—Manager with Definite Creative Potential Category 3, 59–72—Status Quo Manager Likely to Be in Same Position Three Years from Now Category 4, 45–58—Lucky to Make It Through Next Year as a Successful Manager Category 5, 31–44—Consider a Non-Managerial Position or Learn a Trade

Research Results of Creativity Quiz This quiz was administered to five graduate-level classes at Xavier University. The results are as follows: Category

Number of Graduate Students

1

0

0

2

14

15.5

3

67

74.5

4

9

10.0

5

Percent

0

0

90

100%

The research results indicate that there is room for improvement. Essentially, this is an important part of this text—that is, to improve one’s creative skills by utilizing the latest creative computer software. Not only will this software assist to improve strategic thinking and decision making at the top-management levels, but it will also assist those in lower- and middle-level management. In addition, operating personnel who perform managerial activities as part of their jobs can benefit from using creative computer software. REFLECTIONS ON A CREATIVE MANAGER WITHIN A BIS ENVIRONMENT Within a BIS environment, creative managers do not know the meaning of an eight-hour business workday. Their preoccupation with problems is typically incessant. Creativity, in whatever field, is generated by hard thinking and prolonged reflection over problems. Frequently, an intense conscious struggle with problems is useless. But these efforts, futile as they seem to be, are not necessarily wasted because they activate the subconscious processes of cerebration and incubation. Without preparatory work, the subconscious can be unproductive. Essentially, creation is a product of hard thinking, prolonged reflection, and subconscious incubation. There is a continuous assimilation of data and observations, a continual pondering about the causes of regularly met difficulties, and

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a sorting out of hunches and ideas that are a part of consciousness. Creative managers develop a retrospective awareness of when they have solved problems creatively. They take note of the methods that have succeeded and failed. They try to learn “why” by retracing as far as possible the routes followed and those avoided. Creative managers schedule their creative thinking periods for times when they have their most favorable mental set for producing ideas. They are aware of their personal rhythms of output. By keeping a record of the most creative periods during a day, they can establish a pattern and plan ahead, reserving peak periods for concentration, contemplation, and unihibited thinking, and using the less productive times for reading or routine tasks. But even without a time sheet of productive periods, the creative manager develops a sensitivity to moods that promise good returns and knows when those periods are approaching. SUMMARY A creative manager may act in an unplanned or nonscheduled manner. However, using the individual’s creative process, far out ideas are transformed into the realities that power the company into the future. The individual is concerned with bringing products and services to market before the customer is aware of a need for them. From this perspective, creativity can be used to gain and improve a company’s long-term competitive advantage. Sample creative computer software packages as well as other approaches to creativity were set forth to assist in developing new products and services. Finally, a quiz was given that allows the reader to assess his or her level of creativity on the job. APPENDIX: ANSWERS TO CREATIVITY QUIZ 1. a. Very seldom—5 points. b. Once in a while—4 points. c. Occasionally—3 points. d. Fairly often—2 points. e. Often—1 point. No matter what the stress, your old ideas are likely to perpetuate the past and permit the situation to recur. 2. a. Very seldom—1 point. b. Once in a while—2 points. c. Occasionally—3 points. d. Fairly often—4 points. e. Often—5 points. Moving ideas around on paper often is a successful method of triggering creativity.

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3. a. Almost never—1 point. b. Once in a while—2 points. c. More often than not—3 points. d. Fairly often—4 points. e. Frequently—5 points. Brainstorming is another successful creative trigger. 4. a. Never—1 point. b. Seldom—2 points. c. Occasionally—3 points. d. Often—4 points. e. Always—5 points. Theoretically, our most creative moments come after periods of ego regression such as sleep or alternate stimuli. 5. a. Strongly disagree—5 points. b. Mildly disagree—4 points. c. Indifferent—3 points. d. Agree—2 points. e. Absolutely agree—1 point. Creative ideas are worth precious little to a manager’s success unless the ideas can be implemented. 6. a. Never—5 points. b. Seldom—4 points. c. Occasionally—3 points. d. Often—2 points. e. Always—1 point. These times have high creative potential, so use them for complex kinds of problems. 7. a. Very seldom—1 point. b. Once in a while—2 points. c. Occasionally—3 points. d. Fairly often—4 points. e. Often—5 points. List and also rank your ideas according to their practicality and economic feasibility. 8. a. Almost never—5 points. b. More often than not—4 points. c. Unaware of intuition—3 points. d. Often—2 points.

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e. Always—1 point. Rational thinking deals with what is, but a creative manager deals with what might be. 9. a. Usually not—5 points. b. Occasionally—4 points. c. More often than not—3 points. d. Often—2 points. e. Entirely—1 point. If you limit yourself to your own field, your managerial style will be dictated by your own competitors. 10. a. Not at all—1 point. b. Comparatively little—2 points. c. Occasionally—3 points. d. More often than not—4 points. e. Fairly often—5 points. It’s important to you to find out what’s important to the rest of your organization. 11. a. Once in a while—5 points. b. Doesn’t matter—4 points. c. Often—3 points. d. Very often—2 points. e. Always—1 point. Unsuccessful ideas can become successful if saved until the right situation and right time. 12. a. Never—1 point. b. Seldom—2 points. c. Occasionally—3 points. d. Often—4 points. e. Always—5 points. You are better off having the wrong answer to the right problem than the right answer to the wrong problem. 13. a. Never—5 points. b. Seldom—4 points. c. Occasionally—3 points. d. Often—2 points. e. Always—1 point. Plan and assign priorities to your projects, your job. 14. a. Seldom—5 points. b. Sometimes—4 points.

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Improving Decision-Making Effectiveness c. More often than not—3 points. d. Often—2 points. e. Always—1 point. Allowing creative freedom among your subordinates is admirable, provided you are sure your staff does the right jobs in the right ways.

15. a. Seldom—5 points. b. Occasionally—4 points. c. You comment on neither—3 points. d. Often—2 points. e. Always—1 point. People have a right to know exactly where they stand. 16. a. Strongly disagree—1 point. b. Mildly disagree—2 points. c. Indifferent—3 points. d. Agree—4 points. e. Strongly agree—5 points. Annual feedback is too infrequent to modify behavior over the long term. 17. a. Once in a while—5 points. b. More often than not—4 points. c. Often—3 points. d. A great deal—2 points. e. Consistently—1 point. Your job is to manage productivity, not fashion and etiquette. 18. a. Strongly disagree—5 points. b. Mildly disagree—4 points. c. Indifferent—3 points. d. Agree—2 points. e. Strongly agree—1 point. To build a career on managerial ego is to build on sand. 19. a. Never—1 point. b. Seldom—2 points. c. Occasionally—3 points. d. Fairly often—4 points. e. Often—5 points. If not, there’s a good probability that, over time, you will not perform it well. 20. a. Never—1 point. b. Seldom—2 points. c. Occasionally—3 points.

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d. Fairly often—4 points. e. Often—5 points. If you wouldn’t recommend your firm to a friend, better ask yourself why you’re there.

NOTES 1. Edward de Bono, “Why CEOs Are Not Creative,” Chief Executive, July–August 1991, p. 37. 2. Frank Rose, “A New Age for Business?” Fortune, October 8, 1990, pp. 156–164. 3. S. J. Parnes, R. B. Noller, and A. M. Biondi, Guide to Creative Action (New York: Scribner, 1977), p. 14. 4. C. S. Whiting, Creative Thinking (New York: Reinhold, 1958), p. 2. 5. R. L. Ackoff and E. Vergara, “Creativity in Problem Solving and Planning: A Review,” European Journal of Operations Research, vol. 7, 1981, p. 9. 6. Graham Wallas, The Art of Thought (New York: Harcourt, Brace and Company, 1926). 7. W. Gordon, Synectics: The Development of Creative Capacity (New York: Harper & Row, 1961). 8. Ed Brown, “A Day at Innovation U.,” Fortune, April 12, 1999, pp. 163–165. 9. Amy Saltzman and Edward C. Baig, “Plugging in to ‘Creativity’,” U.S. News & World Report, October 29, 1990, pp. 96–97. 10. Ibid., p. 97. 11. Robert J. Thierauf, Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1993). 12. J. F. Nunamker, Jr., Alan R. Dennis, Joseph S. Valacich, Douglas R. Vogel, and Joey F. George, “Electronic Meeting Systems to Support Group Work,” Communications of the ACM, July 1991, pp. 40–61. 13. Gigi Verna, “PCs Powerful Tools for More Productive Meetings,” The Greater Cincinnati Business Record, A Supplement, April 29–May 5, 1991, pp. 1B–2B. 14. Richard Lambardo, “Breaking the Barriers to Corporate Creativity,” Training and Development Journal, August 1988, pp. 63–65. 15. Teresa M. Amobile, “How to Kill Creativity,” Harvard Business Review, September–October, 1998, pp. 77–87. 16. Robert C. Klekamp and Robert J. Thierauf, “On Being Boss,” The Cincinnati Enquirer Magazine, April 4, 1982, pp. 31–33.

PART II Underlying Structure of Effective Business Intelligence Systems

3 Effective Decision Making in a Business Intelligence Environment NEED TO RETHINK DECISION MAKING AS A WAY OF CAPITALIZING ON BUSINESS INTELLIGENCE Today’s worldwide marketplace provides not only more customers, suppliers, and competitors but also increased complexity for the decision-making process. The speed of communications simultaneously makes the environment less stable and predictable and reduces the available time for examining business information, knowledge, and intelligence. As a result, appropriate analyses are unavailable to decision makers or the requisite analyses are infeasible. Not surprisingly, decision makers are increasingly dissatisfied with established procedures for making decisions. This does not mean that the use of models or the use of decision support systems should be abandoned. But rather, both should blend analytical tools with intuitive heuristics to improve managers’ insights about factors too complex to build into models. In particular, business intelligence systems should facilitate the modification of results of analytical tools when they contradict intuition. Alternatively, analytical tools can test and verify intuition before applying it to the decision-making process. The bottom line is that the combination of analytical tools and human interaction provides insight for a company’s decision makers into problems and their related opportunities. Going beyond the utilization of analytical models and human interaction, decision makers are making decisions that combine some goal with predictive models. Deciding that prices of certain products need to be increased is the result of a goal to maximize sales and profits, along with a predictive model that relates to product price. Without the goal of maximizing sales, for example, there is no correct decision concerning product pricing. And without a predictive model equating product prices with product sales, there is generally no way to

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know which decisions will likely maximize sales. This current state of decision making is evolving into a newer form that is needed to reap the real benefits of business intelligence. DIFFERENT LEVELS OF BUSINESS INTELLIGENCE The focus of this text is on a broad framework for acquiring, storing, disseminating, and, in general, managing intelligence within a BIS environment. Essentially, business intelligence centers on computerized methods and processes to improve strategic, tactical, and operational (including financial) decisions using data, information, and knowledge from multiple sources as well as applying experience and assumptions to develop an accurate understanding of the dynamics surrounding decision making. To assist operating managers and their support staffs at different levels of an organization during the coming days, weeks, and months, operational intelligence is employed for decision making. At the next level for lower- and middle-level managers and their staffs, tactical intelligence is useful in decision making for overseeing the overall performance of their functional areas during the coming year. At the highest level, strategic intelligence is useful for decision making by top-level managers and their staffs for combining pertinent external with internal data, information, and knowledge for future periods, say from two to five years and beyond, to accomplish an organization’s strategic plans as related to its objectives and goals. All three of these levels are related to financial intelligence, which provides a basis for a thorough understanding of where the company stands today and of projections into the future. For the most part, this breakdown for decision making is similar to that for all types of information systems, including decision support systems, executive information systems, on-line analytical processing systems, and knowledge management systems. Due to the importance of intelligence levels for decision making, they are discussed further in Part IV of this text—Chapters 7 through 10. A thorough analysis of these levels of business intelligence indicates that they overlap, thereby forming a continuum that underlies financial intelligence. That is, the sources that center on operational control are based on those within the organization, whereas the sources that revolve around strategic planning are based largely on those outside the organization. In the middle, sources for tactical planning and control are somewhat balanced between external and internal ones. The scope of intelligence is somewhat narrow and well defined for operational control, very wide for strategic planning, and in between for tactical planning and control. The level of aggregation is very detailed for operational, moderately detailed and somewhat summarized for tactical planning and control, and very summarized for strategic planning. In terms of time horizon, operational control is historical in nature, including some projections; strategic planning relies heavily on futuristic patterns and trends; and the middle of the two makes use of both historical and futuristic projections. The other characteristics

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of business intelligence, including accuracy, currency, and frequency of use, can be interpreted in a similar manner. NEWER DIRECTIONS TO FACILITATE DECISION MAKING Building upon the foundations of systems set forth in Chapter 1, newer business intelligence systems are making use of self-modifying systems that continuously monitor the real world to see if it behaves as predicted, and when it does not, the predictive models used to make rules are changed. In this enlarged approach to improve decision making, these self-modifying systems could try out different scenarios or predictive models could analyze how well the systems would have fared under different scenarios. In essence, there is a need for a BIS environment in which decisions are based on multiple predictive models with complex measures of uncertainty and where the goals themselves are variable. Such an operating mode would be based on utilizing decision-analysis software. Fortunately, there are several vendors in this area of decision analysis. They include Lumina Decision Systems, Logical Decisions, Strategic Decisions Group, Applied Decision Analysis, and Decisioneering. Some of these companies focus more on the challenge of working through a system of multiple predictive models, using such techniques as decision trees and sequential diagrams. Other companies focus more on resolving multiple conflicting goals. By bringing together the combination of decision-analysis software, groupware, group decision support systems with data mining, OLAP, knowledge management, and visualization systems (as discussed in the next chapter), decision makers can utilize analyses that result in accurate, consistent, and timely decisions over the short to long term. Decision Processing From a different perspective, there are vendors that permit transactionalprocessing systems to coexist and work together with decision-processing systems. Among these are BAAN, PeopleSoft, Oracle, and SAP. As data flows from transactional-processing systems to decision-processing applications that are distributed to business users, its information content improves both in quality and accuracy and also in business value. Within this framework, the concept of decision processing comes into play for managing and coordinating the various components of decision making. For an overview of a decision-processing system, reference can be made to Figure 3.1. Essentially, a decision-processing system centers on the acquisition, management, and distribution of business information, knowledge, and intelligence to facilitate a better understanding of a company’s operations. At the starting end, extraction, transformation, and load (ETL) applications and tools are used to acquire data from transactional-processing systems as well as clean, transport, and load into a data mart or a data warehouse. In the middle, the manage task

Figure 3.1 A Framework for a Decision-Processing System

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maintains business information, knowledge, and intelligence and provides appropriate access. The end task of distribute consists of using appropriate access tools (i.e., query reporting, OLAP, data mining tools, including business intelligence tools, and delivery tools) for supplying desired output to decision makers. An integral part of a typical decision-processing system is the utilization of enterprise information portals, as shown in Figure 3.1. EIPs offer users a simple and personalized Web interface to business information, knowledge, and intelligence scattered across networked enterprise systems. They begin to add value to a company when they support access to a decision-processing system. These decision-processing portals (examples include VIT’s SeeChain and Plumtree Software’s Corporate Portal) give users access to what is needed for their role in the organization. This may involve giving executives high-level key performance indicators about business operations or helping business analysts and line managers navigate their way through a decision-processing system from highly summarized information down to very detailed data. Many decision-processing portals are packaged with business intelligence tools that work cooperatively with the portal to help users access a data mart or a data warehouse and create, manage, and run decision-processing objects such as queries, reports, and analyses. The results of running the decision-processing objects are also managed and accessed via the portal. These decision-processing portals offer more than simply a Web browser front end to a business intelligence tool and associated underlying data marts and data warehouses. Several products also allow business information from collaborative and office systems to be integrated into, and accessed by, the portal. For example, a marketing analyst could use such a portal to access and manage all the business information, knowledge, and intelligence to mount a new marketing campaign, including customer profitability analyses, promotion and campaign analyses, channel comparison analyses, customer segmentation and behavior analyses, and sales force analyses. As the campaign progresses, the analyst can integrate additional decisions documented in office documents, Email, and so on into the portal. In the future, the individual can return to the portal and examine why particular decisions were made and determine the impact they had on the marketing program.1 Collaborative Processing The most basic form of the enterprise information portal is the intranet portal which provides general company information and includes links to important information and Web sites both within and outside the organization. This type of portal is analogous to sites such as Yahoo! on the public Internet. From the intranet portal has evolved the collaborative portal, which adds personalization capabilities and the ability to track and share workgroup information, such as office documents and E-mail. This functionality is similar to that offered by a

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My Yahoo page. In the future, it is expected that vendors will integrate this type of portal into workgroup systems, such as Lotus Notes and Microsoft Exchange. The support for collaborative processing in a decision-processing portal is essential for tracking and integrating business information, knowledge, and intelligence when closing the loop between decision processing and transactional processing. The ability to track the lineage of business information, knowledge, and intelligence with associated business decisions gives decision makers the ability to retroactively discover why a particular decision was made and gauge its impact on the business. Consequently, many portal-enabled business intelligence tool and analytical application vendors are adding collaborative capabilities to their products. Currently, the E-business application is now rapidly appearing in many organizations, and it must integrate into the decisionprocessing environment as well. Basically, E-business applications act as an additional data source for a data mart or a data warehouse. The feedback to an E-business application from the decision-processing environment, however, may need to occur more rapidly (or maybe in real time) than a back- or front-office transactional-processing application because the analytical results from the decision-processing environment may be used to control the interaction (the sequence of Web pages displayed, for example) between the E-business application and the user. In the future, organizations may use intelligent E-business portals to automate the feedback mechanism from the decision-processing environment to an E-business application based on business rules defined to the portal. Overall, newer collaborative-enabling technologies—from multi-user, multisensory virtual reality environments to 3-D data streaming across the Internet— promise to reshape collaborative processes over the long term. However, in the short term, decision-processing portals are evolving to handle E-business environments by supporting external users and external business information accessible via corporate extranets and the public Internet. In addition, such portals let users switch seamlessly from a decision-processing system to the E-business environment. For example, an insurance company could provide analytical and other business information about insurance claims to its key customers. A business user at the customer’s location could use the insurance company portal to access and analyze this claim information and then switch to an E-business application to make any adjustments to the customer’s insurance coverage. Such an effective customer and insurance company collaborative effort could help ensure a continued, successful business relationship over time.2 TYPES OF PROBLEMS THAT CAN USE BUSINESS INTELLIGENCE IN THEIR SOLUTION Within a BIS operating mode, well-structured, semi-structured, and unstructured problems are capable of being solved. However, knowledge and intelli-

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gence tends to be more useful with the second two problem types, while information is generally sufficient for well-structured problems. A problem is said to be well-structured if all of its elements can be identified and quantified in order to determine an answer. Typically, the time frame is of short duration, say up to one year. For example, in a production allocation problem, the time available this month and next month on the first and second shifts as well as the costs to produce the products in the manufacturing departments can be identified. Also, the level of production is known based upon the forecasted sales for these months. Thus, the problem is well defined and can be solved within the information parameters set forth using a quantitative approach to decision making. Since the problem is fully structured, generally an appropriate mathematical or statistical model can be utilized to reach a good solution. A problem is said to be semi-structured if it contains both well-structured and unstructured elements. The time frame can range from the short run to the long run. For example, an investment problem to determine a specific portfolio is considered to be semi-structured. From one viewpoint, a systematic search through data and information on portfolios and securities is required; they can be effected through retrieval, reports, and display via a PC or workstation using mathematical and statistical analytical models. At the same time, the criteria for making intelligent investments based upon past information and investment knowledge and intelligence gained over time need to be left to the manager’s judgment. Thus, output from the computer is combined with the portfolio manager’s judgment to select appropriate securities to solve the investment problem. If the significant parameters of the problem cannot be identified precisely, it is said to be unstructured, since human intuition and judgment are generally needed to reach a decision. Typically, the rationale for the inability to identify specific parameters in the problem is that the time frame is too long, say beyond five years. For example, consider the problem of determining a company’s personnel needs 10 years hence. Because there are a large number of unknowns relating to sales and production, the net result is that the appropriate level of personnel to support these areas is also unknown. In effect, the parameters of the problem are too loosely defined to solve it with a high degree of accuracy. If the problem is unstructured from the user’s perspective, computerized mathematical or statistical models are generally inappropriate. To reach a decision, there is a need for general knowledge as well as meaningful experience, knowhow, intuition, judgment, and past experience. This may mean taking a qualitative approach to decision making. However, rules of thumb (i.e., heuristic methods) may be appropriate to resolve unstructured problems. This may require resorting to trends, patterns, evaluations, educated guesses, hypotheses, and the like. Essentially, it is problem solving under conditions of uncertainty. The attendant circumstances must be surveyed to determine whether or not heuristic methods are appropriate for solving unstructured problems.

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A Problem Well Defined Is a Problem Half Solved Whether problems are well-structured, semi-structured, or unstructured, they are much easier to solve when they are fully defined. As the old saying goes: “A problem well defined is a problem half solved.” The problem can refer to any functional area of a company, such as marketing, manufacturing, or finance. When a project in any of these functional areas, for example, is well defined, (i.e., the business issues are well defined), only then is a solution possible to meet the desired objectives. In addition, technology is effective only when it is an appropriate solution to a company’s business needs. To elaborate further on the proper development of a project, such as a business intelligence system, the information systems department tends to be secretive about its needs and its users, feeling that the company’s competitors will get wind of what is happening internally. Unfortunately, these computer personnel fail to realize that if they offer the proper information about the project up front, it means that they have defined their essential needs. An astute vendor can help a company determine its needs, but it is best when the company itself focuses first on its own business requirements. An effective computer sales representative is not just selling but also determining whether it is worth the time and effort to pursue the current business proposal. Both parties can waste a lot of time if they do not determine up front that they can work well together. And it is especially troubling when the company buys a system and then finds out that the system just does not satisfy its requirements. Solving a problem or working on a project can be only successful when parties come together and work as business partners to produce the desired results. This is particularly crucial at the outset so that what needs to be defined initially is undertaken and there are no hidden surprises later on. In this manner, the right hand knows what the left hand is doing.

PROBLEM FINDING VERSUS PROBLEM SOLVING WITHIN A BIS ENVIRONMENT An important first factor in an effective BIS environment is one that focuses on an overview of a company and its operations. Such an approach is found with the utilization of problem finding and its related techniques to get a handle on present and future problems, and identify future opportunities. Although problems range from being structured to unstructured, it is helpful for company managers to take a broader-based view by distinguishing between a reactive and a proactive approach to resolving them. A survey of many companies today indicates that many managers spend more time putting out fires and less time in helping their business grow. Typically, a company has taken a reactive approach to its problems when they try to apply appropriate management techniques in order to resolve current problems as they arise. In reality, several more

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problems tend to crop up that require the attention of management. This neverending scenario of fighting problem fires never seems to get under control. In contrast, a better way is to take a proactive, preventive approach that keeps managers on top of problems confronting them. Generally, these problems were always there but management either ignored them hoping that they would go away or assigned them to others who did not have the time or clout to get them resolved. Hence, the preferred approach today is to have managers get involved in problem finding versus just problem solving. The problem-finding process not only includes anticipating future company problems and bringing them back to the present time for solution but also looking for future opportunities that are related to these future problems. In the area of marketing, for example, problem finding includes knowing what customers want before they know themselves versus just meeting the competition. Similarly, in the area of finance, this means that risks can be managed with foresight versus damage being controlled through hindsight. This proactive management approach to a company’s problems and its related opportunities is an important prerequisite for improving a company’s competitiveness in today’s fast-changing times. Also, the problem-finding approach can be tied in with a company’s critical success factors—that is, upcoming problems and opportunities that impact a company can be related to those factors that are critical to a company’s survival. Overall, a problem-finding approach is related to the old saying: “A danger foreseen is half avoided” (Thomas Fuller, M.D., 1732). To relate problem finding to data, information, and knowledge that leads to an understanding (i.e., intelligence), reference can be made to Figure 3.2, where management effectiveness is shown as it ties in with a basis for decision making. As illustrated, management is ineffective in the lower-left-hand corner where management spends its time putting out problem fires as they occur. In contrast, management is highly effective using knowledge and intelligence in a proactive mode, as shown in the upper-right-hand corner. Right in the middle is a somewhat effective approach. Adopting a proactive management approach that operates on knowledge, intelligence and ideas, and creating an organization that encourages people to engage in problem finding, represents an important challenge for the typical company. Hence, there is a need for decision making that utilizes appropriate analyses to understand a company’s operations in order to shape its future outcomes. Such systems are found within a BIS operating mode, which holds the promise of increasing organizational effectiveness to meet the challenge of changing times. Creativity Is Essential for Effective Problem Solving and Problem Finding In the problem-solving process, the focus is on solving problems using the proper tools for their solution. Most decision makers have used this process on the job over the years. On the other hand, the problem-finding process, as dis-

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Figure 3.2 Management Effectiveness in a Proactive Mode versus a Reactive Mode as Related to a Basis for Decision Making Using Data, Information, and Knowledge (i.e., Intelligence) for Understanding a Company’s Operations Thoroughly

cussed later in the chapter, focuses initially on information useful in getting at a company’s future problems and related opportunities. But more importantly, the focus then shifts to knowledge that is useful in exploring ideas to solve future problems studied as well as new ideas that can enhance further important opportunities for a company. It should be noted that a thorough understanding of information and knowledge leads to business intelligence by decision makers. This intelligence gives decision makers the ability to learn, interpret, and deal with new and different situations. This enlightened point of view displaces the prior belief that more information will solve a company’s problems. There was a time when information was indeed the limiting factor, and more information made for better decisions. Generally, this is no longer the case—that is, knowledge underlies new concepts and ideas, and detailed analysis leads to a better understanding (i.e., intelligence) of these concepts and ideas. Typically, managers need to study concepts and ideas on their own as opposed to the current emphasis on problem solving. The emphasis should be on enhancing the manager’s creativity in terms of developing new concepts and ideas based on business data, information, and knowledge of a company’s overall operations that is thoroughly understood. Such is the approach found in the problem-centered and opportunity-centered approaches presented later in the chapter.

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Finding the “Right” Problems and Related Opportunities To find the “right” problem, the decision maker must consider the what, where, when, who, and how of the problem. That is, the individual must consider the facts and figures underlying the problem. A thorough understanding of these five items leads the decision maker to develop an objective solution that is used to solve the problem. This is where the problem-solving process or the problemfinding process comes into play to resolve the problem. As noted previously, these problems have related opportunities that should be evaluated by decision makers. It is quite possible that these related opportunities can make or break the organization over the long term. In the evolution process of the what, where, when, who, and how, every business organization has three ongoing basic problems that can be framed in terms of questions. First, how can the company add new and distinctive value for its customers? This includes better ways for customers to use current and proposed products and services that will benefit them directly. Second, how can the company sell more of its products and services today and tomorrow that actually help its customers do a better job to meet their needs? Third, how can the company reduce the cost and complexity of its products and services so that cost savings can be passed along to its customers? Answers to these questions will help a company find problems that are worth solving and may, in many cases, lead to important product and service opportunities for the company to follow today and tomorrow. Overall, finding the right problems and related opportunities should help a company to create new and productive relationships with its customers, trading partners, company personnel, company union members, financial institutions, the community, and investors. Employment of Groupware by Business Teams Related to the newer directions to facilitate decision making is the increasing emphasis on a team approach, which is actually based on the old concept that several heads are better than one. Instead of spending money on problems or forming committees to talk about them, companies are sending out teams to find solutions. Generally, while self-directed work teams are getting things done on a day-to-day basis, cross-functional teams are reaching into all parts of an enterprise to address problems, implement plans, and make necessary changes. Teams are currently the most rapidly expanding approach for involving employees in the effort to improve business results in situations where the use of business intelligence is required. Where applicable, groupware, such as Lotus Notes, can be used to keep team members apprised of results when they are not physically present. It should be noted that newer groupware systems are entering the next generation with Internet features that free users from proprietary platforms and protocols.

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A typical business team has six to 20 members. Successful ones have the right chemistry, a mix of problem-oriented, analytically oriented, and actionoriented individuals. Before seeking solutions, these teams first identify the problem, rigorously gathering the facts. This includes assessing pertinent information and knowledge using a type of groupware system commonly referred to as collaboratory computing in order to provide a better understanding of the problem being studied. It also includes grasping the problem’s dimensions. At times, this process dictates the solution, pinpointing obstacles that must be removed. Where improvements, changes, and breakthroughs are called for, the collective energy and creativity of team members comes to the fore. Overall, enterprise network computing systems, as well as intranets, extranets, and the Internet, can greatly assist business teams in accomplishing their assigned tasks.

TWO APPROACHES TO THE PROBLEM-SOLVING PROCESS In a survey of the current literature in many disciplines, a large number of approaches to solving problems can be found. Rather than try to explore and compare most of them, two approaches that are germane to the problem-solving process will be examined. The first is the quantitative-centered approach, which is oriented more toward solving well-structured problems facing managers and which is a variation of the scientific method. The accent is on using mathematical models that optimize performance (maximize profits, minimize costs, or some other criterion) for one or more functional areas of an organization. In contrast, the second is the decision-centered approach, which is oriented toward solving semi-structured and unstructured problems. The accent is on finding a limited number of acceptable solutions to the problem under study versus many solutions. In turn, the solution is selected from this small number.

Quantitative-Centered Approach The quantitative-centered approach to the problem-solving process is an extension of the scientific method mentioned above. The scientific method was originally formulated by Francis Bacon in the sixteenth century and elaborated by John Stuart Mill in the nineteenth century.3 Its traditional steps have been altered to accommodate the current business environment—that is, the establishment of proper controls over the final solution. As shown in Figure 3.3, the approach consists of the following six steps.4 1. Observation. In the first step, there is a thorough observation of the phenomena surrounding the problem—the facts, opinions, symptoms, and so on. Observation may be a casual glance or a concentrated, detailed, and lengthy study, depending on the requirements of the problem. Observation is used to identify problems. The capable manager is always alert and sensitive to the

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Figure 3.3 A Comparison of Steps in the Problem-Solving Process: The QuantitativeCentered Approach and the Decision-Centered Approach

Note: There is Feedback from the last step of both approaches to the first step.

presence of problems. The individual must be certain that the basic or real problem has been identified, not just the symptoms of it. 2. Definition of the Real Problem. The real problem that is impeding the accomplishment of one or more desired objectives is defined in the second step. To do so, the manager should gain a deeper understanding by discussing the matter with knowledgeable people. Because defining the real problem can be a difficult task, the manager must investigate as broadly as possible the factors surrounding the problem. A thorough analysis of all the factors in collaboration with the appropriate parties should lead to a definition of the real problem. 3. Development of Alternative Solutions. In the third step, alternative courses of action or tentative solutions to the real problem are developed. The alternative courses of action can take the form of quantitative models that can be developed to accommodate the real-world problem. They are generally computer oriented for a final solution. As each model is developed, deficiences may become apparent if the model’s behavior is inconsistent with that of the modeled problem. Certain models that look promising at the outset may have to be discarded.

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Instead of a half-dozen models, the choice might be narrowed to one, two, or three candidates. 4. Selection of Optimum Solution. The fourth step centers on the alternative quantitative models or tentative solutions that remain, which are evaluated in order to select the optimum one. If one fits, a solution may be obtained by using one of the standard quantitative models. If the mathematical relationships of the model are too complex for the standard techniques, a custom-made quantitative model is required. Thus, the selection of the appropriate model using experimentation is dependent on the problem’s nature and complexity. Where deemed appropriate, sensitivity analysis can be employed to select the optimum solution. Sensitivity analysis is a way of observing output changes while varying inputs to determine their relative impact on the optimum solution. 5. Verification of Optimum Solution. Verification involves most or all of the largest population (as defined in statistics) for the fifth step. Implementation is necessary because reaction of competitors, consumer buying habits, and comparable factors observed in the limited sample during the development of alternative courses of action (and the selection of the optimum solution) may not hold true for the target population. To verify the optimum model or solution, it must be translated into a set of operating procedures capable of being understood and applied by the personnel who will be responsible for their use. Major or minor changes must be specified and implemented. 6. Establishment of Proper Controls. Once action has been recommended and implemented, and the results have been interpreted, the sixth and final step establishes controls over the solution. A solution remains an optimum one as long as the factors retain their original relationships. The solution goes out of control when the factors and/or more of the relationships change significantly. The importance of the change depends on the cost of changing the present solution versus the deviation under the changed conditions from the true optimum solution. For effective control over the model (solution), it is necessary to establish a monitoring system, preferably as part of a BIS operating mode. This will permit feedback to the various managers who are responsible and accountable. Continuous monitoring through feedback provides a means for modifying the solution as external and internal conditions and demands change over time. The foregoing steps are seldom, if ever, conducted in a particular order, since there is usually a constant interplay among the steps. However, they provide a conceptual framework for the quantitative-centered approach to problem solving. Typical Applications of the Quantitative-Centered Approach The reader should have no difficulty in applying the quantitative-centered approach to well-structured problems, since this approach is widely used. For example, imagine it is necessary to allocate production facilities to the company’s products on a least-cost basis. This problem is solvable by utilizing a

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standard mathematical model called linear programming from the discipline of management science, which allocates production resources on a lowest-cost basis first before moving on to a higher-cost basis. Another example is the problem of allocating a number of vehicles by size to shipments for a transportation company. This problem is solvable by the transportation model from management science that solves for the proper allocation of transportation vehicles to minimize total transportation costs. Essentially, these illustrations are for relatively well-structured problems. Because many of the problems solved for a typical company are not always well structured, managers must also be familiar with the decision-centered approach to problem solving discussed below.

Decision-Centered Approach Based upon the quantitative-centered approach to the problem-solving process, managers try to choose the best or optimal alternative—one that balances the costs, benefits, and uncertainties best and is therefore most likely to achieve the most satisfactory results. Optimizing a decision means making the best one available to the organization at a given time. In practice, however, managers may lack important information affecting the decision, may be under pressure to act quickly and with apparent decisiveness, or may have overlooked alternatives in the early stages of the problem-solving process. These limitations restrict decision making and thereby result in satisficing. The word “satisficing” means finding and selecting a satisfactory alternative (as opposed to the best one) that achieves a minimally acceptable solution.5 There is one word of caution: managers should not select the first satisfactory alternative developed but should take the opportunity and time to develop other good, feasible alternatives. Included in satisficing is the concept of bounded rationality. That is, managers often make decisions without knowing all the alternatives available to them and their possible consequences, which means that there is a limit as to how logical or rational their decisions can be. In everyday organizational life, managers make the most logical decisions they can, limited by their inadequate information and by their ability to utilize this information, thereby resulting in bounded rationality.6 Rather than make the best or ideal decision, managers more realistically settle for a decision that will “satisfice” rather than one that will “optimize.” This satisficing approach does not mean that managers should give up trying to make the best possible decisions. It simply means that they recognize that at some point it is too expensive, time consuming, or difficult to acquire additional information or attempt to analyze it. For example, it is more practical for a finance manager to try to decide what must be done to earn an “acceptable” level of profits than to try to “maximize” potential profits. In light of the realities of the business world, Herbert Simon’s three steps of problem solving are set forth below,7 including a fourth step he added later.

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One last step has been appended by the author for a more complete decisioncentered approach to the problem-solving process.8 1. Intelligence. The first step is concerned with searching the environment for conditions that call for a decision—that is, problem recognition. It is basically a data-gathering phase in which the manager seeks information to define the problem more clearly and provide some input to the solution process. The manager assesses the extent of the problem and obtains data to be used in the design phase. 2. Design. The second step centers on inventing, developing, and analyzing possible courses of action. It involves manipulation of the data obtained to develop various alternative solutions to the problem. The manager’s perception of the problem is used as the data are assembled and manipulated to provide input in the development of alternatives. 3. Choice. In this third step, the task is one of evaluating alternatives. This phase of the problem-solving process also requires selection of the best from among the alternatives developed in the design phase. The choice is generally made under a satisficing perspective versus one of optimization. Also, sensitivity analysis can be employed to select the best alternative. 4. Implementation. The fourth step puts the chosen solution into effect. The best alternative selected in the prior step is placed into operation for better or for worse. If a good alternative has been selected, the results should be favorable. If a poor alternative has been implemented, the results will generally be poor. This step parallels step 5 of the quantitative-centered approach. 5. Control. The fifth step is the monitoring of the outcome and making necessary adjustments. This last step links to the first step—intelligence—by recognizing that a new problem has arisen and needs to be solved. This step is like step 6 of the quantitative-centered approach. These steps are summarized in Figure 3.3 and related to the quantitativecentered approach. As with that approach, the decision-centered approach provides for feedback. Typical Applications of the Decision-Centered Approach The reader should have no difficulty, as with the quantitative-centered approach, in applying the decision-centered approach. The analysis of bad debts in an accounts receivable department is a good illustration. Essentially, it consists of structured elements—that is, the experience of the firm in the past regarding bad debts. And it consists of unstructured elements—that is, what impact the expanding or contracting economy will have on the company’s customers’ ability to pay. Based upon the integration of both structured and unstructured elements utilizing the decision-centered approach, the amount of the reserve for bad debts can be determined. Another illustration is the determination of what investments should be offered to clients by an investment counselor. Because each client has different investment objectives and information about

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investments is semi-structured in nature, it is advisable for the investment counselor to use a decision-centered approach to assist in the selection of appropriate investment opportunities for his or her clients. TWO APPROACHES TO THE PROBLEM-FINDING PROCESS In traditional approaches to the problem-solving process, the accent is on some type of analytical technique, which has been the main thrust of information systems. However, there is a need to go a step further by incorporating creativity in the form of “logical-analytical thinking.” Logical-analytical thinking goes beyond analyzing the present problems of an organization, typical of the problem-solving process. Its accent is on identifying future problems and their impact on the organization today and tomorrow. In addition, logical-analytical thinking is directed toward future problems that are actually future opportunities in disguise. In order to solve future problems and/or opportunities, it is helpful to employ one or more of the creativity techniques set forth before. Generally, a manager who has identified future problems has also identified opportunities. As such, the problem-finding process within a BIS environment can be separated into a problem-centered approach and an opportunity-centered approach. For the problem-centered approach, logical-analytical thinking centers on examining the environment with the idea of looking into the future and exploring problems that will have an impact on the organization now or at some time in the future. Essentially, the process is one of projecting into the future, determining important problems (i.e., problem finding), and bringing them back to the present time to examine their cause-and-effect relationships. Likewise, logical-analytical thinking is needed in the opportunity-centered approach. However, the perspective is somewhat different in that the main focus is on identifying opportunities for the organization to pursue that generally come from problems uncovered. In effect, managers do need to change an organizational liability into an asset—that is, identify problems that can result in important opportunities for the organization. Also, the opportunity-centered approach need not always be related to future problems. It can center on current opportunities that are identified by top management and/or the corporate planning staff. Specific opportunities can be addressed directly by organization members at the higher levels of management. In addition to the following exposition on the problem-finding process, other information and examples will be found in Part IV of the text. Problem-Centered Approach The problem-centered approach as set forth here is taken from one of the author’s previous publications with some modifications, if appropriate, to include the generation of new ideas using creative computer software.9 As shown

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Figure 3.4 A Comparison of Steps in the Problem-Finding Process: The Problem-Centered Approach and the Opportunity-Centered Approach

Note: There is Feedback from the last step of both approaches to the first step.

in Figure 3.4, it consists of four steps plus the solution and implementation phases from the problem-solving process. Essentially, these four steps precede the actual solution to the future problems under study. 1. Generation This first step is the most important one in the problem search because it focuses on a probe of potential problems that might have an impact on the company. Initially, the analysis is “forward looking” because it is a search for future problems. Once these problems are identified, the analysis becomes “backward looking,” since there is a need to evaluate the cause-and-effect relationships of each problem and its possible effects on the organization currently. Accent is placed on each problem, from the short range to the long range. It may also be necessary to look at each problem in different economic climates (good, average, and bad conditions). To generate important problems, the best approach is to use brainstorming. Generally, top managers and their staffs, along with members from the corporate

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planning staff, meet periodically to brainstorm future organizational problems. In a typical session, all important problems uncovered are recorded; then analysis is performed to explore the important aspects of each problem. These steps are performed in a back-and-forth fashion—that is, the original question concerning the problem as given and the subsequent spontaneous ideas are all written down. When participants’ minds have cleared, they concentrate on reformulations produced from the collected material, and a choice of one or more is made before continuing with questions in sequence concerning the problem as understood. (As noted, the initially forward looking analysis becomes backward looking.) This can be repeated until all aspects have been considered. Complex problems may require the application of other creativity techniques in order to uncover an unexpected, new angle. 2. Evaluation After the problem-generation phase, the second step centers on examining problems in terms of their being worthy of managerial concern. Because many of these problems are found in the future—the next two to five or possibly 10 years—the question can be asked, “Which problem or problems should be undertaken for solution?” To answer this question, it is necessary to evaluate the impact the solution to a problem has on the organization—for example, in terms of net profit and return on investment. In other cases, consideration might be given to other important areas of an organization, such as sales and customer service. Similarly, it may be necessary to relate the problem or problems back to the organization’s critical success factors. If deemed appropriate, creative computer software can be used to generate new ways and ideas that are related to help in the evaluation of future problems. In this manner, a broader approach can be used to evaluate the problems uncovered in the first step. In this evaluation process, it is possible that more problems may be apparent. If this happens, it may be necessary to add these problems to this evaluation step. Generally, there is a need to perform a cost-benefit analysis to determine the impact of the solution on the financial aspects of the organization today and tomorrow. This task, for example, can be relegated to managers at the appropriate levels and their staffs to determine which problems are of valid concern for managerial action. The problems generated, then, are evaluated in terms of benefits versus costs, thereby becoming the basis for validation in the next step. 3. Validation Building on the prior step of problem evaluation, actual problems in this third step are selected as being worthy of managerial concern for today and tomorrow. The validation for solving these problems is generally backed up by a costbenefit analysis and related to the company’s critical success factors, if appropriate. If such an analysis is not available or too difficult or costly to develop, it may be necessary to use alternative means, such as the consensus of the

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majority of this problem-finding group, to substantiate this selection as an important problem to be solved. For example, to determine what problems should be validated and solved, the problem-finding group meets again and reviews the recommendations of the managers and their staffs. For the most part, the staffs have prioritized the important problems to be solved. It is up to the problemfinding group to pass judgment on them. As noted, some of the problems cannot be resolved in terms of a cost-benefit analysis. Input from managers and their staffs is generally necessary to finalize the prioritized list for implementation. 4. Establish Boundaries After the problems have been validated in step 3, it is necessary to describe (define) each problem within its boundaries in this fourth step. This ensures that areas that the problem might touch or come into contact with will be included in the problem-finding process. The net result is that there is the need for some fine tuning so that the appropriate boundaries of the problem will be considered in its solution. Typically, to establish realistic boundaries, the problem-finding group must have a rich knowledge of the future (good, average, or poor economic conditions), a clear description of performance that a solution must fulfill, and a clear idea of what to expect from solving the problem. These areas must be as clear and as accurate as possible because if the problem is badly defined, the solution is generally of no value to management. Solution. Solution to the problem-centered approach can take one of two directions. One is using appropriate steps for the quantitative-centered approach, which determines the best solution for well-structured problems. The other is selecting a good solution from a set of feasible ones using the decision-centered approach for solving semi-structured problems. In either case, the solution centers on solving problems before they actually occur. The accent is on practicing “management by perception,” rather than using the “management by exception” approach that is found in traditional problem solving. Implementation. In this last part of the problem-centered approach, the implementation steps for the problem-solving process are usable. Fundamentally, these steps for the quantitative approach and the decision-centered approach are the same. In addition to implementation, it is necessary to establish control over the solution in order to detect how changing times are affecting it. It should be noted that there is a need of feedback in the problem-centered approach from the last step back to the first one. Typical Application of the Problem-Centered Approach To illustrate the problem-centered approach, consider a chemical products company that is currently offering a line of chemical products which are sold to retail and industrial laundry and dry-cleaning plants. Recently, the president and vice presidents (who assist the corporate planning staff) have become concerned about the direction the government is taking in terms of regulation and

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control of the dry-cleaning industry because of hazardous waste. This waste problem will force a decline in dry-cleaning chemical sales in favor of laundry products. Since dry-cleaning sales are 75 percent of the company’s total sales, the company’s management is concerned. Upon brainstorming the overall problem, a number of problems were generated (first step) by the corporate planning staff. One of these is the effect of shifting the manufacturing plants over to production of more laundry chemicals. An equally important problem is the need to change sales and marketing strategies toward the laundry market. Needless to say, other problems are identified. In turn, ideas related to overcoming these problems are evaluated using a creative computer software package. A costbenefit analysis and compliance with organizational objectives and government regulations serve as a final evaluation basis (second step). At this point, the corporate planning staff meet with top management to validate the overall problem and related problems (third step). After examining their impact on customers, production, and other concerns, a list of priorities is set forth for the specialized problem areas. Next, the scope of boundaries of each problem is addressed (fourth step). Changes in production and inventory levels are outlined for the manufacturing facility. The marketing efforts are defined in terms of advertising medias and markets to address. Other boundaries, like personnel, budgets, and time frames, are set for each problem. Possible solutions for each of the problems are identified (fifth and sixth steps). Solutions for the manufacturing department include building a manufacturing facility exclusively for laundry products, contracting the additional work to other manufacturers, or having existing plants updated. After analysis of potential solutions, top management needs to decide which is the optimum solution for each problem after considering all of the factors. If updating the present manufacturing facility is identified as the best alternative, funds must be allocated to facilitate its completion. Next, the optimum solution for each problem must be implemented (seventh and eighth steps). In addition, there is a need to place proper controls over the implemented solutions. Opportunity-Centered Approach This second approach to the problem-finding process is also taken from a prior publication by the author.10 As illustrated in Figure 3.4, it consists of three steps plus the solution and implementation phases from the problem-solving process. Like the problem-centered approach, these three steps precede the actual solution to the future opportunity under study. 1. Exploration This first step examines the internal and external environment for opportunities that come from problems uncovered. As in the problem-centered approach, brainstorming is generally used by managers and their staffs. The focus is directed away from the short range to the medium and long range where every

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effort is used to determine what opportunities are presented by the problems discovered in the future. As in the prior approach, the analysis is initially forward looking in that there is a search for future problems. Once the problems have been identified, they are examined from the standpoint of identifying opportunities for improving the company’s operations (from the standpoint of sales, profits, or whatever). From this perspective, the concept of opportunities has a “positive” connotation, while the concept of problems has a “negative” meaning. If deemed appropriate for the situation, creative computer software can be gainfully employed to assist managers and their staffs in developing new ideas that exploit appropriate opportunities for a company to pursue. Generally, creative computer software results in the exploration of more new ideas than if performed manually. This will be apparent in the various examples given in Part IV of this text. The selection of more opportunities helps to assure managers that this exploration step is performed in a most comprehensive manner. 2. Selection Having identified appropriate opportunities, the second step is to determine what opportunities (one or more) should be explored by managers and their staffs. The selection process should focus on opportunities that can relate to a company’s critical success factors. Typically, these factors include price, sales promotion, customer service, product mix, inventory turnover, cost control, and quality dealers. In turn, the interrelationships of the critical success factors and the company’s goals and objectives are discussed for further clarification. But more important, this discussion determines which opportunities should be pursued by the company, thereby identifying them in a clear and meaningful way. Moreover, it takes into consideration all the important facts that bear on important company opportunities. Where deemed necessary, a cost-benefit analysis can be used to determine which opportunities are more important than others in terms of how they effect the company’s future profit. 3. Examine Boundaries The third step centers on surveying the environment for the opportunities identified before pursuing an opportunity solution. Due to the nature of some opportunities, the boundaries may be quite wide—that is, they may extend beyond the company and may be related to emerging and established organizations and industries. Generally, greater opportunities are found when boundaries are extended. Thus, top management and the corporate planning staff need to examine the boundaries surrounding the opportunities from a narrow to a very wide perspective. The net result is that the proper boundaries are used in the solution and implementation of the opportunity. Solution. As in the above problem-centered approach, the solution to the opportunity-centered approach can take one of two directions—determination of the best solution using a quantitative-centered approach or the selection of a

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good solution from a set of feasible ones using the decision-centered approach. Current problem-solving approaches require that the decision maker pick an appropriate solution from the set of feasible ones. In a similar manner, an opportunity solution requires that the decision maker select the best opportunity from the set of feasible ones under study. As with the problem-centered approach, the main thrust is on practicing management by perception. Implementation. In this last part of the opportunity-centered approach, the implementation steps for the problem-solving process are used. The opportunity must be monitored and implemented as well as making the necessary adjustments to changing times. Also, this approach provides for feedback. Typical Application of the Opportunity-Centered Approach To illustrate the opportunity-centered approach for the chemical products company (as set forth previously), managers and their staffs need to examine the environmental issues in more depth. That is, they need to identify future opportunities that may exist. If they feel that biodegradable products hold an edge in the future marketplace, then they need to examine such implications. This could include not only a more thorough exploration of the retail and industrial laundry market but also the home products market (first step). Utilizing the same creative computer software package set forth previously, important ideas to exploit market opportunities are introduced by group members. The resulting opportunities in the home market center on private labeling for an established firm, buying a firm for its distribution and marketing efforts in the home market, or breaking into the home market itself. By looking at the tradeoffs of each alternative, one is selected for further review. Using a cost-benefit analysis, private labeling is determined to be the best alternative because of the company’s contracts (second step). The boundaries of the private-labeling issue are examined (third step); that is, which companies would be interested in such a product line, what is the target home market, and what are the cost and pricing needs of offering such a product. These boundaries will lead to the best solution (fourth and fifth steps) and implementation of the best alternative (sixth and seventh steps). Additionally, controls need to be implemented to make sure that the solution is best for the times. DECISION MAKING AND CYBERSPACE IN AN EFFECTIVE BIS ENVIRONMENT Decision making in an effective BIS environment centers on gathering, managing, and analyzing intelligence in order to produce effective answers to a company’s strategic, tactical, and operational problems. As noted earlier in the chapter, decision makers must understand the what, where, when, who, and how of the facts surrounding the problems and apply the appropriate means to solve the problem. Typically, a thorough understanding of the problems and/or op-

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portunities must be undertaken utilizing some type of systems approach within a BIS operating mode. In some cases, decision support systems or executive information systems will provide the appropriate means to answer problems under study. In other cases, on-line analytical processing systems or knowledge management systems will be quite adequate to undertake the required analysis. In yet other cases, a combination of these system approaches or a BIS operating mode is necessary, especially when problems are very large and complex and cut across the entire organization. To place this viewpoint into perspective, an important question can be raised: How can a typical company utilize cyberspace to its advantage in order to maximize its return in the years to come? Needless to say, this far-reaching new technology can impact a typical firm in a very positive way if handled properly. On the other hand, the misapplication of cyberspace can have a negative impact on the company. It should be noted that behind high-tech screens used in cyberspace are some distinctly low-tech costs. On-line sellers that had dreamed of avoiding brick and mortar are now building distribution centers and warehouses. The World Wide Web’s ability to compare prices are at the click of a button. The problems for traditional retailers can be even higher for traditional retailers going into the cyberspace business. In reality, their on-line sales may reduce dramatically sales that they would have made off-line (i.e., in their retail stores). Their backshop infrastructure was built to dispatch truckloads of goods to hundreds of stores as opposed to shipping small orders to millions of individual customers. In addition, manufacturers hoping to sell directly to consumers risk the wrath of the dealers and wholesalers that provide most of their revenues. The World Wide Web has, in effect, been both the problem and the opportunity. It is no wonder that retailers venture into cyberspace with a certain amount of trepidation. Hence, decision makers operating in this relatively new area of electronic commerce need a very thorough analysis of present facts and coming trends in order to be effective in the implementation of cyberspace for their companies over time.

SUMMARY The initial focus of the chapter was the need to rethink decision making as a way of capitalizing on business intelligence. One important new direction centers on merging transactional processing and decision processing to improve decision makers’ effectiveness. The various levels of intelligence within a BIS environment were then discussed. Next, the various types of problems were examined within the context of a BIS operating mode. The types of problems that can be solved were discussed within a problem-solving or a problem-finding mode. In the problem-finding process, the problem-centered approach pinpoints future problems that can be solved today to minimize their impact in the future. Related is the opportunity-centered approach, which searches the environment

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for opportunities that are derived from problems uncovered. Finally, an example of decision making using business intelligence was related to cyberspace. NOTES 1. Colin White, “Decision Threshold,” Intelligent Enterprise, November 16, 1999, pp. 35–40. 2. Ibid. 3. Robert A. Raitt, “Must We Revolutionize Our Methodology?” Interfaces, February 1974, p. 2. 4. Robert J. Thierauf, On-Line Analytical Processing Systems for Business (Westport, CT: Quorum Books, 1997), pp. 39–41. 5. James G. March and Herbert A. Simon, Organizations (New York: John Wiley & Sons, 1958). 6. Herbert A. Simon, Models of Man: Social and Rational (New York: John Wiley & Sons, 1957). 7. Herbert A. Simon, The New Science of Management Decisions (New York: Harper & Row, 1960), pp. 2–3. 8. Thierauf, On-Line Analytical Processing Systems for Business, pp. 41–42. 9. Ibid., pp. 49–52. 10. Ibid., pp. 52–54.

4 Effective Systems and Software Found in Business Intelligence Systems SIGNIFICANT ADVANTAGE COMES FROM AN ORGANIZED DELIVERY OF BUSINESS INTELLIGENCE In the recent past, there has been a great accent on managing data, information, and knowledge. Today, the accent has moved to helping a typical company be more effective in its day-to-day operations. More specifically, this takes the form of E-commerce and enterprise-wide supply chains for business intelligence activities that stretch beyond the boundaries of the organization. At the click of a mouse, intelligent agents are capable of bringing back critical, timely information and knowledge. Tapping into data services provided by logistics partners, managers are able to adjust supply chain strategies. And linked to collaborative computing platforms, such as Lotus Notes/Domino or Microsoft Exchange, a business intelligence approach enables users to search for others within and perhaps even outside the organization who are engaged in related activities. From this broad perspective, the left hand might finally discover what the right hand has been doing all these years. Decision makers utilize the appropriate resources—that is, systems and software (as found in this chapter) plus data warehousing and computer networking (as found in the next chapter) that business intelligence systems draw upon for decision support. They also use business intelligence systems to “close the loop” within the organization, making sure that the organization learns from experience. Business-critical decisions, whether proactive or reactive, help an organization adapt to change rapidly and also help decision makers assess quickly how changes are affecting their organizations, customers, and supply chains so that they can make intelligent decisions in the future. The business advantage to a typical company is not in the data warehouse or the real-time computing

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system but in the seamless and organized delivery of business intelligence to decision makers. Business Intelligence Systems Are the Means to the End Currently, the new generation enterprises need an infrastructure to capture and create business intelligence (BI), store it, improve and enhance it, organize it, make it consistent and usable, and disseminate it to decision makers in the organization who need it. Business intelligence systems (BIS) are not just a set of tools. They are a set of processes, technologies, attitudes, and reward systems. They are an integrated approach to identifying, collecting, managing, and, most importantly, sharing the enterprise information assets with individual employees to put the business intelligence to use. Decision makers take advantage of the information and knowledge that underlies business intelligence and use it to move the organization into the next phase needed for effective marketing and customer management services and support. Decision makers need effective BI to really satisfy customers today as well as tomorrow by anticipating their needs. In effect, business intelligence is worthless unless it leads to action, marketing, and customer support actions which, unlike operational decisions, are very complex and have tremendous variety and variability. Neither generic algorithms nor canned management reports can adequately support marketing activities. Information and knowledge no longer suffice. To be successful, decision makers need a process that leads to business intelligence (i.e., a means to an end). UNDERLYING STRUCTURE FOR EFFECTIVE BUSINESS INTELLIGENCE SYSTEMS As a starting point, it is helpful to look beyond BIS technology and requirements by establishing a fundamental framework that facilitates the development of business intelligence systems. Such a framework is found in an open systems environment, which allows users to retrieve information and knowledge and their resulting business intelligence that may be found halfway around the world locally. Complementary to this environment is client/server technology which centers on the way computers and networking technologies are applied. Because both are important to a BIS operating mode, both are discussed directly below. In addition, the current movement to some type of data architecture is covered below. In all cases, consideration should be given to scalability (i.e., the ability to add processors and disk drives incrementally as system demand grows) and high availability, including on-line backup and recovery. Open-Systems Architecture Because computer managers are being asked continually to manage more and more of the information technology (i.e., hardware, software, and networks in

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their companies), they must develop the ability to reach beyond the mainframe data center into an end-user domain filled with a diversity of microcomputers, departmental systems, and networks as well as a me´ lange of software tools and applications. And they must be prepared to make the elements of this mixture work together. No longer can computer managers think in terms of a single vendor or hardware or a single operating system. Currently, they need to find ways to integrate an expanding assortment of computing products and services to meet the demands of enterprise-wide and even interenterprise information, knowledge, and intelligence in a competitive environment that has expanded to a global scale. Such an approach is found in an open systems environment. Open systems implement specifications for interfaces, services, and supporting formats, which enable properly engineered application software to be ported across systems, to interoperate with other applications, and to interact with users in a consistent manner. To place open systems in perspective, large retailers, such as Wal-Mart, Kmart, Dayton Hudson, and Sears, manage inventory by linking to manufacturers as if in a single network. While this open systems approach eliminates the advantages of a supplier’s proprietary network, it also gives manufacturers valuable information. These retailers provide manufacturers with updated sales and inventory information electronically so that the manufacturers can plan production. With such data arriving in a standard format from so many customers, a manufacturer like Levi Strauss or Vanity Fair can easily estimate aggregate demand weeks in advance and produce sales forecasts to help them plan production even further in advance. This capability can be very helpful to a company desiring to take the analysis one step further using business intelligence technology. Client/Server Architecture A client/server is a computer architecture in which any device in a network can request information or processing services from any other. When a device is asking for data or processing power, it is a client. When it is supplying information or services, it is a server. As such, client/server architectures provide users transparent access to file servers, database servers, print servers, and other devices, thereby maximizing user options and network throughput, while minimizing operating costs and response time over the network. The client/server model is a powerful method for writing applications that partition the program into pieces installed in two or more separate network stations. Tools built for implementing applications in this way allow program parts developed independently to interoperate over a network. Client/server architecture makes sense for improving payoff for information and knowledge because it helps organizations maximize their extensive investments in processing power—from the desktop to the computer mainframe. Client/server architecture only makes sense, however, if it is appropriate for all organizational applications. There are a number of factors for the shift to client/

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server computing. The most important ones are networks, standards, and the tremendous power and versatility of today’s desktop devices. Currently, no single vendor can supply all the pieces to make client/server computing a reality. A good test of how well a vendor is positioned to meet an organization’s client/ server needs is to examine how open the company’s product line is. Open computing, software, and network tools are the plug-and-play components that are the foundation of client/server architecture. Data Architecture Another important technology for business intelligence is the utilization of an appropriate data architecture for aged and real-time data. Typically, online transactional processing (OLTP) systems are generally plain, simple, and to the point (i.e., functional and without artifice). On the other hand, data warehousing systems are large, complex, and varied. Many companies are consolidating previously isolated, stand-alone, or single-product-line databases into very large databases (VLDBs) that provide a unified view of business operations and customers. Hence, data warehouses are growing very large and very quickly. They are, after all, called warehouses for that reason. They are designed to store a company’s inventory of corporate data for later retrieval in much the same way a manufacturer stores finished goods in a physical warehouse. To that can be added daily copies of transactions from a business’s ongoing OLTP operational systems as well as outside data from historical archives, demographics, market research, and the like. In fact, an average data warehouse can grow up to sixfold within two years. For some large companies, data warehouses have reached the terabyte level due in part to the fact that they are collecting more data, and in part to the fact that they are keeping data longer to analyze trends. In addition, data that centers on discovering knowledge about the company and its customers is being opened up to more users and indexes are being created to support their queries. As will be seen later in the chapter, VLDBs are needed to assist companies in data mining, which is the use of knowledge discovery, pattern recognition, data analysis, and expert systems technology to automate the search for information. This is the cutting edge of decision support, already in use by large organizations for sales and inventory analysis, database marketing, fraud detection, financial prediction, and pharmaceutical development. Data mining algorithms and techniques have been and will be the focus of expert database research. Overall, VLDBs have become the norm to help companies discover appropriate intelligence to meet their decision-making needs. It should be noted that NCR’s Teradata has become the database of choice in more than 50 percent of the large data warehouses in production worldwide.1

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FOUR ESSENTIAL ELEMENTS IN DEVELOPING AND IMPLEMENTING BUSINESS INTELLIGENCE SYSTEMS The success of a typical company, as stressed in Chapter 1, depends on its ability to anticipate changes, reduce product life cycles, adjust to changing government regulations, compete with companies in every corner of the globe, and transform ever-increasing amounts of data into information and then into knowledge that can be acted upon using a business intelligence systems approach. In addition, success depends on the ability to learn to work in new and different ways, which includes acting on acquired business intelligence that can change over time. All of these activities center around a company’s declining profit margins. The central focus of this chapter and the next one is on the four essential elements in developing and implementing business intelligence systems. As noted in the prior chapter, these elements start with the utilization of problem solving and problem finding and their related techniques to get a grasp on present and anticipated problems as well as to identify future opportunities that are tied in with a company’s critical success factors. By way of review (from Chapter 1), the four essential elements of effective business intelligence systems are: 1. upgrading current information systems to business intelligence systems using idea processing systems, knowledge management systems, on-line analytical processing systems, decision support systems, and executive information systems (covered in this chapter). 2. utilizing appropriate software that includes business intelligence software, knowledge extraction tools, knowledge management software, knowledge management–intranet engines, data mining or knowledge discovery software, OLAP software, statistical analysis software, and GUI/4GL software (covered in this chapter). 3. building a data infrastructure that is related to very large databases, data marts, data warehouses, and data federation systems for aged and real-time data (covered in the next chapter). 4. developing an organization-wide and global computing network that is linked with a company’s intranets and extranets as well as the Internet via the World Wide Web, including an emphasis on E-commerce (covered in the next chapter).

Traditionally, the development and implementation of information systems has centered on systems that have operated in isolation. However, today, with the renewed focus on the customer and the pervasiveness of the Web, companies need to find ways to integrate such systems. Such an approach is found with enterprise application integration (EAI), which is covered in Chapter 6. Basically, any information or knowledge that leads to a better understanding of a company’s operations—no matter what repository or system it resides in— should be readily available to company personnel to perform their business tasks. An EAI approach is ideally positioned to act as a unifying integration

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layer, thereby providing the infrastructure to manage the access and presentation of business intelligence from disparate sources. CURRENT INFORMATION SYSTEMS THAT CAN BE UPGRADED TO PROVIDE BUSINESS INTELLIGENCE The first important element in an effective business intelligence system is the utilization of the appropriate system to provide a good and comprehensive understanding of the area under study. Such a system should give decision makers new insights and perspectives that were not previously available. Essentially, these systems should provide decision makers with the capability of meeting most problem situations that they encounter in their jobs. The ability of these systems along with the experienced judgment of the decision makers should result in an enlightened way to perceive interrelationships of the presented facts which, in turn, leads to the desired results. In the discussion to follow, knowledge management systems (refer to Chapter 7), on-line analytical processing systems (refer to Chapter 8), decision support systems (refer to Chapter 9), and executive information systems (refer to Chapter 10) are covered. Initially, idea processing systems are discussed as a means of obtaining new ideas for the problems or opportunities under study. Creativity Underlies Idea Processing Systems Generally, idea processing systems (IPSs) are considered to be related to decision support systems. Some in the field consider them a subset of group decision support systems. No matter how idea processing systems are looked upon, they are systems designed to capture, evaluate, and synthesize individual ideas into a larger context that has real meaning for decision makers. From this perspective, idea generators are generally used to assist in the idea-formulation stage. To better understand this type of system, it would be helpful to examine initially the meaning of ideas. An idea can be thought of as a formulated thought or opinion. Ideas spring from knowledge, which is essentially derived from observation of the environment in which one lives as well as from an awareness of one’s internal emotions and feelings about these observations. But knowledge implies more than observations of past experience. It also includes some form of interpretation of past experience. Ideas can be thought of as the conscious expression of these interpretations. The basic stages of an idea processing system center on inputs in the form of problem statement and observation about the problem. In turn, processing involves idea generation and evaluation of ideas for solving the problem. The end result is outputs (i.e., report preparation and dissemination of information about specific ideas to solve the problem). Regarding idea generation, the behavioral sciences have not been able to explain how a person’s mental processes operate nor how a person’s knowledge is organized. However, there is ongoing

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research by behavioral psychologists as well as by computer scientists who are attempting to imitate such functions with a computer. Needless to say, idea processing systems are a natural for helping management and their staffs in gaining competitive advantage and improving employee productivity.2 Knowledge Management Systems Knowledge management systems (KMSs) are designed to improve corporate efficiency by providing a framework and tools and techniques to reuse captured intellectual assets. By marshaling resources to respond to problems and opportunities, a company’s responsiveness can be vastly improved, bringing people together across time and geography to share ideas. The essential elements of knowledge management are knowledge discovery, knowledge organization, and knowledge sharing. Knowledge is discovered and captured where it is: in the heads of people, in workflow diagrams and procedure manuals, or mined from transaction output stored in databases. Knowledge is organized according to the company’s preferred classification schema or taxonomy. Finally, knowledge is shared among those employees who are authorized to know about it and can benefit from its availability. Essentially, a knowledge management system is capable of making comparisons, analyzing trends, and presenting historical and current knowledge. But more importantly, such a system enables decision makers to analyze the patterns quickly and see the most significant trends. As such, this represents an accurate predictive approach for decision makers. In addition, a knowledge management system can track and evaluate key critical success factors for decision makers, which is valuable in assessing whether or not the organization is meeting its corporate objectives and goals. A knowledge management system, then, can assist decision makers in making more informed decisions that affect all aspects of a company’s operations. An important goal of knowledge management systems is to provide competitive advantage by giving decision makers—from the highest to the lowest level—the necessary insight into patterns and trends that affect their domain. In effect, a broad-based KMS environment challenges decision makers to evaluate changing times more thoroughly. Such an environment allows decision makers to tailor their information and related knowledge requirements by discriminating according to user-defined criteria. Business intelligence systems complement knowledge management systems by providing decision makers with a very thorough understanding of their business operations today and tomorrow. Past management information systems basically used the computer as a means of information to solve recurring operational problems. A better approach is the utilization of knowledge management systems that position decision makers at the center of the decision-making process. By increasing the capabilities of decision makers, a KMS environment improves the chances that an organization will achieve its goals of increased sales, higher profits, and so forth by placing

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knowledge and related information in the hands of decision makers at the proper time and place and by providing flexibility in their choice and sequence of analysis and in the final presentation of results. From this enlarged perspective, knowledge management systems provide essential knowledge and related information to decision makers so that they can better cope with changing times.3 On-Line Analytical Processing Systems Today, on-line analytical processing (OLAP) centers on systems that focus on asking and answering “what happened” to operations. A most important part of OLAP systems is their multidimensional analysis capabilities—that is, analysis that goes beyond the traditional two-dimensional analysis. Essentially, multidimensional analysis represents an important method for leveraging the contents of an organization’s production data and other data stored in company databases and data warehouses because it allows users to look at different dimensions of the same data—say, by business units, geographical areas, product levels, market segments, and distribution channels. As such, OLAP makes it easier to do analyses that cross departmental and even corporate boundaries. Another way of viewing OLAP is to see it as getting a typical company out of the custom report writing business and into the data-cube-server building business. An OLAP data structure can be thought of as a Rubik’s Cube of data that users can twist and twirl in different ways to work through “what happened” scenarios and get at the real issues of the situation. Current OLAP tools have proven their value in providing a multidimensional view of summarized data. Some of these tools are available within a business intelligence operating mode to further enhance understanding of a company’s operations today as well as in the future. Although OLAP tools meet many needs, they do not allow for the analysis and understanding of individual customer behavior at the transaction level. The reason is that OLAP tools, both those implemented on top of relational databases (ROLAP) and those implemented on the top of multidimensional databases (MOLAP), center on aggregating and summarizing data. Although aggregated data can provide trend analysis information, it is not actionable at an individual level. For example, knowing that 5,000 products were sold does not help a company’s decision makers to focus on individual customers. It is knowing who those 5,000 customers are that can help decision makers to get at the underlying profiles and possible motivations for buying a company’s products or services. From this broader view, knowledge discovery is needed to complement the information contained within an OLAP system that decision makers have found by “slicing and dicing” through reams of data rapidly.4 Decision Support Systems Essentially, an individually oriented decision support system (DSS) is designed to satisfy the needs of a manager at any level in a distributed data proc-

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essing environment. The system is designed to support the problem-finding (future problems related to the present) and problem-solving decisions of the manager. It incorporates features found in management information systems and in quantitative models of management science. Such a system emphasizes direct support for the manager in order to enhance the professional judgment required in making decisions, especially when the problem structures tend to be semistructured and unstructured. The use of interactive systems and CRT displays in a decision support system are examples of this point. Emphasis is placed on helping the manager to make decisions by being at the center of the decisionmaking process rather than on actually making decisions for the manager. This interplay results in a total effort that is greater than the manager or computer operating independently (as in traditional MIS), thereby providing synergistic decision making. Also, information is presented in a useful form rather than as a mass of all information that might be useful. From this perspective, an individually oriented decision support system builds on present business intelligence systems as well as complements them.5 Since there is currently a move toward group decision support systems (GDSSs), this approach needs to be defined. Basically, group decision support systems combine computers, data communications, and decision technologies to support problem finding and problem solving for managers and their staffs which may also include operating personnel in the newer work environments. Technological advancements, such as groupware, electronic boardrooms, local area networks, teleconferencing, and decision support software have spurred an interest in this area. In addition, fundamental changes in the external environment of organizations are encouraging organizations to head in this direction. Typically, organizations currently are experiencing the emergence of a post-industrial environment characterized by greater knowledge, complexity, and turbulence. One important effect of this trend is that decision-related meetings are becoming more frequent and more important. At the same time, the decisions confronting groups are becoming more complex and must be made more quickly and with greater participation than in the past. As part of the transition into this new environment, organizations are exploring advanced information technologies that might be employed in group meetings. Overall, group DSS has the capability to allow marketing executives, for example, to outmaneuver their competition and assist in resolving issues that center on making employees more productive.6 Executive Information Systems Executive information systems (EISs) are, to a large degree, used for highly structured reporting, sometimes referred to as status access. DSS has become almost synonymous with modeling and unstructured, ad hoc querying. Executive information systems are aimed at senior executives who currently have few, if any, computer-based systems to assist them in their day-to-day responsibilities.

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EIS brings together relevant data from various internal and external sources, delivering important information quickly and in a useful way. More important, it filters, compresses, and tracks critical data as determined by each executive end user. EIS performs the conceptually simple task of informing senior executives on matters relevant to their organizational responsibilities. Unlike traditional MIS functions that focus on the storage of large amounts of information, an EIS focuses on the retrieval of specific information and on status access. The emphasis is on reducing the time and effort that the executive user must expend to obtain useful information for making the organization more competitive and its employees more productive. An executive information system can be defined in its broadest sense as one that deals with all of the information that helps an executive make strategic and competitive decisions, keeps track of the overall business and its functional units, and cuts down on the time spent on routine tasks performed by an executive. As such, an EIS is capable of providing an executive with the right information in the right format, fast enough to enable the individual to make the right decisions. This is in line with the objective of providing executives with a better understanding of their operations as found in business intelligence systems.7

UTILIZATION OF CURRENT SOFTWARE WITHIN A BIS ENVIRONMENT Today, software vendors offer a wide variety of off-the-shelf software products for business intelligence systems. Software includes groupware, document management systems, E-mail, relational databases, and workflow, along with business intelligence software, knowledge extraction tools, knowledge management software, knowledge management intranet search engines, and data mining or knowledge discovery software. In addition, BISs can employ OLAP software, statistical analysis software, and GUI/4GL software to assist decision makers in understanding the area under study. Depending on the nature and purpose of the business intelligence system, there can be still other software products along with hardware that are needed to form a complete BIS package to gather, organize, collaborate, refine, and understand the relationship of the presented facts. Essential to capturing and making individuals’ knowledge available to the entire organization is the ability to deal with heterogeneous information. Although data marts and data warehouses are useful to collect and store structured information in an organization, a large amount of information resides in unstructured text— E-mail messages, documents, presentations, etc. To assist decision makers in using the appropriate business intelligence software and tools, an architecture has emerged that can be called the “Business Intelligence Marketplace.” This important area, which is basically a situational approach to business intelligence, is discussed below.

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Emerging Architecture for Business Intelligence Software There is currently an emergence of a new type of business intelligence architecture that supersedes individual tools and provides a content-rich interface to all types of corporate information and knowledge, leading to a better understanding of a company’s operations. Although initially called an “Information Marketplace,” it can be updated for BI systems. The key to this marketplace is an active repository or catalog that contains or points to a variety of objects both inside and external to the organization. Users can browse through the catalog, shopping for objects that interest them and publishing objects that they have created or modified for others. The objects may consist of structured data found in a data warehouse or unstructured content (such as text, audio, image, or video). The objects might be stored in a local or remote file server, Web server, or application (such as a document management system, Lotus Notes, or a customer application). A business intelligence marketplace is like a shopping mall. It provides users with a single Web-based interface for browsing, launching, publishing, and subscribing to any type of information, knowledge, or intelligence object. The repository or meta data catalog provides users with a plain-English description of the object, the currency and origins of the data, and other relevant facts. It functions like an electronic card catalog in a library that can display information about a book as well as deliver the text and pictures of the book itself. The business intelligence marketplace maintains interfaces to information and knowledge applications that generate or maintain objects. Rather than presenting users with a static collection of objects, the marketplace gives users dynamic access to data, which they can refresh, filter, and manipulate in real time. The upshot is that end users do not have to know where the data is stored, what format it is stored in, or the program required to access it. The business intelligence marketplace enables companies to distribute appropriate facts and figures easily, inexpensively, and securely to a much larger population of users, both inside and outside the organization (i.e., customers and suppliers). It provides transparent access to business intelligence, thereby allowing users to focus on content, not technology.8 Business Intelligence Software In the past, business intelligence software typically addressed a single function and required a certain level of proficiency to utilize it. As a result, organizations had to implement several types of tools to serve different business purposes. Business users needing multiple functions had to install and learn multiple tools and were back in the Lotus 1-2-3, WordPerfect, and Harvard Graphics mode of performing any integration functions manually. However, there have been steady increases in numbers of people utilizing business intelligence tools and performing integration functions, which is currently causing the demand for integrated

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software (i.e., suites). From this perspective, decision makers are better able to transform data, information, and knowledge into actionable intelligence for improved decision making. There are a number of current business intelligence software products. Brio Technology’s business intelligence software, Brio Enterprise, is an integrated query, analysis, and reporting solution. It is easy to use, giving more users access to the data they need in the form they need it via client/server interfaces or Web browsers. Integrated Web- and client/server–based solutions can be rapidly deployed for fast implementation and faster ROI. Another vendor, Business Objects, delivers an integrated, decision support tool that lets users access, analyze, and share the wealth of information stored in an organization’s database. A typical user can benefit from the rich set of Business Objects features. A user can browse through available reports to find the one that best fits his or her needs. With Business Objects, the user can do the following: access—get instant answers to questions autonomously; analyze— switch perspective or find the numbers behind the numbers; and share—highlight key factors in reports and share them with others. Cognos’s enterprise business intelligence tools allow business managers to extract critical insight through data access, reporting, and analysis. PowerPlay and Impromptu, combined with IBM’s DB2 Universal Database, DB2 OLAP Server, and Visual Warehouse offerings, deliver best-of-breed business intelligence in a single, integrated package. Fundamentally, Cognos PowerPlay is an OLAP tool. Its deployment flexibility, scalability, and state-of-the-art performance make it effective for companies of any size. Because PowerPlay organizes and presents corporate data in a business context, users can quickly understand what is happening throughout the company. In contrast, Cognos Impromptu is an enterprise solution for database query and reporting on the desktop and the Web. Hyperion Solutions Corporation is a provider of analytic application software for reporting, analysis, modeling, and planning. Hyperion Solutions’ marketleading packaged analytic applications, OLAP server, and end-user tools help organizations maximize business performance and gain competitive advantage by using information as a strategic weapon. It should be noted that IBM integrates technology from Hyperion directly with IBM’s DB2 Universal Database and other relational database software to create IBM DB2 OLAP Server. This product leverages Hyperion’s Essbase OLAP Engine to provide scalable, open, flexible solutions for a wide range of business reporting, analysis, and planning applications across the company. In addition, IBM resells Hyperion Essbase as one of IBM’s business intelligence solutions offerings. The current approach to business intelligence software by IBM is divided into three parts: products, solutions, and partnerships. In terms of product, the company offers a variety of data warehousing, data mining, and analysis tools. The centerpiece is the Visual Warehouse family for building, managing, and analyzing data marts and data warehouses. It grants access to data from various

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types of databases, including Oracle, Microsoft SQL Server, Sybase, and Informix, and allows data to be cleansed, then integrated into a single repository. As for data mining, Intelligent Miner uses IBM-developed algorithms to identify and highlight patterns. Available for both text and data, the text version trolls through correspondence, E-mail, and news article searching for ways to categorize content; the data version peruses more structured sources such as databases, data marts, and data warehouses. Also rolled into this product set is the IBM DB2 OLAP Server. The heart of the second element of IBM’s strategy is DB2 itself, highly scaleable and friendly toward complex data mining algorithms. A set of tools called Decision Edge targets business intelligence in specific industries, beginning with banks, utilities, insurance companies, and telcos. Each consists of four components—a data model with industry-specific criteria, analytical tools, a data warehouse, and hardware platforms. As befits IBM’s full-service model, an entire consulting arm, IBM Global Business Intelligence Solutions (GBIS), is chartered with implementing and integrating business intelligence systems. Lastly, the third element of IBM’s strategy involves its partners. IBM has formed alliances across the business intelligence systems community. Some partnerships have been formed with many of the software vendors found in this section of the chapter. The Business Intelligence System from the Oracle Corporation is based on Oracle Applications Release eleven and includes preconfigured performance metrics as an integrated part and standard feature of the suite’s modules. The Business Intelligence System leverages the integrated nature of the Oracle Applications modules and the database structure with sophisticated data analysis technologies. Release 11 is an easy-to-use, self-service Internet and intranet application that gathers information from the Oracle Applications modules. With the system, users at every level—from executives to line managers—can determine the business intelligence they need to respond quickly to rapidly changing business conditions. Because effective decision makers demand more than simple reports and single pieces of information, the system is designed to organize and collect intelligence so that it can be applied to answer critical business questions. The system provides a standard set of performance indicators and analyses related to fundamental business issues and critical functions such as financial management, manufacturing, sales, and marketing. The SAS Institute is marketing its Collaborative Business Intelligence (CBI) system. CBI combines the numbers-based data stored in warehouses with the text-based conclusions and trend descriptions drawn by analysts. Users can save their findings into a knowledge repository, making them easily accessible to the rest of the organization over the Web. Decision makers become more engaged in the knowledge-building process as they add to the thread, and everyone can search for similar projects and reuse ideas that have worked in the past. Further information on the above business intelligence software vendors and others can be obtained from their Web sites, as found in Figure 4.1.

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Figure 4.1 A Listing of Popular Business Intelligence Software Vendors and Their Web Sites

Knowledge Extraction Tools Within a BIS environment, knowledge extraction tools are useful to get a handle on a company’s current knowledge. More specifically, knowledge extraction tools can be used to identify and extract important technical information from organization files, documents, and so forth so that their contents can be turned into practical knowledge for a company’s personnel. The important challenge of knowledge extraction tools centers on their ability to provide fast and

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accurate current knowledge about a company’s current operations that can be used as is or analyzed further by a company’s decision makers. Knowledge extraction from text involves several phases of document and linguistic analysis: breaking the document into units (titles, paragraphs, sentences, and words), determining the linguistic role of each word in each sentence (i.e., noun, verb, adjective, etc.), normalizing different surface representations of the same word (i.e., bought, buys, etc.), and identifying and classifying patterns within the analyzed, normalized text that constitute content-rich structures—noun phrases (“stock market decline”), people (“President Clinton”), and organizations (“Treasury Department”). In turn, these extracted objects can be indexed by, for example, search engines in order to support better retrieval, categorization of topic area, and message routing and filtering. The challenge for providers of knowledge extraction tools is to provide a product that is very fast, accurate, and extensible. Because organizations deal with hundreds of gigabytes of textual information, system throughput is critical, as are precision (correctness) and recall (completeness). For many companies, knowledge extraction tools must deal with text in several languages. Above all, the tools must be able to be tailored to the customer’s domain. Due to the complexity of knowledge extraction tools, they have not been developed overnight but over a long period of time. For example, Inxight’s LinguistX product line is a result of 20 years of research and development at Xerox PARC. This product line provides multilingual natural-language processing components to extract knowledge at very high speeds. It supports a range of functions, from automatic language and character-set identification to phrase extraction to automatic summarization, in 11 European languages and Japanese, with others to follow. Today, LinguistX is incorporated into major applications from companies, such as Infoseek, Excite, Oracle, Testwise, and Verity. These products along with many others are covered in the material to follow on knowledge discovery software. Knowledge Management Software A knowledge management system (KMS), as noted previously in the chapter, is a systematic approach to capturing, integrating, disseminating, and applying the full range of a company’s knowledge to enhance its business value and gain strategic advantage. Today, this includes utilizing on-line knowledge management (OLKM), which provides the capability to organize unstructured data into dimensions just like structured data. OLKM lets company employees contribute what they know to a repository, offering unprecedented knowledge sharing and coordination in a typical company. In order to accomplish some form of knowledge management, a number of software packages are offered by many of the same vendors offering business intelligence software (which should be evident in the discussion to follow). GrapeVine for Lotus Domino and Lotus Notes is a groupware management

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system that helps companies classify and index corporate knowledge using charts and profiles. It is highly scalable and follows earlier release of its client/ server version. If a company has Notes deployed in its company, employees are already using it to exchange information about their projects, sales leads, and the like. GrapeVine attempts to discover and route existing information stored in Notes databases or on local area networks that it thinks company employees have an interest in. With this approach, users might even find the new project they are embarking on overlaps with one already in progress in another part of the company. Typically, grapeVine is the best solution for companies that rely heavily on Lotus Notes for messaging, collaboration, and document storage. The application sits on top of the Lotus Domino server and pulls information from Notes databases and other external files, and distributes the information to users through the Notes messaging system. Integrating grapeVine with Notes should be straightforward for a Notes administrator, but grapeVine does provide a consultant for three days (at no charge) to help with the details. Users familiar with Notes will take to grapeVine quickly. It is available from GrapeVine Technologies, Troy, Michigan (www.grapevine.com). Wincite is a Windows-based knowledge management system that has fast data retrieval, flexible report distribution, and graphing and support using an Oracle client/server architecture. The Wincite solution tackles the knowledge management system from a conventional approach. It is aimed at analysts who track market conditions, competitors, and other information. This tool organizes intelligence and distributes it to employees within a business. If analysts are knowledgeable, Wincite will amplify their aptitude; if their insight is less keen, it will distribute those findings as well. This solution approach to knowledge management requires in-depth customization by the vendor to define the content users want to track, design the dataentry screens, and set up the briefing books. Wincite consultants work on site for about six days and then complete the job back at the Wincite office. The entire process takes about one month. Because the Wincite approach to knowledge management is straightforward, it does not profess to find any hidden relationships within the data. However, it organizes what analysts feed into the knowledge base and distributes it throughout the organization. The knowledge base is easily built by linking Wincite to external files or inserting data directly into the system. Findings are consolidated into tailored reports that can be posted to a Web site or sent via E-mail. It can be obtained from Wincite Systems, Chicago, Illinois (www.wincite.com). Another knowledge management software package is KnowledgeX. When fed pieces of information, KnowledgeX can discern relationships that the user might otherwise overlook. For example, by feeding it with public documents about management and investment policies, one might reveal hidden relationships that might be indicative of how future management decisions will be made. In a similar manner, KnowledgeX is a worthwhile tool to track the career paths of

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the competition’s chief executives and display the results graphically. KnowledgeX displays its output either in a hierarchy or in a “spider web” form. Since KnowledgeX is easy to implement for users familiar with SQL databases, it requires little maintenance and lets users create some custom reports. It is available from KnowledgeX, Atlanta, Georgia (www.knowledgex.com). Other knowledge management tools are available. Sovereign Hill provides a search and index tool, a Web-access layer, and a sophisticated database that understands people, places, and things. The software parses the free-form data fed and distills it into the core relationships. For example, after supplying it with industry news, users can query Sovereign Hill about competitors. Its answer will not just incorporate hits on a competitor but also identify the relationships the competitor has with employees, customers, or competitors. It is available from Sovereign Hill Software, Dedham, Massachusetts (www.sovereignhill. com). KnowledgeShare (from Cambridge Technology Partners) is a client/ server–based system for developing and managing enterprise-wide repository on intranets. Lastly, Decision Suite 3.0 (from Information Advantage) is an integrated suite that lets a user navigate, filter, visualize, and share live analysis and information. Overall, these knowledge management tools are designed to help typical companies manage corporate knowledge effectively and disseminate it throughout the organization as needed to assist appropriate company employees.9 Knowledge Management Intranet Search Engines Knowledge management software that utilizes intranet search engines are the most basic and widely used tools for finding and accessing information housed on companies’ Internet-based computer networks. Many of these products came from earlier ones designed to locate and retrieve data from databases, file systems, and other legacy systems before intranets took hold across companies. Typically, search engines often make up just one part of a broader-based knowledge management product package. Typical intranet search engines are set forth below. The Knowledge Access Suite from Information Discovery (Hermosa Beach, California) was the first and only set of products to provide business users with a gateway to knowledge that has been pre-distilled from data and is stored in a Pattern Warehouse. Business users need not perform data analysis. They can simply query explainable knowledge on the intranet that has been automatically pre-mined. At the heart of the Knowledge Access Suite lies PQL: the Pattern Query Language, which is a pattern-oriented query language specially designed to provide business users access to refined information, just as they use structured query language (SQL) to access data. Originally a supplier to the CD-ROM market, the key to Dataware Technologies’ (Cambridge, Massachusetts) NetAnswer knowledge management technology is that it utilizes its own database. Once information is found, it is placed in the database, thereby eliminating the chance of it disappearing from a non-

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Dataware data source. The current Dataware NetAnswer release is a query and retrieval system featuring relevance ranking and natural language processing. It is compatible with major Web browsers and consists of a server and database. Excalibur Technologies Corporation’s (Vienna, Virginia) Visual RetrievalWare software was developed specifically for finding data in a multimedia age. The software is designed to index and retrieve text, photos, video, audio, and animation via Boolean language. It also offers statistical, semantic, and pattern recognition. Excalibur also has a product specifically for text searches. Another intranet search engine is one that is available from Fulcrum Technologies (Ottawa, Canada), which pioneered “brokering technology.” Brokering technology allows users to index information residing on networked computers and execute simultaneous inquiries across network resources. The key to the company’s Knowledge Network product is a feature called Knowledge Map, a folder hierarchy that shows users the information sources available to them through the corporate intranet. Knowledge Network consists of software that sits on a Web server and can be accessed via standard Web browsers. Information Dimensions (IDI) (Hermosa Beach, California), a spin-off from the Battelle Memorial Institute, first developed a product called Battelle’s Automated Search Information System (BASIS). IDI has since developed a BASIS Intranet package for UNIX and NT networks that includes a document manager, a Web server gateway, and client software. Another important company in this category of knowledge management intranet search engines is Verity (Sunnyvale, California). Verity’s flagship product is SEARCH’97, an application platform containing a group of tools designed for accessing information from many data sources across an enterprise. It features agent technology designed to proactively search, filter, categorize, and deliver information to users; database gateways that allow for cross-database searching; and an intranet spider for indexing data. It includes a user interface for the desktop, an information server, and an agent server.10 Data Mining or Knowledge Discovery Software With the proliferation of data warehouses, many traditional report and query tools as well as statistical analysis systems are using the term data mining in their product descriptions. In addition, some AI-based (artificial intelligence) systems are being promoted as data mining tools. The underlying objective of data mining is, above all, knowledge discovery. The methodology of data mining involves the extraction of hidden predictive information and resulting knowledge from large databases. However, with such a broad definition, many OLAP products or statistical packages, such as SAS, can qualify as data mining tools. For true knowledge discovery to occur, a data mining tool should arrive at this hidden knowledge without too much trouble; in most cases, be a part of the desired output. Essentially, data mining is the process of analyzing data and information

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within a multidimensional framework. Data mining allows different individuals to retrieve as much or as little data and information as they need. They can retrieve current detail data and information from within or outside the organization and summarize it by any desired category. Data mining products provide a basic analysis capability (minimum, maximum, and average) and the ability to drill down to obtain more detail and summarize details as necessary. Additionally, some data mining products go beyond basic analysis capabilities by providing statistical and mathematical routines to calculate the coefficients and powers of prespecified independent variables. This is known as curve fitting or trend analysis. Trend analysis is used to determine patterns and relationships and to find out if key measurements are still within their limits and expectations. It is not difficult to determine patterns when the variables are known. Mathematical techniques are available to determine the relationship of one dependent variable (such as sales of a particular model) to several independent variables (such as the selling price, competition, number of sales channels, sales of complementary products, inflation rate, etc.). However, the difficulty is identifying key patterns and trends when the analyst does not know the independent variables or, for that matter, may not even know what dependent variable should be analyzed. This is where more sophisticated and powerful knowledge-based techniques can be useful. A data mining product with knowledge-based capabilities can detect patterns in data and information that are not readily apparent. That is, it can indicate that there is a relationship between one dependent variable and one or more independent variables. Some data mining products utilize data visualization techniques to present these relationships graphically. The analyst can change the scale, display format, and present factors to represent the relationships better. The relationship need not be a causal one, nor is it most likely readily apparent. A typical knowledge discovery tool is KnowledgeSEEKER, which can analyze and understand the patterns and relationships as well as be an accurate predictive tool. Numerous examples of KnowledgeSEEKER can be found in a text by the author.11 A listing of popular data mining or knowledge discovery software vendors, their products, and their Web sites is found in Figure 4.2. Today, database vendors have integrated their products with the data mining capabilities of other vendors. This decision support strategy pairs the processing horsepower of relational databases with the discovery-driven analysis capability of data mining. This combination improves the scalability and performance of data mining applications and simplifies their management. For example, Brick Systems (Los Gatos, California) has integrated its RDBMS technology with data mining technology from Data Mind (Redwood City, California). DataMind Professional Edition provides scalable data mining software that builds a model from historical data via an automatic discovery process, thereby allowing business professionals to explore relationships in historical company data. To illustrate data mining software, consider, for example, a company’s gross

Figure 4.2 A Listing of Popular Data Mining or Knowledge Discovery Software Vendors, Their Products, and Their Web Sites

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margins, which are stored in a retail sales database. Gross margins fluctuate over the course of a year with a 10 percent increase between summer and autumn. A company executive might be tempted to conclude that sales margins generally increase from summer to autumn and that the increase in gross margin depends upon the season. In reality, there are many other potential variables housed in the database that could influence gross margin. These might include quantity sold, discount rate, commission paid, customer location, other purchases made, and length of time as a customer. If the discount rate is greater in the summer than in the autumn, the increase in gross margin could simply be a result of a lower discount rate and have nothing to do with a change in season. By removing the effect of discount rates on gross margins, gross margins might be found to be higher in the summer. In the final analysis, data mining examines all potential explanatory factors and associated data elements to ensure that the best pattern is retrieved from the data and that no potentially misleading effects are introduced into the chosen patterns. Going one step further, in place of showing the effect of only one condition, such as season, on gross margin, data mining can show the combined effect of a pattern, such as a particular time, location, and discount rate, that produces the maximum gross margin. By replicating that pattern, a longer-term strategy that will systematically increase gross margin and associated profitability can be established. That optimal pattern becomes a basis for predicting future trends. In addition, data mining vendors are cooperating with computer vendors. For example, several vendors, such as Information Discovery, NeoVista Solutions, Pilot Software, and SAS Institute, are linked with Hewlett Packard. They are part of HP’s OpenWarehouse Alliance Program, which focuses on selecting best-in-class warehouse component providers to ensure compatibility and to maximize functionality of HP computing platforms. Information Discovery System is a collection of client/server data mining tools that work with Oracle, Sybase, and Red Brick relational databases and also perform data mining on the Internet. This software takes advantage of the parallel processing technology of an HP 9000 Enterprise Parallel Server by decomposing its analysis and sending it to the separate processors automatically. In a similar manner, NeoVista Solutions’ Decision Suite Series provides comprehensive, enterprise-level, data mining solutions and associated professional services to Global 2000 companies. Products are based on a comprehensive suite of automated knowledge discovery software—the Decision Suite Series—designed to be highly scalable and to integrate easily with legacy and open systems decision support environments, such as HP’s Enterprise Parallel Server. Another data mining software product is the Pilot Discovery Server (a key component in the Pilot Software Decision Support Suite), which is a sales and marketing product that uses data mining to help users increase sales and acquire and retain customers. Seamlessly integrated within a large relational data warehouse, Pilot Discovery Server analyzes all available information in customer activity and demographics. The product runs on HP 9000 Enterprise. Similarly,

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the SAS Institute’s software—SAS/Insight and SAS/Spectaview—offers various tools for the exploration of data to uncover anomalies in the data tools for modeling and analytical tools, such as multidimensional analysis. SAS software runs on HP computing platforms. Business Intelligence Software versus Data Mining or Knowledge Discovery Software Because data mining or knowledge discovery tools are complex and sophisticated mathematical and statistical methods, they are different from but complementary to business intelligence software that tells decision makers more about what happened in order to provide a good understanding of a company’s operations. On the other hand, knowledge discovery focuses on telling decision makers why. With knowledge discovery tools, new patterns, trends, and correlations are uncovered by sifting through a large store of detail data. Guided by the way the problem is set up, the software discovers significant relationships that, many times, let decision makers get a better summarization of business results by examining the why of the problem under study. In the past, with most decision support systems, the software did not do the discovering; the user did. The user posed a hypothesis about the business, created a set of complex queries to test the hypothesis, and saw if the data supported it. This was a highly interactive and uncertain process that required a highly knowledgeable person. In addition, it could only answer the questions one knew enough to ask. In contrast, a knowledge discovery approach answers questions the individual does not know enough to ask. It identifies new categories of customers for targeted marketing or customer retention programs, discovers questionable billing practices and credit card frauds, and defines a profile of high-performing stocks, among others. Overall, business intelligence tools are capable of assisting decision makers to gain a better understanding of specific questions under study, while knowledge discovery tools are capable of uncovering patterns that can lead to discovering new knowledge. Typically, knowledge discovery tools sift through data for unknown relationships. Using these tools, the decision maker can, for example, come up with a model to find the most profitable customers. In turn, business intelligence software can be used to see what the impact would be if those customers were lost and how it would affect the bottom line as well as specific relationships to other functional areas of a company. OLAP Software OLAP software centers on multidimensional analysis, which is an architecture for performing computer analysis in new and different ways. As such, OLAP software packages can be used to uncover new patterns and knowledge about a company’s operations. Generally, this analysis centers on dividing data into the

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important dimensions that are useful in managing a company’s operations. Multidimensional analysis provides for the selection, analysis, summarization, and reporting by dimensions and attributes within dimensions. For the most part, this analysis divides data into the important dimensions used to manage the business. For example, dimensions for marketing applications might include products, market areas, distribution channels, and time periods. In turn, these dimensions can be used to describe each data point in the database, such as selling price, units sold, total revenue, and market share. Dimensions can be further described by attributes, such as location, size, or year. Attributes also can describe hierarchies within a dimension, even overlapping and inconsistent hierarchies. From an end user’s perspective, multidimensional analysis provides for the selection, analysis, summarization, and reporting by dimensions and attributes within dimensions. Multidimensional analysis can support virtually any timeseries application. These include forecasting and budgeting. Current spreadsheet, database, and reporting tool vendors are offering simplistic multidimensional tools. However, some of these vendors and others are offering more sophisticated tools. That is, all multidimensional engines are not created equal. It is necessary to look for engines that can work with any data warehouse, use meta data, and use any PC development tools for the user interface. Not only should the engine be robust to support an unlimited number of dimensions, custom groupings, and automatic and custom calculations, but the engine also needs to reside on a server large enough to support hundreds of users, large volumes of data, and intensive processing. The major OLAP software products include Arbor Software’s (Sunnyvale, California) Essbase; Comshare’s (Ann Arbor, Michigan) Commander; Oracle Corporation’s (Redwood Shores, California) Express; Knosys’ (Boise, Idaho) Knowledge Point; Microsoft Corporation’s (Redmond, Washington) SQL Server OLAP Services; and Pilot Software’s (Cambridge, Massachusetts) LightShip Suite. They exhibit some interesting and helpful features to users. For example, some software products handle up to six documents with ease. In addition to this software, some users are turning to alternative products because they are considered easier and faster to implement. For example, Business Objects from Business Objects (San Jose, California) is such a product, since it gives managers the ability to perform complex multidimensional analysis, thereby allowing them to follow lines of questioning on their own. As another example, Microsoft’s SQL Server OLAP Services, formerly code-named Plato, is available in the Mircrosoft SQL Server 7.0. It is built on hybrid technology, incorporating both multidimensional and relational approaches to OLAP, which Microsoft acquired from Panorama Software Systems (Tel Aviv, Israel). Microsoft’s OLAP Services includes features such as write-back, which enables “what if” analysis, member properties for additional queries, and distributed query resolution. These features balance query activity between clients and servers to minimize network traffic and server load.

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Statistical Analysis Software Although many of the current software packages have statistical analysis capabilities, there are those that are designed primarily as statistical analysis systems to uncover important relationships and trends. For a typical business situation, a series of data is used to see what the results look like when plotted under a certain set of conditions. Interactively, the set of conditions can be changed and new output plotted. Needless to say, there are a whole series of business problems that benefit from graphic solutions: project planning, breakeven analysis, learning curves, and so forth. Because a picture is often worth a thousand words, a thousand lines of printout, or 10 minutes of discussion, decision makers are able to absorb a tremendous amount of information as it is shown graphically. Because knowledge management systems make great use of computer graphics, they allow organizational personnel to build models and speculate with their computers using graphic output on how hypothetical decisions might translate into reality. A number of statistical analysis packages currently available include SAS (Statistical Analysis System) from the SAS Institute, SAM (Strategic Analysis Model) from Decision Sciences Corporation, SPSS from SPSS, STATS.II from CompuServe, and DISSPLA and Tell-A-Graf from Integrated Software Systems Corporation. SAS is used for most statistical applications that are widely found in business today. SAM has been used for expediting strategic planning by generating alternative future scenarios. SPSS is useful in market research studies; STATS.II centers on business statistics, forecasting, and econometric modeling. DISSPLA is a graphics software system with a high-level subrouting plotting language for producing graphs, surfaces, and maps. Tell-A-Graf enables users to call up graphs, charts, and plots using simple English-like statements. Some other packages, like Forecast Pro, have the capability to analyze data and information statistically. The software conducts the analysis that will determine which method (i.e., simple methods, exponential smoothing, Box-Jenkins, and dynamic regression) will do the best forecasting based on data and information supplied. There are also business graphics software packages that are complementary to statistical analysis packages. Lastly, it should be noted that knowledge discovery or data mining tools have a statistical orientation, since they help decision makers analyze patterns and trends, serving as relatively accurate predictive tools of the future. GUI/4GL Software Currently, there are a large number of GUI (graphical user interface)/4GL (fourth-generation language) packages that epitomize the term “client/server development tools.” At the outset, it should be noted that while today’s more advanced GUI languages have replaced the fourth generation in many situations, 4GL remains the tool of choice for many straightforward and basic administra-

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tive jobs, large reporting tasks, and screen setups that require page breaks, margins, and specific page formatting. These GUI/4GL packages offer a broad range of features and functionality. Ease-of-use and RAD (rapid application development) via iterative prototyping are important hallmarks of this group. While CASE (computer-aided software engineering) and “pure” 4GLs provide integrated support for many of the functions required for building complex systems (data modeling, version control, software distribution, interapplication middleware), GUI/4GL tools typically but not exclusively provide this same functionality by way of vendor alliances. Typical GUI/4GL tools include Powersoft Corporation’s PowerBuilder, Forte Software’s Forte, Uniface Corporation’s Uniface, and Oracle Corporation’s Developer 2000. Rapid application development is an integral part of PowerBuilder. Within the boundaries of its client-side OOP (object-oriented programming) paradigm, the applications are quite usable and maintainable. The developer can build screens and input some data, and thereby allow users to give feedback. In total, PowerBuilder is an object-oriented client/server development system that is quite useful for mission-critical Windows applications. Financial planning languages, such as Comshare’s Commander and Information Builders’ Focus, enable managers and their staffs to become competent in building and solving models in an interactive, exploratory manner. Typically, users of financial models are found in manufacturing as well as banking and finance, where corporate planning and finance are the largest user groups. Modeling applications most frequently used center on cash-flow analysis, short-term investments and debt structure, accounts payable, and inventories. Other typical uses include pro forma financial reports and financial forecasts, investment analysis, profit planning, sales forecasts, budgeting, merger-and-acquisition planning, and project models. It should be noted that 4GL tools are currently being impacted by several technologies, including client/server and the Internet. The Internet reinforces the need for a middle tier to service clients and interface to back-end databases and applications. The need is even greater for the Internet when it involves an extranet outside the company. Also, there is a drive toward component-based development that hurts 4GLs. Companies are looking to build and deploy applications across networks, and the standard for doing so is using components. The standard for component development looks to Java for the application logic and client-side logic and Common Object Request Broker Architecture (CORBA) on its server side. SUMMARY The initial focus of the chapter was on the advantage that comes from an organized delivery of business intelligence, followed by the first two essential elements of developing and implementing BI systems. The first essential element that was explored centered on upgrading current information systems to provide

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a BIS operating mode. The second essential element focused on the employment of appropriate software for developing business intelligence applications. These two essential elements of developing and implementing provide a basis for allowing decision makers the capability to solve simple to complex present and future problems and benefit from the corresponding opportunities. The bottom line in terms of an effective BIS environment is providing a means for decision makers to have a more thorough understanding of their operations, which was not found in previous information systems. The real question for decision makers is not whether they are busy but what they are busy about in terms of getting a better handle on their company’s operations. An effective BIS operating mode is in a better position to assist decision makers in getting at the real essence of their jobs. NOTES 1. Tom Coffing, “Teradata Territory: The Database of Choice,” DM Review, May 1998, p. 56. 2. Robert J. Thierauf, Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1993). 3. Robert J. Thierauf, Knowledge Management Systems for Business (Westport, CT: Quorum Books, 1999). 4. Robert J. Thierauf, On-Line Analytical Processing Systems for Business (Westport, CT: Quorum Books, 1997). 5. Robert J. Thierauf, Decision Support Systems for Effective Planning and Control: A Case Study Approach (Englewood Cliffs, NJ: Prentice-Hall, 1982); and User-Oriented Decision Support Systems: Accent on Problem Finding (Englewood Cliffs, NJ: Prentice-Hall, 1988). 6. Robert J. Thierauf, Group Decision Support Systems for Effective Decision Making: A Guide for MIS Professionals and End Users (Westport, CT: Quorum Books, 1989). 7. Robert J. Thierauf, Executive Information Systems: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1991). 8. Wayne Eckerson, “The Holy Grail of Business Intelligence,” DM Review, February 1998, pp. 18, 41. 9. Bill Ginchereau et al., “Knowledge Management Solutions: Knowledge Equals Power,” InfoWorld, November 17, 1997, pp. 116–128. 10. Chris Nerney, “Search Engines: Searching for True Knowledge,” Network World, June 16, 1997, p. 42. 11. Thierauf, Knowledge Management Systems for Business.

5 Data Warehousing and Computer Networking Found in Business Intelligence Systems DATA WAREHOUSING AND COMPUTER NETWORKING THAT UNDERLIE BUSINESS INTELLIGENCE SYSTEMS A number of well-known experts, such as Peter Drucker and Alvin Tofler, have been writing and talking for years about the principal role of the global economy, which is now fully upon us. Companies need all of the available help these days, not only from management but also from workers, starting at the lowest level. They need to establish bidirectional paths from customers to suppliers and strategic allies. Most of all, companies need first-hand business intelligence about what customers and prospects want, and they must be able to turn these perceived needs into successful products and services. As noted previously in the text, the best way to develop an effective business intelligence environment is to create a data infrastructure and a computer networking structure that ties in with current information systems, which are supported by newer BI software. By bringing together all sources of business intelligence and providing universal access to decision makers, it is possible to build an integrated business intelligence system environment. To be effective, this environment must reinforce the data and networking infrastructures already in place, tying them together and delivering a product that is greater than the sum of all its parts. Within an integrated BIS environment, there should be provision for supportive collaborative computing that supports workflow communication. Decision makers should be able to obtain information and knowledge from anywhere, in an easy-to-use approach, without regard for their sources and types. Within such an environment, decision makers should have the capability to “pull” business intelligence from data warehouses or have it “pushed” to them, using terminol-

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ogy they understand. An integrated BIS environment should be able to interpret important elements, text elements, and multimedia. To ensure 100 percent accuracy, it should be reliable, available, and scalable (RAS), thereby meeting the requirements of decision makers. To get a better understanding of an integrated BIS environment, the first half of the chapter centers on data warehousing of aged data and the need for a data federation system approach for real-time data. The types of corporate databases for developing a data infrastructure are examined. Basically, data storage supports analytical processing by providing integrated and transformed, enterprisewide historical data as well as real-time data from which to do appropriate analysis. This presentation also includes a look at data mining, which is the knowledge discovery process of extracting previously unknown, actionable facts from databases. In the second half of the chapter, the focus is on computer networking within a BIS operating mode. There are a number of topical areas covered, namely, enterprise portals, intranets, extranets, the Internet, the World Wide Web, and their tie-in with E-commerce. Today, all networking operations must be managed with greater levels of reliability and security than in the past. From this broad-based approach, it is necessary to develop and monitor a networking environment that really is conducive to acquiring and sharing business intelligence for decision makers at all levels of an organization. From Documents into Data Storage and Then to Information and Knowledge for Business Intelligence Today, the concept of “data management” is taken for granted. Basically, data management has two facets: (1) management of operational data by systems that process transactions (often in real time) and (2) the management and analysis of historical data by information systems that provide decision makers with insights. As this area has matured, it has become clear that decision makers do not want massive volumes of data, but they are interested in the patterns, trends, and other analyses buried within the data to obtain desired information and knowledge for use in business intelligence. These items need to be accessed, manipulated, and managed, just as data elements are managed. When data management is properly employed, the content of original documents is used to learn something more about a business and its competitive environment as well as to gain a better understanding of its operations. For example, lifting data off forms to populate a database is routine data capture. But by going a step further, business intelligence that emerges from the data can also be produced. From this perspective, the focus is on “added-value” applications. When document capture is applied to “feeding the machine,” an environment is created in which decision makers can innovate and react to market stimuli better, thereby discovering new business opportunities. Although most documents today are transaction oriented, in the future they will be intel-

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ligence oriented. For example, in the United States Patent and Trade Office, a document is causing a transaction because it is a submittal, but the future lies in extracting the essence that is encrypted in the document. The bottom line is that the real essence of capturing original documents is their ultimate utilization for business intelligence purposes. Need for Aged Data and Real-Time Data in Business Intelligence Building upon the above discussion, there is a need today for data warehousing to store aged data as well as for having real-time data available to decision makers. Inasmuch as organizations face many integration problems, and each technology approach addresses a different set of operating problems, the data warehousing approach has received the most industry attention for storage of aged data. However, the market will steadily move away from data warehousing for two reasons. First, business conditions are increasingly demanding real-time information, which data warehouses cannot provide. Second, few businesses have static integration requirements. As a result, the integration problem being solved varies with business conditions and external events. The bottom line is that companies are becoming increasingly concerned about their ability to respond to change. The need for flexibility and scalability is paramount to them. Overall, these two important reasons will push companies toward data federation systems, where they will require flexible, on-the-fly real-time data integration to compete and survive. The need to unify many of these data sources across the organization will drive companies to adopt federation systems for enterprise integration. There will be rapid growth in these systems’ adoption to support real-time business decisions needed by a company’s decision makers. Although decision makers have relied on data warehouses to shed light on the past, they will increasingly rely on data federation systems to make sense of what is happening now and in the future for effective business intelligence. Data Management on the World Wide Web to Facilitate Business Intelligence An effective business intelligence infrastructure today is related directly to data management (DM) on the World Wide Web. For the typical company, this means that the Web is no longer an architectural strategy but a necessity. Organizations increasingly want their business intelligence applications and infrastructures to operate in a Web-based or intranet-based environment. Web-based data management allows decision makers in any location to consume data, information, and knowledge and then collaborate on their shared results. Clearly, a Web-based approach to traditional data management offers attractive benefits for organizations. With platform-independent Web clients, decision makers can

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access data, information, and knowledge from any computer in any location and can collaborate in business processes. With three-tiered architectures, Web-based DM systems can adequately support large numbers of decision makers accessing the DM system through Web servers. Browser-based clients typically require no user training or special expertise, and they do not require extensive client-side administration and maintenance. In assessing Web-based data management functionality, these systems should let users do on the Web everything that can be done through a standard client, including library services, administration, and workflow. These systems should utilize solid back-end architectures and approaches designed to optimize the Web, including thin clients, three-tiered architectures, Web-based administration, and support for standards such as HTML, Java, ActiveX, and XML. As the Web explosion continues, organizations have accepted the Web not only as a data, information, and knowledge distribution medium but also as a platform for actionable BI applications. From this perspective, companies are looking for innovative ways to leverage their business intelligence resources for competitive advantage. DEVELOPMENT OF A DATA INFRASTRUCTURE A most important factor in an effective BIS framework is the development of a data infrastructure. Because the ability to get at vast amounts of data and its results provides that all-important competitive edge, a company needs to share these results across the organization. The bottom line is that layers of management can be eliminated and the organizational structure can be flattened. These flattened organizations can make and implement decisions quickly. Moreover, sharing data, information, and knowledge, along with the resulting business intelligence, with customers and suppliers helps break down the organizational boundaries. When people share and work together, teamwork is enhanced, and when employees are empowered to deal directly with their customers, the quality of service improves, thereby cementing relationships with the customers. Capturing an organization’s data and making it accessible to users across the company requires an investment of time and money. Not only must the right systems be put in place and the right applications be chosen for accessing the data but the company’s employees must also learn to understand and use the appropriate database tools. The issues involved in providing the right technology are complex. For example, in today’s newer structures, data, information, and knowledge for resultant business intelligence are closer to the individual. This means that the centralized information systems organizations are no longer the clearing houses, but rather provide guidance and standards while the line organizations of individual business units own the systems. Thus, the development of an appropriate data infrastructure must take this important fact into account. Essentially, the development of an appropriate data infrastructure for a BIS

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operating mode can reduce costs and increase profit margins. It enables the reorganization (i.e., reengineering) of business processes and functions. Moreover, the new BI infrastructure eases the development of strategic alliances, whereby companies and their suppliers form long-term relationships, or companies join together to provide a wider range of services than any one of them can provide on its own. To assist in the development of an appropriate infrastructure, there may be the need to develop very large databases, knowledge bases, data marts, and data warehouses. In turn, these approaches can be related to an organization-wide knowledge base and a data federation system. All of these important areas are discussed below from the viewpoint of the development of strategic alliances. Additionally, other information about data marts and data warehouses is found in this chapter as well as Chapter 6. Very Large Databases As companies grow and their systems expand to meet this growth, they are coming up against the limits of their current system architectures, in particular, relational database management system (RDBMS) software, hardware platforms, and manageability. On-line transaction processing (OLTP) systems, which have historically been kept small to achieve maximum transaction rates, are raising their ceiling limits. On-line applications are becoming more complex. For example, automated teller machines today offer customers access not only to savings and checking accounts but also to a full array of financial services. The net result is that improved service requirements demand that more knowledge about customers and products be placed on line to assist sales and support personnel. And as users demand more from these kinds of systems, their demands are driving up the sizes of OLTP databases to what is currently called VLDBs (very large databases). Currently, some experts define an OLTP VLDB as larger than 200 gigabytes and a VLDB data warehouse as anything bigger than 500 gigabytes. Managing a database becomes an issue for users when they go beyond the 100 gigabyte level for either an OLTP application or a data warehouse. When databases move into the hundreds of gigabytes, and even into the many terabytes, the management problem is compounded. Still, other experts state that a database is a VLDB when it gets so large that it is difficult to manage. Although very large databases can be of great help to management for distilling the realities of their operations in terms of specific knowledge, there is first the need to manage the VLDBs properly. It is expected that new VLDBs will differ from those of the past not only in their size and the platforms on which they run but also in the framework with which their developers undertake their construction. Some database developers are even beginning to plan for multipetabyte databases, driven by the incorporation of new information types, including audio/video, unstructured text, images, and spatial data, into traditional relational database frameworks. (A

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petabyte is 1,000 terabytes.) Some database developers predict that it will not take as long to go from terabytes to petabytes as it did from gigabytes to terabytes. Overall, VLDBs in this next generation are simply going to be quite a bit larger than their predecessors in order to house appropriate information and knowledge. Knowledge Bases and Corporate-Wide Knowledge Base Going beyond the above database approach in the development of a data infrastructure, a company may have several expert systems or knowledge base systems. As discussed in Chapter 1, expert systems use knowledge bases that consist of If-Then rules, mathematical formulas, heuristics, or some other knowledge representation structure to represent the knowledge of experts in a certain domain. Essentially, expert systems scan through their knowledge bases to find the appropriate rules, formulas, or some other knowledge structure to apply. The power of these systems derives from the knowledge they possess rather than from the inference mechanism employed. To ensure the desired performance from expert systems, the acquisition of knowledge becomes a vital task in their development process. Typically, maintenance of knowledge bases is extremely important because knowledge is usually much more dynamic than data or information. In some cases, knowledge is being changed on a daily basis and can be derived from many sources. Human experts do have problems in keeping themselves up to date with all the changes in the knowledge domain with which they deal. Also, some of the knowledge is not documented and not well distributed and that can complicate the matter. To assist in the maintenance of knowledge bases, it is essential to establish detailed plans. Generally, a methodology consists of planning, extraction, analysis, and verification. This orderly process can assist a company in assuring that its knowledge bases are current and relevant to the times. To better serve the interests of decision makers at all levels of the organization, there is a need for these multiple knowledge bases and others, along with databases, including data marts and data warehouses, to be integrated into a corporate-wide knowledge base. Their integration into a corporate-wide knowledge base is necessary in order to have a data infrastructure that supports decision makers at all levels of the organization. More specifically, there is a need to integrate appropriate data, information, and knowledge, no matter their source, including from a company’s functional areas (i.e., marketing, manufacturing, finance, and human resources), into a business intelligence operating mode so that decision makers can find what they want in one place. Data Marts and Data Warehouses Typically, a data mart is a subset of an enterprise-wide data warehouse. It performs the role of a departmental, regional, or functional data warehouse. As

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part of the iterative data warehouse process, the organization builds a series of data marts over time and eventually links them via an enterprise-wide, logical data warehouse. In contrast, a logical data warehouse contains all the meta data, business rules, and processing logic required to scrub, organize, package, and preprocess the data. In addition, it contains the information required to find and access the actual data, wherever it actually resides. An important element in both is the level of complexity and size. The complexity of the data model for a data warehouse normally increases in accordance with the number of lines of business it serves. On the other hand, a data mart, regardless of size, requires a far simpler data model, since it generally focuses on a single subject area. The subject of a data mart is generally determined by the department or line of business for which it is created. Typically, the first data marts that an organization develops are marketing and sales marts since the customer information they contain is essential to the welfare of the business. Currently, data marts are subdivided into two varieties: independent and dependent. An independent data mart derives its data from a number of sources and operates autonomously. A dependent data mart is fed data from the enterprise data warehouse and is essentially a subset of the warehouse. The nature of the organization’s business determines whether a data mart will be independent or dependent. Data Federation Systems Federated data architectures are currently being used. Just as the federated government divides power among parties or states, a federated data architecture consists of multiple, distributed data stores with varying degrees of affinity. The goal of a federated data architecture is to provide users with a unified view of distributed data sources. Typically, a data warehouse is just one of these distributed sources. The federated architecture may also include data marts, transaction processing systems, and external data repositories. In some cases, these architectures may also integrate XML content, file systems, document databases, and other sources of unstructured content. Overall, a federated data architecture consists of multiple, distributed data stores with varying degrees of affinity. It allows for global data access but local data autonomy. The balance of power in federated data architectures can change over time, depending on contemporary requirements. Based on these comments, an effective data federation system integrates various independent data systems. It combines aged and real-time data, while leaving the source data in place. It presents a user with location-transparent SQL (structured query language) so that the user can query (and update) a collection of databases as if all data were at a single location. The underlying structure of a data federation system is one that integrates multiple physical databases into a single logical one, letting locally controlled systems act with a global perspective. Data federation systems are different from data-access technologies,

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such as distributed database management systems (DBMSs) or gateway products, which retrieve data from multiple locations, yet require that local systems give up control and independence. Typical examples of distributed database management system products are IBM’s DataJoiner, Sybase’s Omniconnect, and Information Builders’ EDA/ SQL, which all rely on a central query optimizer to determine where operations will execute in the distributed system. As such, local administrators must relinquish control to this central scheduler. Aside from the loss of local independence, distributed DBMS products also suffer from severe scalability issues. This means that centralized work scheduling and query optimization will not scale beyond a handful of sites. There is no way that semi-exhaustive search algorithms can scale to a large network of several hundred machines, since there are too many ways a given piece of work can be accomplished.1 AN ENTERPRISE STORAGE SYSTEM—STORAGE AREA NETWORKS AND NETWORK ATTACHED STORAGE Another way of viewing a company’s data infrastructure is from an enterprise storage system perspective in order to make greater use of business intelligence. The concept is simple enough—that is, improve access to the large volumes of data residing in disparate storage systems. Unfortunately, business intelligence tools need ready access to that data, information, and knowledge. Storing all archieved data on a server can greatly tax system performance. Offering access to large volumes of data, storage area networks (SANs) provide high-speed network storage that is external to processing servers. Separating a SAN from the server greatly improves performance, since data storage and access tasks are removed. Generally, SANs connect with Fibre Channel allowing them to share access to multiple storage devices such as tape or RAID (redundant array of independent/inexpensive disks) systems. The concept of SAN breaks away from the traditional network environment. In the past, storage devices were attached to the network via an available server. This limited the data that could be housed on a particular storage device. Since the server would run a designated operating system, the storage device that was attached to that server would only be able to store information from that operating system. A SAN environment changes that concept entirely. Multiple servers with different operating systems can share the same storage devices. Going beyond storage area networks, there is network attached storage (NAS). Actually, SAN and NAS complement each other to form a complete enterprisewide storage system. NAS provides distributed storage for workgroups and departments by helping companies avoid having dozens of servers to administer and maintain. Still, NAS does not get rid of all administration costs. Network administrators still have to set up workgroups and assign permissions and passwords for each NAS device. There are also times when data needs to be made available to an entire enterprise. When there are more workers accessing a NAS

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device than reside on its subnet, it is time to build a SAN with multiple clustered servers and storage devices. SANs differ from NAS in that they are composed of storage devices and servers connected via high-speed network connections, usually Fibre Channel or possibly gigabit Ethernet. SANs can be server hosted where the software needed to operate the storage devices resides on one or more of the servers or they can be peer-to-peer where each storage device on the network manages itself with its own thin server. Clustered servers route data to users on a wide area network (WAN). SANs are scalable and extensible. They are solutions for companies that need large, centralized storage repositories for active archives or backups. On the other hand, NAS is for distributed storage for workgroups and SANs are best for centralized enterprise-wide use. Both complementary technologies have a place in the typical company within a BIS operating mode. Additionally, the power, reliability, and extensibility of storage area networks are of great importance to implement E-commerce in every industry segment. However, the level of complexity typical of E-commerce and SAN systems results in major challenges to management. Even more important, managers are discovering that being able to see and manage their entire solution’s performance is a critical success factor when it comes to realizing the benefits that a reliable, redundant SAN can bring them. ENHANCING DATA QUALITY OF CORPORATE DATABASES Related to the current directions in corporate databases is the need to address the problem of data quality in a BIS environment. Since data is collected from multiple data sources and stored in various databases, data quality problems may arise anywhere in the process. More specifically, problems can arise in the following areas: (1) the generation of the data, (2) the storage and management of the data, and (3) the final use of the data by decision makers who need data aggregation and interpretation. Each of these three roles is associated with a process or task that can involve data quality problems. To assist in meeting data quality requirements, an excellent way to improve data quality is preventing non-quality data from being entered into the database in the first place. An important benefit to eliminating the entry of incorrect data is reducing the costs of fixing the problems caused by non-quality data. Next, there is need to employ data cleansing, which is the process of extracting data from its most authoritative sources and conditioning or reconditioning it to a quality state. It includes analyzing data to discover its real meaning or use. To set up effective data cleansing and quality improvement processes, a welldefined plan needs to be developed that centers on an enterprise-focused data model that identifies authorized data sources. In turn, a schedule needs to be devised for propagating and transforming the data. Currently, a number of automated tools are available for data cleansing and

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data quality improvements. These products perform one or more of the following functions: rule discovery, analysis/audit, and data cleansing/scrubbing. In terms of the first function, rule discovery products analyze data in databases to discover data relationships and rules describing how the data is used. These tools help analyze data that is not well documented or whose usage has evolved from the original documentation. For example, WizRule from WizSoft is a lowpriced, easy-to-use rule discovery product that analyzes data from one or more files to discover both quantitative (formulas) and qualitative (relationships) rules. It also identifies exceptions that may be errors. Related to the second function, data quality analysis and audit products analyze data against a set of business rules to discover inconsistencies. Reports from QDB Solutions’ QDB/Analyze, for example, include exception reports and Pareto diagrams that graphically depict the number of violations by error type. A useful Metrics feature lets the user define a weight or cost for each error type so that Pareto diagrams show the financial impact of non-quality data. Statistical control charts illustrate the results of automated data audits over time. In terms of the third function, products that cleanse data and automate much of the tedious job of data cleanup are important when porting data from disparate data bases and software into a single data architecture. They analyze and standardize data, identify duplicates, and transform data to correct or probably-correct values.

Focusing on Total Quality Management Underlying data quality requirements in a business intelligence environment is total quality management (TQM). Total quality management provides a useful definition of quality that centers on meeting customers’ expectations. TQM also means “meeting” customers’ needs, not necessarily exceeding them. For example, the luxury automobile producer Rolls-Royce experienced financial difficulty in the early 1980s. Analysis revealed that Rolls-Royce was improving components that the luxury automobile customers felt irrelevant. This drove the price beyond what the luxury automobile customer (like myself, a current owner of two Rolls-Royces) felt was value for money. Overall, quality means improving those items that customers care about and are worthwhile. This statement regarding quality can be applied to data quality. To define data quality, one must identify the “customer” of data who needs data to perform his or her job. Data quality centers on consistently meeting users’ expectations through information and knowledge, thereby enabling the individual to perform his or her job effectively. Data quality exists when information and knowledge enables users to accomplish their desired objectives and goals. Data quality is measured not just by the immediate users but also by those users downstream. The bottom line is that total quality management focuses on customer needs that can impact a company’s competitive advantage.

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START WITH DATA MARTS To be successful in developing business intelligence systems, there may be a need to start small when developing these systems for any functional area of a company. From this view, vendors have been marketing products and services that make it easier and much cheaper to deploy scaled-down sources for generating business intelligence, known as data marts or mini–data warehouses. Their sales pitch is that data marts help put important business facts and figures into the hands of more decision makers. From this view, data marts can be the key to enhancing strategic decisions in all functional areas for a company. Essentially, data marts are subject-specific data warehouses, often departmental or a line-of-business, usually under 50–100 gigabytes (GB). As such, data marts are database server systems that make important business facts and figures easier for managers and their staffs to access, manipulate, and analyze. To do so, these systems must be able to extract operational data from transactional databases or other data sources on a regular basis, present the data for analytical processing, and deliver the converted information and knowledge to local servers for quick retrieval by decision makers. This process not only preserves the integrity of existing on-line transaction processing databases but it also makes important business data available to a wider array of decision makers throughout the company. It is not surprising that data marts are widely used in highly competitive industries where accurate analysis of sales trends and customer preferences are essential to the business. For example, a retail chain could use a distributed set of local data marts to let regional store managers analyze sales figures on a daily basis. In turn, the store managers could use these figures to decide which items to mark up or down and which to relocate to the front of the store for faster turnover. The essential difference between a data mart and a data warehouse (covered in the next part of the chapter) is scale. Data marts, which are typically smaller than data warehouses, comprise less than 100GB of data. They are usually optimized for a single subject, such as customer trends analysis or inventory management, and they reside on a server local to each workgroup. By providing dedicated performance for workgroups, departments, and small business units, data marts are often faster and easier to install and manage than data warehouses. Because data marts contain a subset of data for a single aspect of a company’s business, their focus is more on a functional approach to data warehousing. A Low-Cost Approach Using Data Marts Low cost and ease of development are the main attractions of data marts. For not much money, a pilot project can be put together so that the company can gain some experience. Vendors have descended upon the data mart market from every direction. The major vendors range from those who promote tools for specific functions to those who offer a data mart in a box. There are many newer

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vendors—Informatica Corporation (Menlo Park, California), Intellidex Systems (Waltham, Massachusetts), and Sagent Technology (Menlo Park, California). Veteran vendors include IBM, Information Builders, NCR Corporation, Prism Solutions, and SAS Institute. As the market matures, vendors are moving toward turnkey solutions, and analysts agree that no one vendor provides all the pieces of the puzzle. More recently, the World Wide Web has been used as a means to tap data marts. This approach is helpful to fulfill the potential benefits of data marts by providing an access platform that is highly interoperable, accessible, and affordable, and which eliminates the need to deploy business analysis software on each desktop through a company. As more data mart vendors support HyperText Markup Language (HTML), it becomes easier to share corporate facts, figures, and the like and mix and match them with related information and knowledge residing elsewhere on the Internet. Typically, using the Web as a data mart access vehicle provides an end-to-end solution. That is, it allows a company to open up data marts to employees at no expense when an intranet is in place. Because of the Web’s unique technologies, such as HTML and Common Gateway Interface (CGI), certain issues and limitations demand attention. Data mart managers should evaluate tools with careful consideration of performance, ease of use, and security to maximize and protect this potential information and knowledge distribution. Problems Facing Data Marts Currently, there are several problems facing data mart systems. First among these is that of query performance. The data mart server may sit virtually idle while users perform a variety of nonanalysis tasks, and then requests may all come in at once, all calling for similar data sets. Users can submit queries that are relatively simple to run, requiring the extraction of a single easily defined data set, or they can submit highly complex queries. At this time, many data mart vendors have failed to take into account this broad range of query demands. A related problem is a lack of tightly integrated tools to perform initial and continuing data extraction, data mart loading and presentation to users, and similar items. Hence, it is necessary to put together an array of products from a variety of vendors. This exacerbates performance and administration difficulties, thereby making data marts nearly as complex and time consuming as many data warehouses. None of the above problems are insurmountable. Using a multitiered, multithreaded architecture, data mart server software can speed query performance considerably. Within the multithreaded structure, the architecture can enable software agents to be dispatched to process users’ queries simultaneously. Additionally, these agents can be leveraged across connected servers to deal with unexpected surges in queries and updates. For complex queries, such software agents can “wait in line” on behalf of users, thereby speeding perceived per-

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formance by enabling users to proceed with other tasks as their queries are being processed. DATA WAREHOUSING WITHIN A BIS ENVIRONMENT Data marts, as discussed above, can provide a starting point for decision makers to discover usable business intelligence throughout an organization. In turn, intelligence about a company and its operations can be further expanded using an enterprise-wide approach that ties in to its widespread database. From this view, reference can be made to data warehousing that serves as a delivery mechanism to provide integrated business intelligence to a company’s decision makers. Although this data warehousing concept has been around for some time, only recently has its implementation become truly realistic. It is based on the use of specially designed electronic databases (i.e., data warehouses) and a series of powerful tools to extract data from original sources and convert it into valued facts and figures for decision makers. In effect, decision makers who gather data from several sources in order to answer a critical business question or questions using business intelligence about a company’s operations are generally engaged in some form of data warehousing. Because the data warehouse is designed to serve the specific business intelligence needs of decision makers for an entire company, it is necessary to store data at different levels of granularity—from current detail data to highly summarized reports. Generally, the more current the detail data is, the more immediate is its use. Current detail data supports day-to-day decisions, while historical data supports trend analysis and long-term decisions. As such, a data warehouse combines various data sources into a single resource for user access. Users can perform ad hoc queries, short- to long-term analysis, and trend plotting against the warehoused information and knowledge. Typically, data warehousing involves combining products from a variety of technology vendors into an integrated solution. One of the requirements of data warehouse design is the ability to accumulate and manage large amounts of data. Thus, it is important to choose levels of granularity and summarization for the data in the warehouse. Other design considerations for managing large amounts of data in the warehouse are storing data on multiple-storage media, summarizing data when detail becomes obsolete, encoding and referencing data where appropriate, and partitioning data for independent management and indexing. Justifying the Data Warehouse Since data in the warehouse is organized by subject rather than by application, the data warehouse contains only that which is necessary for decision-support processing. The data is collected over time and used for comparisons, trends, and forecasting. Also, this data is not updated in real time, but rather is refreshed

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from operational systems on a regular basis when the data transfer will not adversely affect the performance of operational systems. In light of these comments about the operational aspects of data warehouses, the issue of their justification comes into play. Basically, the justification process revolves around identifying key business objectives and goals the data warehouse will meet for a company’s decision makers. These objectives and goals include increasing national and international sales, lowering the rate of chargeoffs of accounts receivable, and improving the impact of advertising expenditures. If the data warehouse, for example, can help the company generate more sales, then it can serve as a model for achieving other business objectives and goals as well. Going beyond this initial reason for data warehousing, there are a number of other important reasons for developing a data warehouse. A data warehouse: • allows a company to become more competitive • improves the decision-making capabilities of company personnel at all levels • helps company personnel to identify hidden business opportunities • improves productivity through improved access to information, knowledge, and intelligence • improves customer responsiveness to company promotions • speeds retrieval of company information, knowledge, and intelligence • lowers the cost of access to information, knowledge, and intelligence

Typically, data warehouses are updated on a daily, weekly, or fortnightly basis. In this manner, the warehouse helps users make complex queries about a business without slowing the high input. Users (i.e., corporate executives, managers, and analysts) employ data warehouses to analyze historical and operational data. For example, a bank executive could use a data warehouse to interpret the profiles of credit card applicants over the past years. Or a sales manager might need a history of sales orders to help launch a new marketing program. Or a corporate planner could analyze strategic information that provides the foundation for the company’s five-year strategic plan. The data warehouse has even helped some companies downsize. According to a survey of 250 information systems professionals by the Meta Group (Stamford, Connecticut), the most common applications of data warehouses were: (1) customer information systems—26%, (2) marketing—24%, (3) finance—22%, (4) sales—12%, and (5) other—16%.2 Some of the companies building and implementing data warehouses include insurance companies, telecommunications companies, financial services firms, pharmaceutical companies, and hospitals. Their common characteristic is the need to build better information systems about their customers to better track and direct their businesses. Additionally, data warehousing has been helpful to companies wanting to improve their customer response times. Saving time in this area may be an

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important key to controlling short- to long-range costs. To achieve lower costs, data warehouse architects should consider data warehouse tools that link operational databases with the warehouse. In addition, they should consider using tools that extract, update, and replicate information as well as help users query databases. In this manner, a number of useful data warehouse applications can be developed that do assist in making managers and their staffs more effective in their jobs. Data Warehousing and the World Wide Web A Web-enabled data warehouse is a means of providing access and query availability to a company’s data via a standard Web browser. More specifically, it means that users can perform ad hoc queries and obtain specific reports against the database via their choice of Web browsers. Data warehouses combined with the Internet and the World Wide Web have the potential to transform business processes and capture the untapped value of enterprise intelligence. While most businesses understand data warehousing technology and have implemented it, high upfront expenditures, including warehouse design, hardware, and desktop software as well as data integrity, middleware, and connectivity, have kept many organizations from fully exploiting the benefits of data warehouses. However, the Internet and the World Wide Web eliminate some of the these concerns. Data warehouses and the Internet are now converging, which will finally make data warehousing available to budget-conscious organizations. The Internet and the World Wide Web are transforming every aspect of developing and deploying global information technology. Data marts and data warehouses offer users a more manageable opportunity to capitalize on their intelligence assets. Essentially, the World Wide Web provides inexpensive, universal connectivity, thereby allowing companies to deploy instantly reports to customers and partners around the world as well as to company employees. The Web also eliminates the overhead involved in distributing and maintaining client software. Accessing a data warehouse using the Internet is so compelling that software developers have integrated RDBMS-driven technologies, including parallelscalable hardware and large volume enterprise-scale data warehouses with the services of the Web. The results are that systems leverage the best of the relational world, while giving business users the capability of multidimensional analyses. A Web-centric data warehouse builds on a model that gives users an easy and accurate way to browse, analyze, and present and communicate the results that they need. Then it takes the requirements a step further by providing powerful presentation capabilities to convert that data into business intelligence that is truly useful. Such systems make it very easy, for example, for users to integrate business intelligence into documents, such as reports, memoranda, and spreadsheets, for electronic distribution within an organization, overcoming the publication and communication obstacles associated with decision support.

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Data Warehousing Software Before focusing on representative data warehousing products per se, it is wise to state that most data warehousing software products currently focus on one of the three areas: (1) acquisition, (2) storage, and (3) access. Most software vendors that provide these products have a proven track record for performing one of these functions well. Generally, most have simply retrofitted existing products to meet warehousing requirements. In the first category, there are a number of vendors who have products designed to manage and automate the acquisition process. Some of the data acquisition product vendors have begun to integrate several of their products into complete warehouse offerings. The largest vendors in this category include Prism Solutions, Carleton Corporation, and Platinum Technology. In the second category, there are a number of storage software products. A representative listing is found in Figure 5.1. Currently, firms like Oracle, Sybase, IBM, and Informix control the bulk of the market. When a typical Unix server or mainframe computer seems incapable of handling the envisioned workload, MIS executives look to SMP (symmetric multiprocessing) or MPP (massive parallel processing). For the truly large jobs, SMP may not be enough. In those cases, some companies are turning to MPP machines. Although SMP machines can handle up to 16 additional processors at a time, MPP systems incorporate the use of dozens or even hundreds of processors. The power of these machines cannot be approached by any other hardware on the market today. The third category is access software products. First, unlike the overly simplistic report writers of the past, query facilities tools turn a large, complex data warehouse environment into a friendly, well-managed workstation. Second, access software tools center on statistical analysis as found in products like SAS and SPSS. Third, a number of data discovery products are getting a lot of attention. These include decision support tools plus artificial intelligence and expert systems. Using neural networks, fuzzy logic, decision trees, and other tools of advanced mathematics and statistics, these products allow users to sift through massive amounts of raw data to discover new, insightful and, in many cases, useful things about the company, its operations, and its markets. In addition to the three areas above, representative data warehouse software typically includes data visualization and on-line analytical processing. Data visualization represents a graphical rendering of information from data warehouses. As such, data visualization software products bring graphical representation to new heights. A popular visualization tool that falls under this group is geographical systems. Such systems turn data about stores, individuals, or anything else into easy-to-understand, dynamic maps. On-line analytical processing (OLAP) centers on multidimensional analytical tools for accessing, storing, and manipulating decision support and EIS (executive information system) style information. These tools represent a whole new

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Figure 5.1 A Listing of Popular Data Warehouse Vendors, Their Products, and Their Web Sites

generation of high-powered, user-friendly data investigation systems that allow users to look at information from numerous different perspectives. OLAP software products provide the capability to slice and dice reports dynamically, and to look at the same kinds of information from different perspectives. For example, at the press of a button, a product manager is able to view sales figures for a given product at the national level, view them broken down by division, drill down to view figures by territories within a division, check sales numbers for each store in a territory, and then compare them against sales of stores from a different territory. This last category of data access products can enrich a decision maker’s capability to ask and answer important questions facing him or her on a daily basis.

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Employment of Data Mining Tools to Better Understand a Company’s Operations The purpose of data mining, as discussed in the prior chapter, is to sift through large amounts of data to discover meaningful relationships (i.e., patterns, trends, rules, etc.) that were previously unknown and then use the resulting business intelligence to make crucial business decisions. So the question can be asked: What exactly are the data mining analytical elements that produce this meaningful business intelligence for better decision making? Basically, data mining is a set of dissimilar and independent analytical tools, such as neural networks, decision tree algorithms, logistic regression, multiple regression, fuzzy logic, genetic algorithms, clustering, market basket analysis, and other analytical methods. All of these techniques are designed for a certain purpose in the analytical arena with their goal in mind, their own set of assumptions, their own data structure requirements, their own range of applicability, and their own ways of interpreting results. In terms of a company’s data marts and data warehouses, data mining aims to discover something new from the facts recorded therein. For many reasons— encoding errors, measurement errors, unrecorded causes of recorded features, and the like—data in a database is almost always “noisy”; therefore, inference from databases invites applications of the theory of probability. From a statistical point of view, databases are usually uncontrolled convenience samples; therefore data mining poses a collection of interesting but difficult inference problems, thereby raising many issues, some well studied and others unexplored or at least unsettled. Data mining almost always involves a search architecture requiring evaluation of hypotheses at the stages of the search, evaluation of the search output, and appropriate use of the results. Statistics has little to offer in understanding search architectures but a great deal to offer in evaluating hypotheses in the course of a search, evaluating the results of a search, and understanding the appropriate uses of the results. To better understand the various facets of data mining, reference can be made to the Mellon Bank Corporation (Pittsburgh, Pennsylvania). Increasing revenues, reducing risks, and maintaining a competitive edge require sophisticated tools and techniques. This bank has been using data mining for its marketing activities for years and has recently expanded its use to prevent fraud, delinquency prediction, and business process reengineering, including streamlining loan underwriting. But its most significant use of data mining by far is managing its complex customer relationships. Its overall strategy is to provide superior customer service. The bank found that without data warehousing, data mining is an ad hoc special project requiring a fair amount of programming and other efforts to pull data from various sources. More recently, Mellon has invested significantly in data warehousing, building its enterprise warehouse in DB2 on OS/390. It now makes data mining a normal part of its operation. The bank uses Business Ob-

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jects on an NT Web Server to access the DB2 data warehouse on the OS/390. Although several vendors provide attrition models, the bank decided data mining was an important part of its business strategy and wanted the capability to build its own models. Therefore, it has a joint project with IBM, focusing on a broadbased multiplatform data mining tool, called Intelligent Miner for Data. This software package enables users to mine structured data stored in conventional databases or flat files. The software tool searches for hidden information stored in databases, data warehouses, and data marts. The bank deploys data mining tools on an ongoing basis. The organization already had the basic data mining units, so when it was given another tool, its users could do their jobs better and more efficiently. As with any tool, users have to learn how to use it, but adopting Intelligent Miner required no major shift in job responsibility.3 Companies Using Data Warehousing and a Data Federation System as a Strategic Weapon To place common applications of data warehousing and a data federation system into focus, reference can be made to the Fortune 500 companies that have launched some form of data warehousing initiative. For example, WalMart has been using its data warehouse to determine what items need to be on the shelves, at what stores, and at what price. It is difficult to find a major player in the retail industry that is not attempting to emulate Wal-Mart’s data warehousing successes. In a similar manner, it is difficult to find a federal agency or a state government that is not looking at data warehousing to play a major role in addressing many of their problems. The impact of data warehousing has also been felt in most other industries. For telecommunications providers, MCI is leading in making the transition from a product-driven company to a client-driven company through the analysis of customer preferences and needs. CVS delivers a global view of its pharmacy operations to all levels of the organization that have a need to know. MasterCard, using the World Wide Web, empowers its member banks to analyze credit card data for highly targeted one-on-one marketing. Bank of America accurately anticipates fraud and takes appropriate preventive action. R&V Insurance in Germany has found the key to integrating information about its lines of insurance into a common, accessible data warehouse. And Hughes Electronics has revolutionized its buying patterns and cut costs by aggregating all its purchases from individual vendors. All of these examples are focused on data warehousing in terms of the need for information and knowledge. In all of these examples, strategic thinking benefits from information and knowledge, but the more powerful strategic ideas are demanding it. Until recently, intranets and extranets were basically stores of large volumes of information, much of which is static corporate communications materials such as telephone listings, internal memos, corporate communique´ s, and similar documents. Today, because intranets and extranets have become more pervasive,

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the kinds of intelligence on them has changed and expanded. Companies use their intranets and extranets to give users dynamic access to the information and knowledge in their databases and data warehouse that can help them improve the speed and quality of their business intelligence. An important goal of a data warehousing strategy that includes the Internet and a company’s intranets and extranets is to ensure that the intelligence derived from it has value and that a company and its clients and suppliers can use it when and where it is needed. For the typical data warehousing manager, Webbased distribution reduces the complexity and delay associated with supporting remote users. New Web-development tools automatically link Web pages to databases or data warehouses. Among the key tools are those based on Sun Microsystems’ Java language, which is embedded in the latest version of Netscape; Oracle’s Web-enabled tools; and Web-enabled ROLAP (relational online analytical processing) tools from Dimensional Insight, Information Advantage, and MicroStrategy. Because both the Web and data warehouses have a common goal of data access, newer releases of query tools will be Webenabled so that Web-browser software is able to access data warehouses. In fact, innovative user companies, like MasterCard, are already building such interfaces between their applications. MasterCard now offers banks access to its customers’ buying habits, for instance, based on their credit card transactions. Additional information will be given later in the chapter on the use of browsers for the Internet and a company’s intranets and extranets. COMPUTER NETWORKING WITHIN A BIS ENVIRONMENT Computer technology has moved and continues to move in the direction of networks to support mission-critical and newer applications, including business intelligence systems. From this view, the complexity of tools and processes, along with people that are needed to build, operate, and monitor the networks, have become visible to more and more computer managers and users throughout the organization. The way an organization approaches the design, implementation, and operation of networking can make or break the quality of its network services. More than ever, networks of all types are at the very heart of a company’s operations, whether they are LANs, MANs, WANs, intranets, extranets, linkage to the Internet and the World Wide Web, or a combination of these. They must be managed with greater levels of security, reliability, and service quality today as well as into the future. In addition, there is a need for enterprise portals that allow users to link data, information, and knowledge with their collective insight, values, and experience. Both of these critical areas essential for an effective BIS operating mode are covered below. Overall, the business intelligence derived from a company’s networking infrastructure is the very essence of a company’s effectiveness and productivity.

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Integration of Client/Server Architecture with the World Wide Web Going beyond the basics of networking useful within a BIS environment, it is helpful at the outset to take a look at a client/server architecture that is tied in with the World Wide Web. In traditional enterprise computing, a client machine is typically a PC that runs a highly complex operating system and uses much of its substantial supply of memory and local storage to house an assortment of resource-intensive applications, including E-mail and other network access software. These are “fat clients” and they tend to be high in costs. By comparison, the kind of “thin client” that Java computing makes possible costs substantially less. Part of that substantial price difference can be attributed to the original price tag: thin Java clients need less local storage, since all code, data, and configuration information are stored and managed centrally. Most of today’s client/server tools, such as decision support and knowledge management, perform most processing functions on the client, which leads to heavy network traffic and poor response time. Performance of client-centric tools degrades further when they are used to access large corporate databases, execute complex queries/reports, or serve a large number of users. The solution is a three-tiered architecture that partitions data access and reporting functions to take advantage of the respective strengths of clients and servers. Many of today’s fat client tools, which place most processing on the client computers, work well with small databases but fail in medium- to large-scale deployments. Enterprisewide knowledge management systems and data warehousing, for example, require a different approach. A three-tiered architecture addresses these issues by optimally partitioning the processing among the clients and servers. The first tier (the client) is used primarily for creating and defining queries and reports. One-server tier processes queries and formats results while a second server tier hosts the database. The two server tiers can reside on a single server or be divided among two or more servers. The three-tiered approach leverages powerful servers to perform query and report processing, while taking advantage of client capabilities for creating, defining, and viewing queries and reports. Restating the three-tiered architecture within the context of the World Wide Web, Web browsers become a new frontend option, another way to present the application. Existing database and transaction processing resources remain in place, and the Web server becomes the middle tier between the two—distributing queries and requests from Web users to the database and the transaction processing resources of the organization. Hence, it is necessary to rethink the entire application infrastructure just to accommodate the Web. It is anticipated that Web browsers accessing multiple Web servers is the architecture for the next wave of client/server computing.

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Enterprise Portals within a BIS Operating Mode An enterprise portal (EP) is also called a corporate portal (CP) or an enterprise information portal (EIP). It is a window into the myriad data streams, applications, networks, and processes that make up an enterprise. It is a layer of technology that unifies the data, information, and knowledge found in an organization via a single point of access. Typically, the synthesis of one, two, or three of the myriad transactions occurring during the course of a day could have incredible value if they are shared. The convergence of those transactions and their resulting intelligence streams could result in some insight that, shared through an enterprise portal, could give someone else an insight not had before. On the public Internet, an enterprise portal employs a profile of the user’s information requirements and the services of a search engine to help the user to find information quickly that matches his or her needs. An Internet portal provides the consumer with a single interface to the vast network of servers that constitute the Internet. A typical enterprise portal in the corporate environment has a similar objective: to provide business users with a single interface to data, information, and knowledge scattered throughout the enterprise for the purpose of distilling business intelligence. Such enterprise portals fall into two main categories. A collaborative processing enterprise portal helps users organize and share workgroup items, such as E-mail, discussion group material, report memos, and meeting minutes. A decision processing enterprise portal, on the other hand, helps managers and business analysts access corporate data, information, and knowledge as well as their resulting intelligence for making key business decisions. This type of enterprise portal supports a wide range of different types of corporate business data, information, and knowledge and offers significant potential to organizations to leverage the resulting intelligence for business benefit. Essentially, enterprise portals provide the central launching point for corporate decision processing and content management applications. Their primary focus is to connect users with structured and unstructured content relevant to them. They take the extremely popular and useful concept of a Web portal as a single entry point to a host of disparate Web-based resources. The basics of an enterprise portal are simple. A single Web page serves as a starting point, hiding the complexity of sometimes convoluted enterprise information technology infrastructures behind an intuitive, point-and-click graphical interface of a Web browser. Today’s portal technology and business-to-business (B2B) technologies, for example, embody the principles of business intelligence. It is not just a migration from one label to another. Now that there are tools that can manifest BIS principles by making them visible and tangible, it is possible to put together a value proposition for businesses. That was something computer professionals had a difficult time doing with so many fragmented components scattered about. It is now possible to connect users not only with all the data, information, and knowl-

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edge they need but also with everyone they need. Overall, enterprise portals within a BIS environment are a natural extension of E-commerce as well as Internet and intranet technologies. AN INTRANET IN A TYPICAL COMPANY With the phenomenal popularity of the Internet (as discussed above as well as in the next section of the chapter), it is viewed today as a messaging model. Related to meeting the needs of organizational personnel is an “intranet,” a private-network version of the Internet or simply internal Internets, modeled after the global Internet and its World Wide Web. Intranets employ the hypertext and multimedia technologies used in Web pages, but for applications internal to an organization. They provide a standard browser-based window in which all information is displayed the same way, and all processes interface the same and work the same. As such, intranets enable companies to make massive amounts of information and knowledge available to their employees locally and remotely and to improve their messaging strategies. Typically, intranets are erected to support one or more publishing applications that, while not mission critical, offer a real payback. For example, staff directories, product data sheets, employee handbooks, and other human resource materials fit into this category. Essentially, these are read-only data sets where user response is not required. These applications can be easily achieved at low cost. On the other hand, more ambitious applications use Web pages linked to backend applications that retrieve or store data in databases. For example, many companies are building Web-based human resources applications that allow staff to check on vacation entitlements and other benefits. Overall, intranets change the way a company thinks about information access and distribution as well as transform network infrastructures. Essentially, a typical intranet’s key to creating a boundaryless enterprise lies in sharing not only information and knowledge, but also processes across the entire value chain of activities, from production to consumption, through a single universal interface, thereby collapsing transfer times and creating a new level of intimacy with customers, suppliers, and teams. Within this context, there are four basic classifications for intranets: 1. document-based—concerned with the archival and/or distribution of documents 2. process-based—focused on automating processes within a company 3. commerce-based—used to facilitate business-to-business information and knowledge exchange or useful as a replacement for EDI 4. knowledge-based—concerned with the management and sharing of a company’s centralized and decentralized knowledge bases

When a company designs and implements an intranet, there is a need to decide whether the intranet will use an internal wide area network (WAN) and local

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area network (LAN) as the communication path between the employees and the intranet. If so, it is necessary to be aware of the possible traffic load that may be generated and determine if the WAN/LAN connection can handle the increased traffic. If a company decides to use an external network, such as the Internet, as the communication vehicle, there are concerns about the security and privacy aspects of the intranet. With company information flowing around between the employees at their PCs and the Web server connected across the public Internet—perhaps information such as a company’s policies and procedures—it is possible that outsiders could tap into the information and use it for their own purposes. Essential Elements of an Intranet The main building blocks of an intranet and how they are related to the Internet are shown in Figure 5.2. More specifically, a typical intranet consists of servers, databases, browsers, firewalls, and dynamic HTML pages. At the heart of most intranet systems are Web servers. These simple but effective information distribution mechanisms are becoming increasingly sophisticated as vendors extend and enhance their basic functionality. Intranets can also involve the use of TCP/IP (transmission control protocol/Internet protocol) as a transport and a number of TCP/IP applications that range from file transfer to audio and video distribution. As shown in the illustration, Web servers are integrated with databases linked to mainframes and other legacy systems and provide workflow services. Combined with the high bandwidth capacity of corporate data networks, intranets allow a company to capitalize on advanced features, such as real-time audio and video as well as collaborative applications and 3-D data representation. A client browser is the means by which the user interfaces with the intranet. The most sophisticated browsers include Netscape’s Navigator, Microsoft’s Internet Explorer, Attachmate’s Emissary, and Sun Microsystems’ HotJava. These products allow the user to support advanced presentation techniques and a whole range of add-on products. In contrast, a firewall is the main line of defense. It is essentially a computer connected to both the corporate network and the Internet. Once companies create direct links between their private networks and the global Internet, their internal network is open to attack from anyone. As such, the need for a firewall is paramount for security reasons. Additionally, there is generally a need to convert the scanned documents into a text format suitable for Web access (i.e., a format known as HyperText Markup Language, or HTML). Beyond these basic building blocks, there are a number of specialized tools to help a company build an intranet. There are videoconferencing systems, such as the low-cost VideoPhone from Connectix Corporation (San Mateo, California), Web-based bulletin boards, such as the WebBoard product from O’Reilly & Associates (Sebastopol, California), and conferencing systems, such as

Figure 5.2 The Building Blocks of an Intranet and Its Relationship to the Internet

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OpenMind from Attachmate Corporation (Bellevue, Washington). VideoPhone could be used for meetings, even over slow WAN links, while WebBoard could be used to maintain discussion areas for workgroups or the entire company. While finding products is easy, deciding what is best for a company’s intranet can be difficult. The key is to employ those products that address clearly identifiable needs, offer a quick payback, and are simple to implement. The most important benefit of an intranet is in the area of new methods of information distribution and collection, new opportunities to involve staff in the process, and the cost benefits of recentralizing some of those functions that are difficult to manage in a distributed environment. Companies that build intranets will find that information publishing becomes a way of corporate life and results in improved internal communications and a more effective community culture. Overall, intranets are here to stay and, as shown in Figure 5.2, can be linked directly to the Internet. Using an Intranet within a BIS Environment Intelligence-based intranets can serve as a means to access databases and generate reports and other information and knowledge. These intelligence-based intranets tie together intelligence otherwise locked up in disparate applications. To illustrate, HBO (Home Box Office), a division of Time-Warner Entertainment, provides programming via both the HBO and Showtime cable networks to nearly 30 million viewers throughout the United States. HBO uses an intranet to provide enterprise-wide access to information and knowledge needed to make informed and intelligent TV programming decisions and to assist its sales force in selling its premium television services. In the past, the sales force would learn of new marketing campaigns once a month, when HBO’s marketing department sent the large boxes of marketing materials, video tapes, and other collateral to its 200 to 300 sales representatives nationwide. Now this information is rolled out via the intranet, eliminating costs for printing and video duplication. Going one step further, the intranet provides the sales force nearly instantaneous access to time-sensitive market intelligence. In contrast, it took weeks for sales representatives to receive this under the previous system. From the company’s intranet, HBO employees are able to access crucial information on virtually every movie ever made (including those in the process of being made) that lies within the company’s corporate database. The database tracks various information related to each movie, including its cast, director, distributor, gross earnings, and details on HBO’s rights to sell it. It is also linked to a Nielsen database that tracks ratings of each show. Previously, this information could be accessed only as paper reports. With HBO’s intranet, users can execute SQL queries to the database from within HTML forms. The server then dynamically generates a graphical representation of the requested information. HBO’s ultimate goal is a single point of access to all the line-of-business information and resulting knowledge spread throughout the organization, thereby

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providing one common interface to all corporate information and knowledge, including such items as network programming, HR policy manuals, the employee database, and organization charts.4 THE INTERNET AND THE WORLD WIDE WEB Over time, the Internet has been called by different names, among them are the information superhighway, the digital expressway, or just plain cyberspace. It utilizes the World Wide Web, which is a vast, interlinked network of computer files from all over the world that a user can access by clicking on a mouse. The Web is the place on the Internet where organizations post a home page. The Web can be thought of as the Internet’s multimedia subuniverse. CompuServe, Prodigy, and America Online all feature the Web, as do many smaller services. A Web “browser” is essentially the software that gets the user from file to file. Although there are several currently being offered, the most popular one is the Netscape Navigator, which the user can download free of charge. The Internet started out as an ad hoc computer network created in the late 1960s by the Defense Department. Fundamentally, two events converged to create the Internet: (1) technology and (2) necessity. The technology was the packet-switched switching. The necessity was the perceived threat of thermonuclear war. That is, the U.S. Air Force was interested in building a computer network that stood a chance of surviving a major attack. Therefore, they built a network without a central control point so that it could lose one, or several, computers and the computers remaining could still communicate with one another. This led to the creation of the ARPANET, the progenitor of today’s Internet. HTML and VRML Used in the World Wide Web To better understand the Internet using the World Wide Web, a comparison can be made to a fax. When one sends a fax, the page sent is actually broken down into light and dark images. This graphical method of transmission is inefficient and slow, and does not work with colored images. The World Wide Web, on the other hand, uses HyperText Markup Language (HTML) to break up a document into simple text elements that are reconstructed by the “browser.” The browser is what makes the Web possible. The first browser, NCSA Mosaic, has spawned dozens of competitors, all of which are vying for market share and acceptance. Standards are lagging way behind the release of new language enhancements and browser developments. The result is that every one of the 30 or more available browsers will display a document differently. HTML 4.0 is the latest published standard. It builds on Version 3.2 by adding new elements, deprecating some elements, and obsoleting a few others. It should be noted that some HTML standards are not supported by all browsers. In an effort to spruce up Web pages, there is the Virtual Reality Modeling Language (VRML), a standard-file format for describing virtual worlds. It is

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designed to bring real-time interactive three-dimensional graphics to the Internet via the World Wide Web. VRML allows prospective world-builders to sit down at their keyboards and build full-fledged 3-D environments, then make their creations available over the Internet. Users running Web browsers can click on a link that takes them into a virtual world, and a VRML browser running on their local machine will take over the task of rendering that world and letting the user navigate through it. VRML can be used for everything from visualization of a building layout to representation of complex data. For example, the dynamics of the stock market or a complex industrial process as a moving, 3D image can be represented. Instead of trying to determine cause and effect using static numbers, employees will be able to more easily grasp the forces driving the information and its resultant knowledge. A marketing manager, for example, might find VRML useful when creating a pricing model. With VRML, the manager could plot a 3-D graph that charts the number of units shipped against the profit margin and against the return on investment. Then, the individual could watch the graph change as the variants are altered. If the manager wants to project the impact of a drop in units shipped, for instance, the individual can simply push down the shipment curve and watch what happens to the profit margins and return on investment. There is no need to spend time crunching numbers and interpreting the results. Longer term, VRML supports 3-D–rendered data and processes that let the user walk or fly through virtual worlds. The ability to manipulate one’s viewpoint and interact with virtual objects brings a new understanding of details, structure, and relationships. Overall, the capability to navigate around a conceptual space is an important asset to applications such as workflow for accounts payable or the sales department. The spatial characteristics allow identifying locations and 3-D characteristics. Instead of entering a flat page, a person can enter a city, a business, or a drawing. In fact, industry experts believe the beginnings of tomorrow’s important applications are starting to be developed today. These include 3-D simulation and training, visualization of financial data, virtual retail stores, visualization interfaces to data warehouses and executive information systems, network management, and corporate presentations. Newer XML (eXtensible Markup Language) Currently, eXtensible Markup Language (XML) has emerged from a desire to change the Internet’s HTML to separate the data content of a Web page from the display formating of that page. XML standardizes the data content format, while other technologies deal with the layout format. Now XML usage has expanded beyond Web pages. XML files are human readable, usually in Unicode or ASCII. Elements are specified with leading and trailing tags identifying their names. The element value is contained between the tags. Elements are commonly nested in groups, and each group is named. As with HTML, XML permits

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content to be separated from its format. But XML adds meaning. A number could be identified as, for example, a list price or the December sales of the western region. Non-numerical data is also covered—for example, an XML tag can describe a shirt’s color or style. Those tags and the standardized procedures for interpreting them can accompany the information wherever it goes. This enables documents, browsers, databases, and computer programs to follow programmed rules for displaying or applying the data. Although HTML is a good language for computers to use to communicate information to people, it is a poor choice for companies to talk to other computers. Inherent limitations in HTML and Web technology have hampered the Internet’s usefulness for business-to-business transactions. XML technology has emerged as a better model for managing and storing on-line content. XML has been rapidly adopted on the server side, even before this technology has become widely available on the client browser. The advent of the XML server and its emphasis on open standards lets XML servers from different vendors talk to one another and propels the use of XML for business-to-business exchange. Newly emerging XML server technology also gives on-line publishers more control of their data and make up for HTML’s inherent limitations. Basically, XML technology makes for low cost and flexible E-commerce exchanges. XML is now widely viewed as the catalyst for building E-commerce sites that interact with multiple trading partners.

Essential Elements of the Internet for Business Intelligence The essential elements of the Internet differ from those set forth previously for intranets. The transition to Web-based on-line transaction processing calls for different application architectures. To provide proper support for the Internet, an application needs to be built in a three-tiered manner (as set forth previously in the text). By way of review, they include: (1) the user interface on the browser, (2) application logic on a middle-tier server, and (3) data on a backend data server. The key issue is to insulate the middle-tier business logic from the front-end clients and back-end data servers. By doing so, changes can be made at any tier without affecting the others. As shown in Figure 5.3, a threetiered architecture is used for the Internet and its tie-in with the World Wide Web. Besides running the business logic, the middle tier hosts some transaction middleware. This type of software gives the Web a memory (technically called “state”) so a transaction can consist of several separate actions taken by a user. It also provides integrity for the transaction so customers are not charged for a purchase that is never recorded. Transaction software typically includes security features, supports other security products, or does both. Lastly, transaction middleware can run on several servers, so the user can add servers as the traffic load increases and balances the load among them.

Figure 5.3 The Utilization of a Three-Tiered Architecture for the Internet

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Doing Business on the Internet Although establishing a direct connection to the Internet was somewhat difficult in the past, it is easy today as more commercial Internet service providers have sprung up to meet the growing demand from businesses and as increasingly powerful computers and software make it possible to hide the Internet’s Unix command system behind graphical, point-and-shoot interfaces like Mosaic or even Microsoft Windows. In the immediate past, most of the Internet’s traffic consisted of E-mailing, browsing the Web, reading and posting network news, or just languishing in artificial role-playing and interaction environments. Today, the orientation has broadened to include such items from high-volume trading of stocks and bonds by the likes of Bank of America, Merrill Lynch, and Fidelity Investments to consumer purchases of everything imaginable. Products and services are being bought and sold every day on the Internet. While consumer sales on the Internet will continue to rise, the real growth will be in business-tobusiness commerce. That is, for large companies, an emerging crop of products that support EDI over TCP/IP make the Internet a more attractive, cost-effective medium than traditional value added networks (VANs). Meanwhile, small companies are moving to capitalize on the Internet and are challenging even the biggest conglomerates in the Internet marketspace. Products that secure largescale transactions on the Internet are being developed, which will further increase the Internet’s attractiveness. Although the Internet’s role as the new “global business village” is still evolving at this time, there is one thing that is clear: The Internet is linked to the need for communicating business information about a company’s products and services. The Internet has the capability to communicate with customers, potential customers, partners, distributors, and employees. Over the Internet, consumers can place an order, brokerage customers can make a stock trade, and suppliers can even update a manufacturing database which, in turn, could alter a production schedule. These capabilities apply equally to users accessing a Web site via the Internet or an intranet. The Internet’s allure is that it provides a relatively cheap, rapidly updatable, easy-to-distribute medium with a rich environment of text, graphics, sound, and video. Today, any size company must be involved in the Internet using the World Wide Web in some shape or form as a way of doing business if it wants to survive in the long run. Emergence of Internet II Currently, the Internet is so saturated with commercial and other informed users that it can no longer provide the speed, access, and reliability needed for interactive and high-density transmissions. In fact, the Internet has become so popular with household computer users that the Internet’s originators—universities, industries, and the federal government—are being crowded out. So the developers have created Internet II, which they plan to keep all to themselves.

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Initially, Internet II is designed to be a private network owned by its charter members. The charter members are 100 universities that do a lot of research. However, these schools are not excluding everybody from their Internet II club. They are cutting in the federal government and big industries because that is where universities get most of their research money. At the university level, the current Internet restricts classroom demonstrations to simple computer drawings akin to stick figures. Connections are not good enough to produce complex teaching aides without unpredictable delays. On the other hand, Internet II seeks to fix that by improving computer connections among and within campuses and by developing ways to sort and prioritize information to allow real-time video presentations to cruise past less urgent Email on the information superhighway. The ultimate goal of Internet II’s enhanced voice, video, and data capabilities is to create a network that researchers can rely on to obtain the high-volume computer files they need, when they need them. The bottom line is that professors can effectively reserve network capacity. Latest Direction—Wireless Internet Access The latest direction in the Internet is wireless access whereby people anywhere feel like they are connected directly to the Internet, without a wired connection and without requiring dial-up access. This wireless approach to the Internet opens doors to applications that people have begun to imagine. Motorola has embarked on an ambitious and expensive plan to develop a global, wireless Internet access system for use in conjunction with third-generation cellular networks. The company will invest upwards of $1 billion over the next 10 years, much of that directed toward its technology partner, Sun Microsystems, which will provide the software and hardware. The project is an extension of existing digital wireless networks, offering broadband network access to the masses with service comparable to current corporate LANs. The company’s networking sector has targeted its development efforts at three main levels. The lowest two tiers in the cellular hierarchy, basestations and call-processing control centers, will use the computer group’s hardware, along with Sun’s real-time Chorus operating system, high-availability software utility features, and the Java Dynamic Management software kit. Sun’s Netra server platforms will go into the top tier as the basis for central-office server applications.5 MERGER OF TRADITIONAL E-COMMERCE WITH THE INTERNET, INTRANETS, AND EXTRANETS A logical starting point for a discussion of the Internet and a company’s intranets and extranets within a BIS environment is the area of E-commerce. Traditionally, electronic commerce has utilized such means as electronic docu-

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ment or data interchange (refer to Chapter 1), fax communication, symbol technology, bar coding, and interenterprise messaging. While the deployment of this information and communications technology has grown steadily over the years, traditional electronic commerce has never reached the level of acceptance that has been accorded to electronic commerce on the Internet or Internet commerce. The reason is that traditional electronic commerce relied for the most part on the value added networks (VANs) and private messaging networks, which generally offered limited connectivity at high cost. Connectivity on VANs reached only other paying enterprises and relied mostly on store-and-forward methods. While quite adequate for electronic commerce functions such as passing purchase orders and invoices (which can be processed in a batch mode from a store-and-forward mailbox), VAN connectivity is too limited for advertising and interactive functions such as browsing product and service text and graphics. Its chief advantages are relatively good security, reliability, and confirmation of receipt. The Internet, on the other hand, has worldwide connectivity that is growing at a rapid rate in every segment of society, can be interactive, and is relatively inexpensive to use. The major disadvantage is that there is no central authority for managing the Internet. Also, there is a problem of reliability which, while improving at this time, is sometimes questionable. Security must be considered nonexistent unless the user provides it. But relatively new Internet services, such as the World Wide Web and the technology advances behind it, make it possibly the most exciting development for business today and tomorrow. The Internet’s potential for offering nearly unlimited electronic communications with a world of trading partners—not just those who subscribe to one private network—has stirred the imagination of EDI users and software vendors. Software such as Premenos Technology Corporation’s Templar now offers companies the alternative of Internet-based EDI. The traditional EDI VANs are looking at the Internet as a logical extension of the core commerce services that they provide anyway. The attraction of the Internet and other Internet protocolrelated technologies for business EDI is the same as in other areas of use: lower cost, greater speed, and the potential for vastly wider interoperability. Where VANs charge by every thousand characters of data transmitted, an Internet connection entitles users to transmit countless documents for flat monthly fees. For a medium-volume EDI program, this can mean a total cost of hundreds of dollars a year over the Internet versus thousands with a VAN. In addition, Internet transmissions usually range from 9.6Kbps to 1.5Mbps (on a T-1 line) up to 300 times as fast as the typical 4.8Kbps of VANs using the X.25 standard for data transmission. To protect their territory, a number of VANs have responded with Internet-based products and services. These come in two principal types: traditional store-and-forward document transmission using the Internet as a transport mechanism, which usually includes encryption for security, and a Web-based EDI service that allows small trading partners to

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exchange documents with their larger partners using only a basic Internet connection and document forms found with a World Wide Web browser. The merger of electronic commerce with the Internet can be extended to include a company’s intranets and extranets. However, the accomplishment can be somewhat difficult if a company’s commerce and Web-related projects are spread across departments, which is usually the case. Often, the computer department oversees intranets and extranets, marketing runs the corporate World Wide Web, and traditional EDI is specific to each business unit. Nonetheless, the potential payback of unifying various endeavors under a Web-fired vision of electronic commerce is enormous and well suited for strategic-minded intranet and extranet managers. Current Business-to-Business E-Commerce To better compete in the Internet economy, a company needs to embrace a newer business-to-business E-commerce architecture that makes the partners full participants in a company’s work processes. When companies begin to look for their place in a business-to-business E-commerce operating mode on the Internet, some dream of on-line stores and other important moves right away. However, it may be better to see a return on investment if the company starts with projects that will reduce costs and increase profits rather than generate new revenues by conquering new markets. E-commerce development can be simple or sophisticated. At the most basic level, a company can get into E-commerce merely by adding communication over the Internet—E-mail—to establish a form of communication comparable to the telephone, the fax, or the postal service. Up one level, a company can use the Internet to manage information, including on-line databases. A company might use the Internet or an intranet to put its current and archived newsletters on line for staff and customers. At the next level, E-commerce is integrated into reengineered business processes within the organization. A company can post information about its benefit plans and job openings on a Web page accessible to employees. Ready access to that information can reduce the number of calls the human resources department has to field and speed the dissemination of information, which both reduces costs and improves quality of service. At the high-end, E-commerce can be used to reach potential customers, current customers, suppliers, and other groups. That gives companies an opportunity to tap into a large new market. For the financial executive spearheading an E-commerce initiative at a company, one approach is to start small, preferably with a pilot project, before going all out. A company, for example, might develop a pilot Web page for several hundred dollars to $1,000. That way, the company can get some modest experience with E-commerce vendors and the implementation process, learning how to weigh costs and benefits in an E-commerce environment. In other cases, companies will want to go a lot farther. Although E-commerce project costs can

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grow as expensive as management will let them, it does not always take a million dollars for a national company to develop a substantial Internet presence. For example, a multimillion-dollar company can develop an on-line presence commensurate with its business size for under $100,000 and in less than two months. As a starting point, the Web set itself should feature a home page that greets visitors and makes them feel welcome. It should download fairly quickly so that visitors do not lose patience before they have even gotten started. Essentially, the home page should be linked to other pages that offer company information similar to what might be found in the annual report, employment opportunities, departments and subsidiaries, management structure, and a feedback mechanism that facilitates contact with appropriate organization personnel. It should be noted that the Internet service provider (ISP) may act as the “host” of the Web page and may provide other services as well. A security system is very important to protect the site itself and the company’s other computers, which a hacker otherwise might be able to access through the Web site. The security system should include encryption to protect customers’ data when they order on line, firewalls to keep out unwanted visitors, and a system for providing different levels of access to people within the company. All in all, an effective approach to business-to-business electronic commerce on the Internet must be well planned and executed in a logical manner. Typical software available currently is from IBM, which has helped thousands of companies build, run, and manage powerful interactive E-commerce Web sites. To help create E-commerce, IBM software provides the building blocks for a range of comprehensive, end-to-end solutions. Products, such as Net.Commerce, DB2 Universal Database, and IBM Firewall offer companies the scalability, reliability, and security it takes to reach, sell, and service its customers worldwide. COMPANIES USING BUSINESS INTELLIGENCE SOFTWARE (INTERNET- AND INTRANET-BASED) TO ANSWER IMPORTANT QUESTIONS Computer networking within a BIS environment is generally tied in with some type of software. For example, Oracle’s Business Intelligence System is aimed at giving decision makers information from their operational systems that can help them make better decisions. Usually, executives at the higher levels want to ask a different level of question than line managers or operations staff. The questions tend to cut across the entire organization. Typical questions include the following: How much can the company’s sales grow before outgrowing its manufacturing capacity? What can the company outsource effectively to avoid building a new plant? Which employees are most productive and are they being compensated fairly? Executives need to be able to assess the impact of moving manufacturing elsewhere in terms of the affect on suppliers and the whole dis-

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tribution process; what impact it would have on the sales process; and what could the company expect in terms of cash inflows and outflows. These are the kinds of questions executives want to be able to ask. To answer such questions, information and knowledge and the resulting business intelligence must be culled from all functional systems—corporate planning, marketing, manufacturing, finance, and human resources. Essentially, this is the job of Oracle’s Business Intelligence System. Designed as an easy-to-use, Internet- and intranet-based, self-service application, it provides answers to difficult questions about a company’s business across the entire set of Oracle Applications. The system implements Oracle analytical and reporting components, such as Oracle Reports for news and simple drill-down, Oracle Discoverer for analysis, and Oracle Express for sophisticated, multidimensional analysis and modeling. A starting set of 50 performance indicators ships with Business Intelligence System. To access performance indicators, decision makers simply point their browser to the appropriate Web address on the corporate intranet and select the indicators from the screen. Depending on the level of information, knowledge, or intelligence needed, they can drill down into deeper levels of detail. Decision makers can view the current status of their key performance indicators automatically on their desktops. In addition to incorporating the Business Intelligence System, the most recent release lets decision makers leverage the data managed by the Applications modules for better business value. The cycle of continuous process improvement is realized when decision makers make decisions based on information and knowledge derived from analysis of data held in the Oracle Applications, using the Business Intelligence System or OLAP and multidimensional analysis tools such as Oracle Financial Analyzer. Whereas most enterprise application suites focus on automating manual processes, Oracle Applications go further by providing the integrated information access that is needed to answer important questions about the business and across the enterprise. Oracle Applications provide a strong combination of process automation and information access not only for the traditional realm of enterprise resource planning (ERP) applications—financial accounting, manufacturing and supply chain, and human resources—but also for customer-facing business areas, via integrated Applications for front-office management, including sales force automation and customer service (Oracle Sales & Marketing, Oracle Sales Compensation, and Oracle Service).6 SUMMARY Data storage in the form of data marts and data warehouses was the main focus of the first half of the chapter, along with their underlying framework and current directions in how the data is stored. Building upon this discussion was a tie-in with data mining (i.e., the ability to sort through large volumes of data and discover patterns, trends, associations, and relationships, including those not previously suspected). Also, there was a discussion of utilizing data warehousing

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and a data federation system as a strategic weapon for a typical company. In the second half of the chapter, the fundamentals of computer networking that are essential in developing a BIS operating mode were discussed. Underlying such a networking infrastructure are the Internet and the World Wide Web, including collaborative computing that enables company personnel to work together as well as work virtually. Today, this includes the utilization of not only groupware but also Web technology that links together disparate sources of business intelligence globally and provides a platform in the search for appropriate intelligence for decision makers. This background will be helpful in gaining an understanding of how to build and implement business intelligence systems (Chapter 6) as well as their applications (Chapter 7 through 10). NOTES 1. Michael Stonebraker, “United We Stand,” Intelligent Enterprise, April 20, 1999, pp. 39–45. 2. Katherine Bull, “Data Warehousing: The Ideal File Cabinet,” InformationWeek, January 16, 1995, pp. 43–48. 3. Judy Silver, “Mellon Banks on Mining,” Enterprise Systems Journal, May 1999, p. 24. 4. Thomas M. Koulopoulos and Nathaniel Palmer, “Intranets: A New World Order,” Virtual Workgroups, September–October 1996, pp. 9–12. 5. Will Wade, “Moto to Invest $1 Billion in Wireless Internet Access,” Electrical Engineering Times, June 14, 1999, p. 6. 6. Kelli Wiseth, “Intelligent Business,” Oracle Magazine, September–October 1998, p. 64.

PART III Building Effective Business Intelligence Systems

6 Development and Implementation of Successful Business Intelligence Systems GETTING STARTED ON DEVELOPING AND IMPLEMENTING BUSINESS INTELLIGENCE SYSTEMS BY EMPLOYING ENTERPRISE APPLICATION INTEGRATION Because a comprehensive and unified operating mode is the ultimate goal of a business intelligence system, hardware and software products cannot be used in isolation. Building a corporate-wide enterprise application integration (EAI) infrastructure requires the integration of many different methods and technologies. For a company to unlock its collective expertise for companywide usage successfully, it is necessary not only to implement integrated hardware and software technology but also to integrate a company’s personnel and their related business processes with appropriate business intelligence technology. If company personnel are not working in a collaborative environment or if procedures and processes are not in place to share data, information, and knowledge, no amount of business intelligence technology can change that. However, for an EAI environment to work effectively, it must be viewed by company personnel at all levels as a strategic means for the company to become more competitive and ensure the company’s success in the long run. Enterprise application integration is essentially the ability to read from and write to all the applications and data sources across the enterprise. Such integration supports unified views of information and lets users update synchronously across systems. Although divisions can operate to maximize their operations, decision makers can view data, information, and knowledge, along with their intelligence, at a global level, assured that what is in one system is in sync with the rest of the enterprise’s systems. Using a commercial EAI product, there is a layer of glue attached to each system that provides an interface

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from each application to an external integration system. Using this approach, appropriate data, information, and knowledge can be forwarded to the intelligence system and synchronization can be performed. Typical EAI systems are available from Cross-Worlds Software, New Era of Networks (NEON), and Vitria Technology. Overall, enterprise application integration makes a lot of sense if the enterprise is focused on synchronizing a reasonable number of existing applications. Since enterprise computing is about consolidating and harmonizing the many islands of disparate data, information, and knowledge systems scattered throughout an enterprise into a unified whole, its goal is to streamline business processes and enable outward facing systems, including E-commerce. The attention that has been given to EAI computing during recent years is a result of the business process reengineering revolution which, in turn, was enabled by information technologies such as client/server computing. Through some hard-learned lessons, companies now know that it is not enough to wire together machines through a network using a client/server architecture. Although a well thought out technical architecture may have been developed, they know that a coherent information model that uses an object orientation is also necessary. In this chapter, the essential components of developing and implementing a business intelligence system within an EAI environment will be explored at some length, along with the steps found in developing a typical system. A New Direction in Application Development Is Needed to Help Decision Makers Better Understand a Company’s Operations In order to realize the full potential of enterprise application integration, it is necessary to take a fresh look at the whole development process of systems. What is needed is a new approach for building highly dynamic, flexible, and scalable solutions. With past approaches to systems development, the implied assumptions were that business requirements were static. There is no problem if the environment is static. With the traditional development methodology, only by clearly understanding business needs can a complete solution be delivered. Hence, “freezing the system specifications” was the order of the day. In an ever-changing dynamic environment of today where things are far from static, projects can become outdated before they can be delivered. As the business environment and its needs change—more toward E-commerce and the Internet—business applications are evolving and adapting to address these new emerging needs. Essentially, newer applications must be able to absorb additional functionality and capabilities easily. This is absolutely critical for maintaining alignment between the application capability and user needs of today and tomorrow. Of course, an application can be built that is aligned with users’ needs when initially deployed. Such needs will change, however, and the application will become increasingly ineffective if it is static.

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Trend Toward Real-Time Computing or the Zero-Latency Concept Not only should applications be able to react to their environment but they must also be able to affect their environment in a proactive way. That is, these applications within a BIS operating mode should help decision makers change how they do things and push boundaries, thereby helping to reinvent the organization where necessary. For operational systems, this means putting systems in place, such as real-time inventory management on a global level or dynamic product pricing based on daily or even hourly demands. For effective BIS, solutions must give decision makers the capability to analyze (i.e., understand more than ever before). Business intelligence solutions should provide decision makers with the ability to anticipate customer needs—not just react to them. For this newer perspective, there is a need for real-time computing (i.e., an instant or almost instant response to a request by most anyone, ranging from customers to decision makers). Early real-time efforts centered around the military and aerospace industries. The model then spread to Wall Street, where the traditional commercial early adopter turned to complex publish-and-subscribe middleware to give traders near real-time access to data and information. Fortunately, the convergence of several newer technologies brings the possibilities of real-time computing to more traditional industries. As more and more organizations utilize middleware and EAI technologies to link previously stand-alone applications, while at the same time refining and automating business processes, the opportunity to implement real-time capabilities increases substantially. This real-time response has the capability to help decision makers with a better understanding and insight into their operations. Based upon the above discussion, the change in system development methodology is away from a static approach to the employment of real-time computing, or the zero-latency concept. For the most part, the technologies employed in real-time enterprise systems vary. Typically, real-time enterprise operations tend to use all or some of key middleware technologies, such as data transform software, message brokering, message queuing, or publish-and-subscribe software, along with on-line analytical capabilities. As such, the EAI movement has a role in the real-time enterprise. The response time involved is what makes it real time. If one system event is programmed to trigger other application events automatically, the real-time enterprise concept is being employed. A fast reaction time to market-to-market forces and improved operations through leveraged information handling across groups are the end results. Today, the real-time enterprise, or zero-latency concept, goes under many names. These include just-in-time computing and straight through processing. Distributed object technology, business process reengineering, enterprise resource planning, and supply chain management systems, to some extent or another, all share some tie-in with the real-time enterprise. Typical examples are as follows. Investment banker D. E. Shaw & Company uses Talarian publish-

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and-subscribe middleware to provide a global backbone for statistical arbitrage systems that exploit slight price movements in capital markets. Dow Jones Interactive is using the Cloudscape DB and an XML application server to integrate varied news feeds for deployment on corporate intranets. In the area of manufacturing, a performance and operations management system for the Toronto Works and Emergency Services Department provides hourly data to shop floor crews. Only monthly data had been provided before. Overall, real-time computing, or zero-latency, is providing an underlying approach for developing business intelligence systems.1 COST JUSTIFICATION FOR BUSINESS INTELLIGENCE SYSTEMS An often asked question by corporate sponsors of business intelligence systems is: How does one justify the cost in terms of a traditional cost-benefit analysis? The answer given by those who have tried within or outside the computer department is that one does not. For the most part, the payoff from business intelligence systems are different from other types of information systems in that they are often less tangible, less quantifiable. For example, in the implementation of a new accounting system for accounts receivable, an information systems manager is able to estimate specific savings from tracking accounts receivable more closely. However, the new BIS focus is on providing decision makers with an improved understanding (i.e., intelligence) via some type of analysis over the short to long run. The real payoff is in giving them quick access to analytical information and knowledge that shows patterns and trends as well as projections that may lead to new ways of thinking and understanding, and, hence, better decisions. On the other hand, there can be instances where savings or gains can be tied directly to business intelligence systems. For the most part, however, no one has been able to come up with a model or system for cost justifying these systems. Typically, cost-benefit analysis is not used. In its place, the focus is on values of these systems to managers, support staffs, and technical workers. Also, it is not easy to predict the total cost of such systems in an accurate manner. The largest expense generally comes from the ongoing acquisition and dissemination of business intelligence to decision makers as they become more familiar with the systems and seek more intelligence about patterns and trends that can be related to future projections and assist them in doing a better job of problem finding and problem solving. Overall, cost justifying business intelligence systems tends to be more elusive than it is with traditional mission-critical information systems. FOUR ESSENTIAL ELEMENTS IN DEVELOPING AND IMPLEMENTING BUSINESS INTELLIGENCE SYSTEMS Today, the development and implementation of business intelligence systems involve a mixture of techniques and technologies. Related to this development

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and implementation processes is the proper planning and leadership needed to identify and organize business intelligence. Also, there is the cultural aspect that promotes an atmosphere of community and intelligence sharing among company employees. In its efforts to acquire the business intelligence for future success, the company defines and then implements management practices that encourage business intelligence creation and sharing. To better understand the mixture of techniques and technologies needed in the development and implementation of business intelligence systems, the following four essential elements (noted previously in the text) are recapped below and explored in some depth in the next sections of this chapter. They are: 1. employing current information systems as a way to upgrade to business intelligence systems 2. utilizing data mining or knowledge discovery and business intelligence methods and software to better understand a company’s total operations today and in the future 3. building effective data warehouses and real-time computing systems where the focus is on a multitude of factors that relate to the Internet, intranets, and extranets 4. making the greatest use of computer networking that is related to E-commerce as a way of doing business with a company’s customers and suppliers

1. Employing Current Information Systems as a Way to Upgrade to Business Intelligence Systems In Chapter 4, the essentials of current information systems were given. Initially, idea processing systems were set forth, followed by knowledge management systems, on-line analytical processing systems, decision support systems, and executive information systems. Each of these systems can be related to the other three elements in developing and implementing business intelligence systems. Basically, the decision maker is the focal point of an effective business intelligence system. Typically, current software modules from present information systems can be integrated with BI software to provide a new way of thinking about and an understanding of a company’s operations. The upgrading of current information systems can take two major paths. For one, a BIS project can be undertaken that customizes the new system to meet decision makers’ needs throughout part or most of the organization. These steps will be given later in the chapter. The second path stresses starting with vendor building blocks that are related to the current information system installed. In either case, there is a need to go away from a static approach to system development and recognize the importance of real-time computing within a BIS operating mode. The second path means working with the present building blocks that are installed by one or more vendors and working with them to undertake a successful BIS implementation. IBM, for example, may have installed all of the building blocks for a typical company that were needed for a successful imple-

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mentation. These include DB2 Universal Database, DB2 OLAP Server, Visual Warehouse, DecisionEdge, and Intelligent Miner for data and text. In turn, IBM’s business partners, which are supporting its BI solutions, include Brio Technology, Business Objects, Cognos, ETI, Hyperion Solutions Corporation, and Vality Technology, all of whom have a wide range of products for tying in with the present information systems. The capability of bringing several BI vendors together using the present information systems as a foundation can only help the BIS movement to better realize actionable business intelligence over time. Empowering Decision Makers Using a BIS Operating Mode When decision makers transfer decisions into action using a BIS operating mode, they are supported with the intelligence they need to help the company’s customers and its partners. Business intelligence systems essentially come down to getting more power to meet their needs. As such, decision makers can call on the rest of the organization for information instead of the rest of the organization telling them what to do. The shift is from a product-driven organization to a completely market-driven organization. From this broad perspective, the more open the process is, the better will be the results. The more structured the process is, the less it works. It is helpful to think in terms of open space, electronic meetings when some decision maker announces an issue. The people who organize around the issue are the ones who are interested in the issue or who know something about it. The meeting continues until the decision maker who desired the information in the first place is satisfied. Then the process stops and everybody goes on their way. Hence, a free-form exchange that empowers decision makers provides a better approach to problem solving and problem finding than under the traditional approach. 2. Utilizing Data Mining or Knowledge Discovery and Business Intelligence Methods and Software Data mining or knowledge discovery is considered to be an outgrowth of the data warehouse concept. It allows data access and analysis that gives users unparalleled capabilities to extract hard-to-get-at data, spot trends, and recognize patterns in corporate databases. While companies have built data warehouses to consolidate data located in disparate databases, they are implementing data mining to learn more about the data. While data warehouses are a part of the mainstream technology, available data mining tools allow companies to improve their knowledge of their customers and markets and thereby understand the important factors surrounding them. For example, retailers want to use it to target customers for sales promotions better or to manage inventory across various geographic locations. Similarly, telecommunications companies want to use data mining to forecast demand patterns, profile and segment customer groups, customize billing, and analyze profitability. In addition, financial services companies are em-

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ploying data mining to consolidate information from multiple sources, analyze customers’ business patterns, and sell them more services. Data mining has the potential to explore the data, information, and knowledge found within a company’s database by revealing patterns and trends that can suggest improved performance in terms of greater customer satisfaction, higher quality products, savings, and profits. In effect, data mining uncovers hidden patterns and provides predictive trends that can be easily applied to benefit the business. The raw materials for data mining are abundant, and the data contains records that could potentially reveal hidden patterns and predictive trends that could further a company’s mission, its objectives, and its measurable goals. Sales records, for example, could reveal highly profitable retail sales patterns. In turn, financial analysts could find future trends that are highly profitable. As another example, an engineering firm could determine the combination of data conditions (e.g., manufacturing time, lot size, assembly parameters, operator number, reject rate, etc.) that determine the quality of the products being produced. In the final analysis, the key to successful data mining is to employ those tools that result in real knowledge discovery. Related to data mining tools is the world of business intelligence. Essentially, BI tools are being marketed by a wide range of vendors that include both new and established vendors selling products that leverage both aged and real-time data. The mix includes data warehouse vendors, enterprise application integration (EAI) vendors, development tools vendors, pure analytical applications vendors, and even enterprise resource planning (ERP) vendors. At the very high end are BI tools that have the ability to combine the real-time aspect of just-intime (JIT) with an analytical tool. JIT analysis is best suited for decision makers who need the most complete and up-to-date intelligence. At the lower end, business intelligence tools employ data warehouses with some type of analytical tool. For the business intelligence system to be successful, it should be accessible and appealing. The company’s intranet site can be used as a launch point for BIS applications. There can be a link up to a bulletin board or knowledge repository Web page to enable users to post tips as well as share success stories about how they gleaned valuable business insight from the BIS software. Key queries can be set up to be automatically refreshed and displayed whenever data is updated. Even “teaser reports” can be prepared and broadcasted to users for further investigation. Exception reports (alerts) can be utilized to focus on anomalies and outliers in the data rather than continual scans through large volumes of detailed data. Overall, making the accessibility, speed, and business insight highly visible to the user community will encourage data mining utilization. Output from Data Mining Useful for Decision Makers Because data mining reaches deep into databases, its tools provide decision makers with the capability to find patterns in the data and infer rules from them. Those patterns and rules can be used to guide decision making and forecast the

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effect of those decisions. In addition, data mining can speed analysis by focusing on the most important variables. Although users might find these patterns with a series of queries against the data, data mining lets the users explore a wider range of possibilities than even the most sophisticated queries. Types of information and knowledge that can be obtained by data mining include the following: forecasting, associations, sequences, classifications, and clusters. Essentially, all of these may involve predictions of some type.2 However, in the first type, or forecasting, there can be a different form of prediction in that it estimates the future values of one or more continuous variables—like sales figures—based on patterns within the data. The second type centers on associations that happen when occurrences are linked in a single event. For example, a study of supermarket baskets might reveal that when potato chips are purchased, 60 percent of the time some type of cola is also purchased, unless there is a promotion, in which case cola is purchased 80 percent of the time. Knowing this fact, managers can evaluate the profitability of a promotion. In the third type, or sequence, events are linked over time. For example, if a house is bought, then 60 percent of the time a new oven will be bought within one month and 65 percent of the time a new refrigerator will be bought within three weeks. For the fourth type, or classification, patterns are recognized that describe the group to which an item belongs. It does this by examining existing items that already have been classified and are inferring a set of rules. Because the loss of steady customers is a common problem for many companies, classification can help discover the characteristics of customers who are likely to leave and help provide a model that can be used to predict who they are. It can also help a company determine what kinds of promotions have been effective in keeping which types of customers. The fifth and last type is clustering. It is related to classification but differs in that no groups have yet been defined. Using clustering, the data mining tool discovers different groupings within the data. This can be applied to problems as diverse as detecting defects in manufacturing or finding affinity groups for bank cards. Current Data Mining or Knowledge Discovery Methods and Software A starting point for data mining methods and software available from vendors is a brief introduction to how data mining works. Typically, a decision maker feeds a business goal and corporate data into data mining tools, which use a variety of methodologies to model the data. The outcome is a set of factors in the data that are related to, or predictive of, the business goal in the form of data visualization. Using data visualization software, which presents a picture for users to see, an enormous amount of information is presented in a concise format. A very wide range of information, including knowledge, presented to the user allows peaks or valleys to stand out. Many methods currently used in data mining are natural extensions and generalizations of analytical methods that have been used for decades. Neural net-

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works, a special case of projection pursuit regression, were developed in the 1940s. CART (classification and regression tree) methods were used by social scientists in the 1960s. K-nearest neighbor, a form of density estimation, has been used for a half-century. Other methods include rule induction, discriminant analysis, and logistic regression. Basically, these methods, just like regression techniques, model relationships between a set of profile variables and an outcome. However, what is new is that these methods are now being applied to more general business problems as a result of the increased availability of data, inexpensive processing power, and user-friendly software for implementing these methods. The recent interest in data mining is in part due to improved user interfaces. Varieties of regression techniques, discriminant analysis, and even simple graphs can help reveal hidden patterns. Because no single method solves all or even a majority of problems, successful data mining requires a portfolio of tools, both old and new. Data mining or knowledge discovery software useful in a BIS environment is an important means for decision makers to uncover patterns, trends, and relationships as well as rules for today and tomorrow. Among the major players are: Angoss Software International, Business Objects, Cognos, DataMind, Information Discovery, Magnify, NeoVista Solutions, Pilot Software, SAS Institute, and Thinking Machines. The Data Mining Suite from Information Discovery provides an example of data mining products. It is an integrated set of data mining products and services that provide the users with a powerful, complete, and comprehensive solution for large-scale, enterprise-wide decision support and data mining. The system works directly on very large SQL repositories with no need for sample or extract files and delivers results on the Web. Over 90 percent of computations are performed on the server, in parallel, if desired. The system directly accesses large volumes of OLAP and relational data, incrementally discovers powerful general patterns, and goes far beyond decision trees and simple rules. The system delivers automatically generated English text and graphs as explainable documents on the intranet. Another typical software product is the Intelligent Miner from IBM that runs on AIX using a 5-node RS/6000 SP. Analyses are performed from Intelligent Miner clients on Windows NT. This software package serves as the core technology for Loyalty Consulting’s data mining services for clients in finance, retail, telecommunications, and other industries. Intelligent Miner’s full range of data mining capabilities help users to acquire more intimate knowledge of their customers, predict customer behavior, and find the most cost-effective way to influence that behavior. Most importantly, Intelligent Miner meets users’ key criterion for data mining: the ability to deliver measurable results, such as increased revenues and lower selling costs. It provides a wide range of algorithms for clustering (both demographic and neural), classification (tree induction and neural induction), value prediction, association discovery, sequential pattern discovery, and predictive modeling. Essentially, Intelligent Miner allows users to build better data mining models than with many other tools, since it offers a

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broad range of data mining algorithms that can be combined to provide greater value to users. Server data mining software licenses typically cost $150,000 to $200,000, while desktop or “micromining” software licenses typically cost $500 to $50,000. For example, IBM’s Intelligent Miner can deal with data warehouses containing hundreds of gigabytes or terabytes of information. Its licenses range in price from $25,000 to $150,000 for systems from Risc/6000 UNIX machines to System/390 mainframes. Although these costs may appear to be somewhat high, some vendors and users note that data mining offers a good return on investment and offers an important competitive advantage. Some of the other major data mining software packages were set forth in Chapter 4. Current Business Intelligence Software To assist in building an effective business intelligence system, there are a number of off-the-shelf software solutions from vendors such as Brio, Business Objects, Cognos, Hummingbird, IBM, Informix, Platinum, SAS Institute, Seagate, and Sybase, which allow immediate analysis as well as customization. Some of these vendors were covered in Chapter 4. Furthermore, traditional enterprise application vendors, like PeopleSoft, SAP, and Oracle have introduced their own BI-specific applications. Systems built of components from the same vendor utilize an open architecture in order to integrate existing desktop applications. An integrated, open platform, such as Mircosoft’s SQL Server 7.0 or IBM’s DB2 UDB (Universal Database), allows an easy interface with tools from differing vendors. Integration concerns are not just about traditional BI components of structured data but also about applications handling unstructured data. Increasingly, organizations seek to incorporate imaged data, geo-spatial, or text data into an analysis system. Solutions such as the SAS Institute’s Collaborative Business Intelligence exemplify the emerging market for tools that handle structured and unstructured data with equal efficacy. The market can expect more of those crossover solutions as structured data vendors like Computer Associates and Hummingbird integrate their newly acquired unstructured data experts Platinum and PC DOCS/Fulcrum. It should be noted that the building of an open platform facilitates the inclusion of data from multiple sources—legacy data, transaction data, and information from any relational database management system. As organizations spread across multiple locations, often with completely separate databases, the need for open integration becomes all the more critical. 3. Building Effective Data Warehouses and Real-Time Computing Systems Due to the fact that all types of data, information, and knowledge needs to be made available to decision makers on a batch-processing basis or a real-time

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basis for understanding a company’s operations, this third element in developing and implementing a successful BIS centers on building effective data warehouses and complementary real-time computing systems. Due to the importance of real time today for decision makers, an effective way to integrate real-time data in many independent systems is to federate the systems. Enterprise-class data federation systems that can unify data while retaining local administrative control have appeared in the market today. The technology has reached a point where the twin goals of local control and global views can be simultaneously achieved. It should be noted that current real-time computing systems are designed to perform a logical integration, and these systems can logically integrate either aged data (using a data mart or a data warehouse) or real-time data. Before examining the essentials of real-time computing systems, it would be helpful to examine the essentials of data warehousing. In general, building a data warehouse can be costly—say, in the millions for a very large one, and time consuming—say, two years. This costly and timeconsuming undertaking bears witness to the value of information and knowledge as well as a complete understanding of its operations is a strategic asset of a typical company. Getting there, however, is not simple, since computer managers must wrestle with the problem of moving various forms of data from legacy and on-line transactional processing systems. It requires preparing, conditioning, and staging data so business users, armed with appropriate data access tools, can perform analyses that were previously either impossible, too expensive, or too time consuming. Choosing a Proper Server Platform A most important decision to be made when planning and constructing a data warehouse architecture is the choice of server platform. Small databases with local query activity can often be hosted by Pentium-based PCs or Unix workstations. However, for larger databases supporting larger number of users, multiple network segments, and large queries, SMP (symmetric multiprocessing) computers are recommended. Where very large databases and very complex queries are required, companies usually use mainframe processors or sometimes turn to massive parallel database processors. In the final analysis, careful planning is required, as is the involvement of the networking staff, in order to ensure a successful implementation. Many companies have found that data warehouses tend to create a lot more demand than orginially anticipated. It is essential that extensive predictive analysis be undertaken so that network demand is estimated realistically. To assist in choosing the proper server platform, it is necessary to assess the work to be done by the client and the server. For example, one might ask, “Give me the top 20 customers by sales volume for the month of March.” To arrive at an answer, the system must add up all the business from all possible customers over that time period. There are two ways to determine the 20 customers. The first is to transfer all the customer and revenue data from that period across the

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network and process it on the client. The second is to place a query engine on the server to perform the query operation. In this second case, just the answer set—the information of the top 20 customers—gets sent back across the network to the client. The differences from the network perspective are quite substantial. The first scenario might involve a transmission of 200MB, while the second might involve only 200KB. Thus, depending upon the client/server situation, the demands on the company’s networking can be quite different and must be considered by the networking staff. Populating a Data Warehouse Once the data warehouse architecture has been established on the server, there are two primary methods for populating a data warehouse. The first method is called change-based, in which only the changes or differences are copied from the production database to the warehouse databases. The second method is called batch copy, in which the entire warehouse is periodically refreshed in a bulk upload or download of production data. Typically, bulk downloads can tax the network. Although transmitting only the changes puts less stress on the network, it requires more complex programming and maintenance. Which architecture is selected depends largely on the size of the warehouse and available resources. If there is limited network capacity, the change-based method is generally the best. On the other hand, if the network can handle large data transfers, the batch copy architecture will be simpler to set up and maintain. Building an effective data warehouse, however, involves more than just copying data and letting users employ PC-based query and reporting tools. As the data warehouse is populated, it must be restructured. That is, tables must be denormalized, data must be cleansed of errors and redundancies, and new fields and keys must be added to reflect the needs of users for sorting, combining, and summarizing data. The more thought that is given to the design of the data model, the easier it will be to maintain, update, and retrieve warehouse data. Overall, effective data modeling relieves network stress by rolling up or summarizing information into meaningful constructs so that users do not have to perform as many interactive queries to join tables in real time.3 Turnkey Data Marts and Data Warehouses Software To assist companies in getting their data mart and data warehousing projects up and running in a short period of time—say, in 10 days, vendors such as Acta Technology, Hummingbird Communications, Information Builders, Informix, NCR, Silvon Software, and Smart DB Corporation are shipping rapid start or turnkey versions of their software. Although complexity of application, type of terminology and speed of deployment may vary, Broadbase Information Systems, Enterworks, Influence Software, and others might be included in a list of vendors offering shrink-wrapped warehousing alternatives. Needless to say, the question can be raised: Are any of these products true out-of-the-box data warehouses? That is, don’t all data warehouses require some

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form of customization? Actually, out-of-the-box anything is a misnomer because what one gets out of the box is basically starter tools and templates to create a data mart or a data warehouse. Out-of-the-box data warehouses are not turnkey in the true sense of the word. In general, there is still the need to tailor and configure according to some type of process model. A process model refers to accessing, transforming, storing, and analyzing data from heterogeneous sources and bringing it to an end user. Today, most prepacked applications are primarily data marts. They can be deployed quickly and have a good return on investment. But over the long term, as more of these are implemented, they become somewhat disjointed. They need to be tied to an integrated enterprise architecture. Data marts are the beginnings of a company because they deliver information in a precise, integrated manner to users. Data marts are a very important element of warehousing strategies for most companies. When a company gets to the data mart level, users are more specific in terms of the data they need. On the other hand, an enterprise-wide data warehouse can have so much information and knowledge that it is unmanageable. Also, an enterprise data warehouse is unique to each organization’s needs. A company can use an out-of-the-box model, but the transformations required to get to the data will differ from organization to organization. Typically, a company has better luck with out-of-the-box data marts because they tend to be application specific—that is, tied to an enterprise data warehouse or directional operational systems. An intelligent approach is to implement out-ofthe-box data marts, while keeping the big picture of the data warehouse in mind. Building Effective Data Warehouses on the World Wide Web Until the advent of the World Wide Web, delivering information and knowledge other than in reports was difficult and expensive. However, the World Wide Web has changed the fundamental equations that determine the cost of delivering essential information and knowledge with an understanding of its contents. When deciding to Web-enable a data warehouse, a company must consider whether or not the warehouse will work, whether or not it can be done within a reasonable budget, and which tools fit within a company’s corporate standards. Typically, enabling a data warehouse requires less skill and programming than creating a full-blown application, but there is still the need for programming expertise. Once those concerns have been addressed, it is necessary to set a plan of action. There should be no attempt to convert every possible application unless the company is in a high-risk environment. Attempting to develop fullblown applications—from simple data entry to complex reporting—using only HTML can be difficult and time consuming. Thus, it is necessary to pick conversions carefully. Currently, data warehousing vendors are supporting raw connectivity between browser and database. IBM’s Net.data, for example, takes a request from the browser in HTML, converts it, connects to the database where the request is processed, translates the information back into a form readable by the browser,

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and finally redistributes it. In addition, enabling access to the data warehouse via the World Wide Web requires an extra tier in the data warehouse mix. The number of tiers that a Web warehouse spans depends on complexity and management preferences. In fact, a company can spread the Web-based client/server paradigm over as many tiers as it sees fit. Or it can mix and match them until there are very few physical tiers but several virtual ones. One can put the Web and database components into one tier. In the most typical scheme, however, a user armed with a browser and browser-based analytical tools queries a data mart or a data warehouse for information or knowledge via the World Wide Web. Queries to the data mart or data warehouse via the Web are interpreted by the Web content server and passed along to the applications server that drives the data warehouse. Needless to say, Web access to a data warehouse puts a heavy burden on servers. Most data warehouse operations have been small scale (in terms of users and functionality, not actual storage size), with functionality traditionally on the client, so a company must ensure that it has the server capacity in place to accommodate a Web warehouse. A data warehouse by today’s standards usually has only a few hundred users at the most, but a Web warehouse can expect tens of thousands. Overall, company personnel can use the World Wide Web to increase its company’s strength in the market place by putting the data warehouse on line. The more accessible data, information, and knowledge is to customers, vendors, business partners, and employees, the more successful the company can be. Essentials of Real-Time Computing Systems Today, it is clear that data marts and data warehouses are no longer the strategic “end game” of computer operations as they were a few years ago. They are part of a larger strategic solution for integrating people, processes, and aged data as well as real-time data into a seamless supply chain of business intelligence. The real strategy is figuring out how a company should blend its major sources, including data marts, data warehouses, real-time systems, packaged front- and back-office applications, document systems, and E-commerce systems, into a coherent whole. The data federation system approach is in line with enterprise application integration and the real-time computing concept found throughout the text. A real-time computing system that does not use centralized query optimization and scheduling will retain local control while scaling to hundreds of machines. An enterprise-class data federation system supports dynamic load balancing across system resources. As loads on individual machines and networks change, the system adapts and adjusts query execution. As a result, the system can support many machines with high performance and throughput. Such a system can be viewed as the complement to a transactional processing approach. From this view, this system is capable of obtaining the desired data in real-time or as near to real-time as possible.

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4. Making the Greatest Use of Computer Networking That Is Related to E-Commerce An introduction to computer networking was set forth in the prior chapter, along with the relationship of a company’s intranets and extranets to the Internet and the World Wide Web. In that discussion, the merger of traditional electronic commerce with the Internet, intranets, and extranets was detailed. However, there is the need today to go beyond what has been successful in the past and focus on what E-commerce can be for a typical company. Essentially, this means focusing on a creative approach (see Chapter 2) to E-commerce for changing times. The ingredients include creative ideas, business strategies, value propositions, and E-commerce technology, among others. The real winners of tomorrow are those who are not afraid to experiment with E-commerce technology. Experimenting means challenging the traditional E-commerce assumptions and going against the grain (i.e., being a revolutionary and believing in one’s ideas when others are not convinced). This could include paying one’s customers, and even one’s competitors, changing services, and providing value in new ways. Hence, it may be necessary to reshape traditional business models and develop new, emerging E-business models, and figure out how to leverage or improve them. Being in a highly changeable world where customers can be competitors and competitors are customers, where new profit centers can be created out of traditional cost centers (as in the case of Internet bill presentation and payment), and where traditional power structures and businesses are being overturned and transformed by newer approaches, the best Ecommerce approach for a typical company begins with a creative idea that is quite different from the past. In the discussion to follow, some new E-directions are examined. Initially, the focus is on the employment of groupware by business teams within a computer networking environment. Employment of Groupware by Business Teams Even though the utilization of business intelligence within a computer networking environment can be performed on an individual or team basis, increasingly today there is emphasis on a team approach, which is based on the old concept that several heads are better than one. Instead of spending money on problems or forming committees to talk about them, companies are sending out teams to find solutions. Generally, while self-directed work teams are getting things done on a day-to-day basis, cross-functional teams are reaching into all parts of an enterprise to address problems, implement plans, and make necessary changes. Today, groupware and Web-based tools let company teams take part in virtual teams; brainstorm, develop, present, and deliver business intelligence; share documents or applications; discuss and manage projects; and coordinate their activities. Typically, teams are the most rapidly expanding approach to involving employees in improving business results that make use of information and knowl-

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edge, including business intelligence. Where applicable, groupware can be used to keep team members apprised of results when they are not physically present. It should be noted that present groupware systems have entered their nextgeneration phase with Internet features that free users from proprietary platforms and protocols. A typical business team has six to 20 members. Successful ones have the right chemistry, a mix of problem-oriented, analytical types and action-oriented individuals. Before seeking solutions, these teams first identify the problem, rigorously gathering the facts, which includes assessing pertinent information and knowledge using a type of groupware system commonly referred to as collaboratory computing, and grasping the problem’s dimensions. At times, this process dictates the solution to pinpointing obstacles that must be removed. Where improvements, changes, and breakthroughs are called for, the collective energy and creativity of team members come to the fore. Recently, Lotus announced the availability of Sametime 1.0, an instant messaging program that allows users to chat via typed messages and to share and collaborate on documents in real time. Sametime is more than an on-line chat environment. It is a strategic technology which, when integrated with solutions such as a Customer Relationship Management solution, brings new dimensions to customer interaction. It enables new processes by which business-to-business collaboration can take place. Companies that seek to innovate and understand their customers’ needs should have collaborative infrastructures. It is not enough to try to change people’s perceptions and attitudes through friendly perception. The best way to build those infrastructures is to make sure that people can easily grasp and use tools for collaboration. When Notes was released as a groupware technology in the early 1990s, it gave technical reality to business collaboration. At that time, the Internet, browsers, and tools, such as Sametime, were not technical realities. But with E-commerce driving business strategy to support a typical interenterprise, globally connected world, Sametime brings value in the form of expanded individual-to-individual, individual-to-group, and group-togroup collaboration. Effective Use of a Company’s Intranets and Extranets Intranets and extranets are an important part of a company’s operations. In one sense, a company’s intranets are a variation of the Internet and the World Wide Web. Unlike the normal use of the Internet (a public highway), a company’s intranets are a private or semi-private use of either the Internet or Internet-related technology for a particular organization. Intranets let companies exploit what they have learned from the Internet for the use of private, internally focused networking needs. For example, many companies have volumes of policies and procedures that they would like their employees to be aware of and actually use when appropriate. Unfortunately, most of the time, a set of policies and procedures sits on a shelf somewhere in a company and collects dust. Employees may not know

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that the specific policies and procedures exist or even how to locate a current version. Intranets allow companies to take their policies and procedures and put them on line for ready access by internal employees. In the past, the effort to put such policies and procedures on line might have been arduous and costly. By using company intranets, employees are able to get at the policies and procedures quickly and relatively inexpensively. Intranets are not just for company employees anymore. The phenomenon of “outside intranetting,” or extranets, which gives third parties such as trading partners and customers access to the corporate World Wide Web site, is widely used today as companies explore more creative uses for their intranets. At their most powerful, intranets can be harnessed in this manner to create new types of organizations, such as virtual groups that convene for a specific purpose and time period and later disband once a project is complete. In essence, intranet innovators are marrying internal networks and extranets with the Web technologies to create powerful, collaborative capabilities for employees, business partners, and customers. As will be seen below, they have the potential to transform business into electronic commerce virtually overnight. Linkage with Outside Organizations Using the Internet In the above exposition on the utilization of company intranets and extranets, reference was made to the Internet. The Internet has basically become the postal service, telephone system, and research library of the electronic age, allowing millions of people to exchange business intelligence virtually anywhere in the world and at any time, usually in a matter of minutes, using available data communications and networking technology. Its appeal is that anyone on the Internet can post and retrieve desired output. Thus, it behooves a company to plan Internet use with a clear focus on the customers who will be the most important to it in the future. Although most companies focus on building an informational Web site, it is more important to provide a service defined by key customers. Customers do not come to a company’s Web site because it runs secure Web servers. They come if they are given value for their time—that is, it reduces their cost of doing business. In some cases, it is possible that customers will pay higher prices if their overall costs of identifying and acquiring the products or service are significantly reduced. Typically, a company’s Web site says more about the company than its logo, buildings, or annual report because it is where customers go first to find out about and access a company’s products and services. In turn, this input to customers may help them reduce the costs of their operations. Gaining a favorable presence on the Internet’s World Wide Web, then, is the name of the game for the typical company today. Information about customers’ responses can lay the foundation for future knowledge about customers’ reaction to favorable Web sites. In terms of knowledge-based expert systems, their ability to answer specific well-defined problems makes them ideal as a Web-resident technology for prod-

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uct selection, technical support, help desks, and regulations (i.e., any area that requires specific decision-making knowledge). Accessing an expert system on the Web, as opposed to a Frequently Asked Question (FAQ) list, is like talking to an expert instead of reading a technical manual. If, for example, a customer has a problem with a PC printer, an expert does not tell the person all the things that can go wrong with the printer to see if any apply. He or she asks questions about the problem and its symptoms and, in turn, gives one or several recommendations. That is exactly what the expert system does. Rather than using a FAQ list, it presents questions to be answered, followed by a solution. This is not just a database search because the questions are focused. Subsequent questions depend on the answers to earlier ones and on the relevance to a possible solution. Essentially, straightforward development tools allow developers to build expert systems rapidly on PCs. In turn, they can move them to a Web server and run them with the appropriate runtime engine. On the other hand, when dealing with problems that are much larger in context and way beyond the scope of expert systems (which is somewhat typical today), it is necessary to make use of enterprise-wide business intelligence. For example, sales representatives when calling on large customers may lack the required intelligence about cost and quality issues needed to answer tough questions that go beyond the confines of the customer’s organization, like those relating to a union or governmental and financial institutions. To facilitate the flow of information and knowledge to the customer, it may be necessary to access appropriate information and knowledge sources about the area under study via the various Web sites on the Internet. In turn, the resulting business intelligence can be brought to bear on the selected areas under investigation. From this viewpoint, linkage with outside organizations via the Internet can help decision makers to resolve important issues. New Model Needed for E-Commerce Initially, the question can be raised: How does E-commerce survive the coming shakeout? The answer lies in reaching untapped customers with innovative projects through community networking as well as taking advantage of the expectations that persistent connections will engender. The customers that the current model—low prices and low margins—do not engage are the high-margin buyers—people who expect quality products, high-involvement customer service, and personal attention. Typically, these customers require too much individual effort to be profitable in the current model. Dealing with this kind of customer requires a new model that delivers high or normal margins in exchange for real-time interaction, equal communication in both directions, the ability to learn enough about non-commodity items to risk buying them without touching them, and most of all, high-quality personalized customer service from a real person with expertise and authority whose functional intelligence is higher than that of a set of frequently asked questions. It results in a partnership between buyer and seller, at the level of a business-to-business transaction.

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At this time, companies not yet fully committed to the new model have an opportunity to extend themselves to the Internet, using it to amplify their personal touch. Dealing with high-demand customers is becoming technically possible because of the introduction of real-time interaction products, some of which are part of software vendors’ platforms. Free portal mail, WebTV, PalmPilots, Net-Meeting, AIM, and Sametime are available today, and Microsoft’s Windows 2000 and Exchange Platinum, Web cell phones, and Novell’s DigitalMe have just arrived. To better understand the new model for E-commerce, consider a customer who wants advice on a new digital video camcorder. A friend has E-mailed a link to a comparative review that boils down to a choice between two leading models, both hard to keep in stock because of heavy demand. While browsing, the customer notices her friend is on line and available for a chat via an AOL Instant Messenger client. The customer asks which on-line store he recommends and is pointed to an E-commerce site that also allows interactive querying of technically proficient sales representatives. After asking questions about features such as image stabilization, zoom ratio, and audio quality, the customer hones in on price and, more important, availability. The E-store representative offers a toll-free number to close the sale, suggesting that a nearby affiliate store would be the fastest way to get the camera and sending an E-mail link to a map showing how to get there from the customer’s office or home. Before ending the transaction, the E-store offers a technical support and maintenance service package on the Web and bookmarks to discounts on blank tapes and accessories. If the customer chooses a package delivery service, another link points to a tracking page for customer service. Within this tight communication loop, more fully informed customers look at the seller not as a vendor but as a partner. Overall, important factors in the success of the new E-commerce model are rapid adaptation and excellent timing. Market leaders pay for their leadership by adopting new technology early and delivering it to customers when they are ready for it. The current approach is what most on-line shoppers expect. However, companies that manage to bring support for high-touch customer service to market as customers’ expectations rise will most likely be real winners in their markets.4 Organizational Overhaul for E-Commerce To make the transition to a successful E-commerce operation, nearly two thirds (61 percent) of the 250 IT executives surveyed by Information Week Research say E-business has prompted the reengineering of the IT department. And about three fifths (59 percent) say business processes and functions had to be reengineered. Most affected were technological skill requirements, employee training, and cross-functional job descriptions. But employee turnover and job satisfaction also changed as a result of E-business implementation. How successful a company is centers around its E-business strategy and how well it handles the myriad of changes that E-commerce brings about. According to the

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Extraprise Group, companies that succeed in E-commerce share a few common traits. They support or rearchitect internal business processes, have a measurable business objective, have top-level management committed to the program, and make a long-term investment in operations. They also integrate the initiative with existing sales and customer-support programs. As an example of the organization overhaul for E-commerce, reference can be made to Chevron. The oil giant discovered the truth of the axiom that in the information technology field, only 25 percent of the challenge is technology, the other 75 percent is management. When the Houston company developed a supply chain to link the business, suppliers, and retailers nationwide via the Web, it faced significant management hurdles. Essentially, the retail alliance was reinventing its business by setting up an automated supply chain and a state-of-the-art call center. The role of retailers in Chevron’s business structure also is being elevated as a result of the new processes. In the past, the company used fax, phone, and face-to-face meetings with retailers to communicate brand promotions, pricing, and other relevant information. Although Chevron had four or five promotional campaigns during the year, the retail operations generally received little attention from headquarters. Now, retail operations are getting noticed partly to offset losses in the declining petroleum industry. The company has completed a beta test of a new extranet with 50 of its retail outlets. The company credits a solid business plan as the critical piece of the project.5 E-Commerce and Data Warehousing Today, data warehousing is an important element in E-commerce. Accessing data warehouses has been made a lot easier for a company’s decision makers due to the pervasiveness of the Internet and the World Wide Web. The Internet has opened the door to a global business community where they are able to make better informed decisions as well as move more quickly. Following on the heels of the Internet are intranets, which are private networks that leverage the Internet’s infrastructure to extend corporate client/server networks to users wherever they may be. Currently, companies are leveraging the Internet’s infrastructure to extend their corporate networks. The reasons are clear: intranets are easy to use, quickly implemented, cost effective, and an efficient way to make information and knowledge available to the people who need it. The fact is that any authorized user can get to information on an intranet with nothing more than a standard Web browser. Basically, the Web offers a real information and knowledge highway that includes business intelligence to companies who want to extend data warehouse access to others in their supply chain (i.e., suppliers, distributors, and even customers). Web browsers are cheaper than most client tools. And maintenance releases are much simpler to administer, since updating the client application on the Web simply requires updating the server. The bottom line is that the

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Web offers a compelling new way to deliver data warehousing applications that can broaden the reach and value of data warehousing investments. Currently, there is a growing demand for the development of Internet, intranet, and extranet business-to-business transaction capabilities. The Stamford, Connecticut–based Gartner Group predicts that by the year 2004 more than 50 percent of all enterprises will use the Internet for more than 80 percent of their external procurement activities. Meanwhile, almost every company offering products and/or services has developed or is developing Web sites ranging from information-only sites, known in the industry as “brochureware,” to sophisticated Web commerce sites where customers can select and purchase products ranging from music CDs to automobiles.6 In terms of E-commerce and data warehousing, there is a trend toward sharing information and knowledge, including business intelligence, in a company’s data warehouse with its customers, suppliers, and business partners. Sharing data warehouses with others is the evolution of a trend that started when companies realized harnessing the information and knowledge scattered throughout their organizations could mean increased revenue and improved operations as well as better products and happier customers. However, leveraging that value outside the company is not straightforward, since it requires planning and coordination, exceptionally clean data, and increased security and scalability. Still, the potential gain from a tighter, leaner, and more responsive supply chain is causing more companies to seriously consider it. One of the early reasons for data warehousing was to optimize a company’s own business. Sharing data with suppliers is an extension of that. To be more agile, a company has to have a supplier base that is equally agile. The automobile giant General Motors (GM) is using Internet technologies and data analysis tools so it can share its data warehouse with suppliers and, in effect, treat them like other company divisions. GM is using its Supply Chain Data Warehouse, which is available via the Web to more than 5,000 suppliers and supplier organizations worldwide. GM suppliers can log on to a secure Web site via a browser and perform queries on data that resides in a warehouse containing information and knowledge on the quantities of supplies shipped, delivery times, and prices from the short term to the long term. This helps GM suppliers optimize their own product planning, their ability to source materials, and their shipping fulfillment processes. Also, North American suppliers are able to check on warranty claims received by GM for the components they provide GM. In a similar manner, the automaker gives suppliers access to quality metrics stored in the data warehouse. It helps them understand where they may have production problems. That means improved quality on their part and, in turn, improved quality on GM’s part. GM now lets suppliers access on-time delivery metrics so they can measure how well they meet GM’s requirements for delivery. Other companies are creating industry-specific, community-shared data warehouses. More than 200 manufacturers and distributors are participating in IDX-

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change, an industry-wide extranet. Built and operated by MCI WorldCom in a deal with the Industry Data Exchange Association, the extranet’s core is a data warehouse that serves as a place for manufacturers to distribute product information and knowledge using electronic data interchange. Distributors authorized by the manufacturers pay a monthly fee to access the data warehouse and pull information and knowledge into their operational systems. The data warehouse serves as a common ground for suppliers and distributors, holding such content as product specifications, parts numbers, pricing, and packaging quantities—all in standard data formats. Built on Sun Microsystems ES5500 hardware and running Oracle’s Oracle8 database, the warehouse is about 160GB in size and is expected to hold more than 3 million items.7 STEPS NECESSARY TO DEVELOP AND IMPLEMENT SUCCESSFUL BUSINESS INTELLIGENCE SYSTEMS Today, there is no comprehensive BIS approach nor is one anticipated in the near future. Due to this current and future state of BIS development and implementation, a number of steps for a successful business intelligence system can be suggested. Underlying all of these steps is the empowerment of employees (and customers) to have more control and understanding of total operations. The ultimate goal of a BIS for a typical decision maker is not to understand every possible detail about a company’s operations, but rather to do a better job of understanding the company’s innerworkings in order to run it more effectively in the short to long run. Typically, there is an order that should be followed in undertaking these steps. They are as follows: 1. get support by starting at the very top of the company 2. appoint a chief business intelligence officer 3. select an experienced team to develop and implement the system 4. develop the system to produce the desired results 5. select appropriate software tools that meet decision makers’ needs 6. determine a proper organization to acquire, understand, and disseminate appropriate business intelligence 7. develop BIS applications 8. focus on transforming decisions into action

Each of these steps is covered below. 1. Get Support by Starting at the Very Top of the Company The first step in developing and implementing a business intelligence system is to obtain executive sponsorship (i.e., to identify a corporate sponsor). No

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business intelligence system will succeed without a strong advocate at the very highest level. Ideally, it should be the number one or two executive in the organization. If top management support is not there, the system is not going to go very far. This is like trying to undertake a BIS from a grassroots effort. Overall, getting started centers on obtaining the support of the president of a company or, at least, the support of the company’s executive vice president. 2. Appoint a Chief Business Intelligence Officer Additionally, sponsorship must go beyond a corporate executive sponsor and include a day-to-day operating sponsor. Today, this takes the form of a chief business intelligence officer who makes the initial request for the system and oversees its continuing development over time. Sponsorship means more than sitting back and saying, “I’m for this project and will be its champion throughout the development process,” and then delegating all of the work to others. A successful implementation requires a total commitment from the company’s chief business intelligence officer. This person must be capable of not only managing old and present business intelligence but also of putting in motion what it takes to acquire new intelligence. The individual must equip the organization to respond to as yet unknown forces for change. That is, the chief BI officer must question assumptions, learn new technologies, and thrive in the face of uncertainty. As such, the chief business intelligence officer must be capable of operating comfortably in either the business or technological areas of an organization. 3. Select an Experienced Team to Develop and Implement the System The third step centers on the need to establish a project team that is capable of carrying out the implementation of the BIS on a corporate-wide basis. The head of the BIS project team should be knowledgeable and receptive to newer computerized systems and related software technology set forth earlier in the chapter. Ideally, the person should have the time to work on the project full time, have an excellent understanding of both the organization’s and management’s intelligence needs, and have priority access to the chief business intelligence officer. Overall, the BIS project head should have the proper credentials to head the team. Because intelligence is a level higher than information and knowledge and uses data as its raw material, the recruitment of a BIS project team must take this important fact into consideration. To get an idea of the composition of the project team, a balanced mix of technology specialists and business analysts is needed. Technical specialists from the information systems department who are knowledgeable about business intelligence, data mining, data warehousing, computer networking, and security issues, to name some important technologies,

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need to be recruited. Similarly, business analysts who are knowledgeable in identifying, locating, and utilizing business intelligence and knowledge required by the company’s management at all levels need to be recruited in order to complement the information systems professionals. Ideally, the business analysts representing the various business/financial areas should be providing to the company’s managers information and current knowledge and intelligence that focus on the accomplishment of a company’s objectives, goals, and strategies. 4. Develop the System to Produce the Desired Results Underlying the appropriate methodology to develop a successful business intelligence system is the consideration of whether to go with a “push” or “pull” philosophy for delivering intelligence. If the push approach is utilized, this means that the intelligence necessary for company employees, managers, or otherwise to do an effective job is relayed to their desktops. That is, intelligence that is produced by the BIS is pushed forward to decision makers on a routine basis to assist them in their day-to-day tasks. In contrast, if the pull approach is employed, decision makers are provided the tools needed to find the intelligence they need. In the prior part of the chapter, a wide range of business intelligence tools and software was discussed as possible candidates to assist in this pull approach. Needless to say, two different system methodologies have a decided impact on the design architecture. The design methodology set forth below focuses on the push design approach, since the pull design approach is very open ended and varies from one installation to another. An important part of this push approach is the development of a corporate-wide data warehouse. A well-designed, corporate-wide data warehouse assists a company in such processes as data, information, and knowledge acquisition; intelligence inference and explanation; and validation of company strategies, procedures, rules, standards, and heuristics. Concurrent with the development of the corporate-wide data warehouse by the BIS project team is the design of a user-friendly interface for intelligence construction and validation as well as implementation. A user-friendly interface is also helpful for intelligence retrieval and updating. For example, this can take the direction of a graphical user interface (GUI). Related to these design activities is the selection of a suitable underlying methodology for business intelligence. An object-oriented method or one of the other current methods should mesh with the objective of establishing a business intelligence system and meeting the decision makers’ needs. In total, the appropriate BIS design methodology for a push or a pull approach is not a product or a total solution that the project team can buy off the shelf. But rather, it focuses on the capability to link important structured and unstructured factors over time with the changes by which people apply intelligence. The methodology must provide the basis for learning and the compounding of learning—that is, the ability to create longer-term intelligence. Essentially, an

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effective BIS design methodology is a process that is, in actuality, implemented over time and focuses on a thorough understanding of a company’s operations to further a company’s objectives and goals. 5. Select Appropriate Software Tools That Meet Decision Makers’ Needs An integral part of the above BIS design methodology by the project team is the selection of the appropriate data mining or knowledge discovery and business intelligence tools to meet decision makers’ needs. The end result from using these tools is an efficient and effective mechanism for business intelligence acquisition, inference, and explanation. Some of the tools, such as data mining or knowledge discovery and business intelligence software (as set forth in Chapter 4), are excellent candidates for adoption. As will be seen in the remaining chapters of the text, other tools can be employed to demonstrate the usefulness of BIS for a typical company’s functional areas. For the most part, it is prudent to build flexibility into the business intelligence system as decision makers’ needs multiply over time. Appropriate tools today include using the company’s intranets, extranets, and the Internet along with some type of groupware, such as Lotus Notes. By adopting an easy-to-use platform plus standardizing on a single-technology platform, there is easy access to intelligence as well as the ability to connect everyone in the company. From this view, the “best business intelligence practices” for a company in a specific industry can be shared throughout an organization. They are useful for indicating trends and identifying opportunities for improvement. The selection of the appropriate tools by the BIS project team centers around their capability to meet decision makers’ needs—that is, their contribution to assist managers and analysts, including their staffs. Obviously, a business intelligence system will not provide all possible intelligence nor should it attempt to be a total solution because people are the problem solvers. The system is simply a means to give wide support to company personnel. Ultimately, they will have to ask “why,” and they will have to come up with the solution. Overall, not only do knowledge and intelligence software tools need to be selected carefully such that they provide flexibility in understanding a company’s operations, but also the output from these tools should provide decision makers with the capability to improve their effectiveness in their everyday operations. 6. Determine a Proper Organization to Acquire, Understand, and Disseminate Appropriate Business Intelligence Once the appropriate knowledge and intelligence software tools have been selected, it cannot be assumed that data, information, and knowledge exist that can be converted into useful business intelligence. If they do, it cannot be assumed that these items are compatible with a BIS operating mode. Because a

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business intelligence system represents a key tool for leveraging the contents of an organization’s data warehouses and real-time operational systems, there is a need for a proper organization of a business intelligence system to acquire, understand, and disseminate a company’s intelligence. In working through these issues, the BIS project team must give decision makers the capability to acquire, understand, and disseminate business intelligence in new and different ways. These important organizational issues are treated below. In terms of acquiring business intelligence, the capture and collection of data, information, and knowledge is related to an organization’s corporate-wide data warehouses and real-time operational systems (as noted above). No matter the size of the organization, producing business intelligence is not a static source. Useful business intelligence is found, to a large degree, in the sophistication of the methods and processes by which that intelligence can be consistently renewed. From this perspective, business intelligence cannot be preserved for very long without losing its inherent value, which is timeliness. While the technology underlying accounting data warehouses and real-time operational systems changes very little over a five-year period, the technology for engineering and designing integrated circuits changes every few months. Effective intelligence capture is based on its relevance to today and tomorrow. Hence, appropriate utilization of business intelligence is much more difficult than the simple capture of data, information, and knowledge needed for it. Once procedures have been established by the BIS project team to capture relevant data, information, and knowledge, there must be procedures put in place to allow decision makers to get at the business intelligence for a better understanding of a company’s operations. Consider knowledge links, for example. Any single document can lead to an indeterminate number of other links, making navigations almost impossible. To resolve this difficulty, there is a need for intelligent inventory systems that catalog knowledge as it is needed rather than in advance. This approach recognizes that knowledge is changing constantly. Today’s knowledge extraction tools, such as Inxight’s LinguistX product line, support a wide range of functions, from automatic language and character set identification to phrase extraction to automatic summarization for a better understanding of a company’s operations. The dissemination of business intelligence means communicating the implicit nature of not only the “what” but also the “how” of what should be done. While prior information systems, such as OLAP systems, placed the burden of asking the right questions on the users, business intelligence systems have shifted the burden, to a large degree, to the system. At the same time, a BIS provides users with the ability to expand their understanding of the company’s operations over time as they discover new facts about their company that they did not think to ask in the first place. From this view, the project team can pinpoint new directions for transferring intelligence to system users (i.e., decision makers) that were not obtainable with past information systems.

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7. Develop BIS Applications The project team should start with one or more important BIS applications that have wide appeal across the organization. Typical applications could focus on a company’s strategies or its critical success factors (CSFs). A company’s CSFs (noted briefly earlier in the text and to be expanded upon in the next chapter) center on performing those factors that are critical to the survival of a company. If, for example, customer satisfaction or quality are two important CSFs, what business intelligence should be retrievable by managers to understand and improve these areas? In terms of an OLAP system, new sales to existing customers might be the measure of customer satisfaction or quality might be measured by the volume of product defects. In contrast, a thorough understanding of customer satisfaction using a BIS operating mode would go a step further by starting with the customer as the focus for improvement. This would entail utilizing in-depth customer surveys, examining point-of-purchase information, forming customer focus groups, and employing data mining or knowledge discovery techniques. In terms of data mining or knowledge discovery, a store could collect useful information about their customers by asking them for their ZIP codes and correlating this with bar coded data collected at the cash register. In turn, this could provide input for decision makers trying to better understand the demographics of their customers. This same type of approach could be applied to the area of quality for goods and services. The focus would be initially on customers in terms of how they perceive the quality of the company’s offerings—good, bad, or indifferent. In turn, this could lead to internal improvements that impact the goods or services offered to its customers. The linkage of a company’s customers to the employment of business intelligence is a must if the project team wants to show improved results for initial BIS applications. Overall, companies that utilize business intelligence systems have the capability to add to their top and bottom lines through the greater use of superior answers across the organization. 8. Focus on Transforming Decisions into Action The output (i.e., from typical BIS applications) centers on assisting decision makers to make appropriate decisions, which can then be transformed into action. The output from a business intelligence system for decision makers might be useful as is or it may indicate the need for further analysis. The bottom line in terms of placing decisions into action is finding the best balance between a payoff and cost. For example, imagine that a company has found that 25 percent of its customer base is the most profitable, but it has a difficult time determining which 25 percent. A business intelligence system can help the company’s decision makers find that segment and devise programs to maximize its sales. Typically, the use of complex algorithms lets decision makers correlate customer transactional behavior—how recently a customer purchased a product,

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how frequently a customer buys, and the size of the transactions—to factors such as shareholder value, growth strategies, and other business concerns. Business intelligence tools enable decision makers to discover new relationships and trends that they generally would not see. Although building data warehouses and deploying knowledge discovery and business intelligence tools require a tremendous amount of effort, the payoff is that these actions let decision makers keep their fingers on the pulse of the business every step of the way.

CONTINUING SUPPORT OF BUSINESS INTELLIGENCE SYSTEMS If a corporate-wide business intelligence system is developed and implemented correctly, some type of training for decision makers and their staffs is necessary. Typically, they need to know how to use the level of in-depth business intelligence that can be displayed on their screens. As noted previously in this text, there is a tendency for change over time in certain industries. If hand holding of the company’s personnel is undertaken, the BIS stands a much higher chance of being accepted because a decision maker tends not to bother with a user’s manual or even a user’s pamphlet. In some cases, a company may supplement the BIS project team with outside consultants. The use of consultants during development, implementation, and support phases does not relieve the company from acquiring expertise in the management of intelligence. Usually, the consultants have a broader perspective that may help avoid pitfalls and may speed up the learning curve. Outside consultants can give the BIS effort an extra boost and ensure its final success. For effective continuing support within a BIS environment, essentially the project team becomes the support team. However, it usually takes fewer people to support the business intelligence system than to develop it initially. The BIS support team members should have a continuing interest in the system’s success. Needless to say, the implementation of a business intelligence system can cause major changes throughout the organization in terms of altering the flow of data, information, and knowledge to and from the top of the company. Within this context, decision makers can spend more time analyzing and understanding with the view of improving the company’s strategic business direction. Whereas most of a decision maker’s time was previously spent gathering data and information, the time has now been greatly reduced, requiring less than 20 percent of the decision maker’s effort. Thus, time savings allow more time for intensive analysis with the emphasis on understanding and improving a company’s operations. As the world renowned management consultant Peter Drucker has said in the past, that which is measured ultimately improves. Updating this concept to the present time, a BIS environment must provide a basis for learning and compounding of learning in order to create relevant intelligence for understanding and improving a company’s performance over time. In turn, this relevant busi-

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ness intelligence becomes an important asset of a company and needs to be managed effectively. MANAGING BUSINESS INTELLIGENCE OVER TIME This last component in developing and implementing business intelligence systems is the successful management of business intelligence over time. Essentially, this means building intellectual assets or know-how over time that leads to a more thorough understanding of a company’s operations. The focus is on creating a repository of business intelligence decisions and the processes that make them repeatable. From this perspective, business intelligence reaped from a company’s intellectual assets can be used to change the rules of competition in order to redefine the entire market. Unlike its competitors, a company can choose to rewrite the rules to its own advantage, thereby leaving would-be competitors struggling just to keep up. To illustrate, consider Amazon.com. Amazon, which entered the book market, did not try to figure out how to run a better bookstore than everyone else. Instead, it figured out how to do what the rest of competition was not doing. In this case, it meant selling books on line and doing it in a direct, personalized, one-on-one way that customers found appealing. The net result of this enlightened approach was that Amazon.com was able to establish a foothold in a promising market before its competitors were able to take over the market. Maximizing Intellectual Assets By managing business intelligence effectively over time, a company can use it to maximize its intellectual assets. BI management initiative can be used to deliver significant, measurable improvements to an organization’s “return on intellectual capital.” More specificailly, these can include identifying new market opportunities by combining an organization’s competencies in new and more creative ways as well as improving customer support processes so that questions are answered faster and more accurately. There can be a need for radically reducing cycle times (i.e., product development, issue resolution, etc.) by reapplying existing knowledge and intelligence instead of recreating it. There can also be a reduction in the costs of information gathering and decision support that do not add value to the business. Last, but not least, there can be a need to reduce “intellectual hemorrhage” when key personnel leave the organization so that more of their knowledge and intelligence is retained and can continue to add value to the organization. REAL-WORLD EXAMPLES OF SUCCESSFUL BUSINESS INTELLIGENCE SYSTEMS Numerous examples of successful business intelligence systems abound today. For example, the desire for fast-access to up-to-date information spurred Doug

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Brady, the chief financial officer (CFO) for Plante & Moran (a public accounting firm in Southfield, Michigan) with a staff of 1,150 in 17 offices in Michigan and Ohio, to look for a way to extract information from the firm’s IBM AS/ 400 DB2 database and present it in a format that was more user friendly than the standard “green screen.” He also wanted the system to produce reports fast enough to give business unit managers immediate answers to their questions. Creating the business intelligence system was a challenge. The necessary hardware and communications infrastructure had to be in place to facilitate data exchange, and the solution had to be intuitive enough so that training conducted via E-mail and help-desk support would be sufficient. One of the most important steps was to pit solutions from outside vendors against proposals for internally generated solutions. The process required 10 months, but the time spent on the preliminaries paid off. Once the filters for bad transactions had been built, it took the company only a month to clean and cube the data. But high-level views of cubed data do not give the same perspective as conventional database reports, since users have to know that they may see what look like anomalies, at first. Although developing filtering capabilities is not particularly difficult, what is hard is making sure that all the data fits together and that it represents reality. To facilitate this effort, Brady enlisted key managers, who examined and analyzed the cube, pointed out potential problems, and suggested adjustments. Work continued to tweak the cube until it produced the desired results and there were no more anomalies. Plante & Moran’s business intelligence initiative is based on two Cognos applications, the Impromptu report generator and the PowerPlay OLAP tool, which extract data from the firm’s AS/400-based practice management system, and CPA/MIS from Mize, Houser & Company. The Cognos tools run on a Compaq Proliant Windows NT web server that allows staff to access the tools from anywhere in the world through the company’s intranet. From this perspective, staffers can create “what if” scenarios on the fly. For example, if they see that their billing rate went down from $105 an hour to $95 an hour during a quarter, they can examine all the factors that may have contributed to the decrease. As CFO, Brady can look at how various departments are faring, which industries among the firm’s practices are most profitable, and whether staffers are meeting their goals. If the performance of a staff person is not as planned, the analysis tools can help determine whether the individual is falling behind in the ratio of chargeable hours to total hours worked or whether an entire industry segment is simply becoming more labor intensive. BI tools can be used to identify both profitable and unprofitable markets. The ability to compete more effectively is the main reason Republic Indemnity (Encino, California), a provider of workers’ compensation insurance, turned to business intelligence. When deregulation hit the California insurance market, the company tried to use PC-based tools to tap into its reservoir of 30 years of sales and performance data residing on its AS/400 DB2 database. But, with

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millions of records and databases built using non-standard SQL formats, that approach did not work. The solution was to create a data warehouse on the existing AS/400. The company plugged in NGS-IQ from Next Generation Software to massage data and build the tables, Microsoft Access and Excel for queries and reports, and IBM’s Net.data software as the data mining tool. Today, Republic managers can sift through company data in the warehouse and compare it to market data that the company obtains from various state agencies. With a business intelligence solution, managers are able to determine which of the company’s products are profitable and in which markets. Pricing can be adjusted almost instantly to maximize the profit potential for that market. The bottom line is that insurance companies can no longer count on a revenue stream simply because they are in the market. Survival depends on using business intelligence tools to compete more effectively.8 These two examples are interesting from the standpoint of getting the most out of aged data. The future, however, lies in incorporating real-time data, information, and knowledge with aged data to produce the business intelligence output desired by decision makers. Future information technology literature will abound with examples of combining aged data with real-time data, information, and knowledge to provide a better understanding (i.e., intelligence) of a company’s operations. SUMMARY Since business intelligence systems are useful to decision makers by providing them with on-line access to business essentials in these competitive times, there is a need to bring together the appropriate technology, people, and work processes by employing the enterprise application integration (EAI) concept. Basically, the chapter looked at the important components underlying the development and implementation of business intelligence systems. Among the important ones treated were upgrading current information systems to business intelligence systems, utilizing data mining or knowledge discovery and business intelligence software, building effective data warehouses and real-time computing systems, and making the greatest use of computer networking with accent on E-commerce. This background provided a basis for setting forth the steps to implement a successful BIS. In addition, cost justification was examined initially in the chapter for a typical business intelligence system. Although cost justification is somewhat elusive, a BIS operating mode makes a company’s decision makers more effective. That is, by mobilizing the intellectual resources of a company’s people, a knowledge intelligence system gives decision makers a better understanding of a company’s operations and provides for incremental improvements over the short to long term. Lastly, BIS case studies were presented to demonstrate the wide range of these newer type systems. As BI systems develop further, there will be more accent on real-time, or zero-latency applications.

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NOTES 1. Jack Vaughan, “The Real Time Enterprise,” Application Development Trends, June 1999, p. 30. 2. Chris Nerney, “Search Engines: Searching for True Knowledge,” Network World, June 16, 1997, p. 42. 3. Joseph Maglitta, “Know-How, Inc.,” Computerworld, January 15, 1996, pp. 74–75. 4. Steve Gillmor, Jeff Angus, and Sean Gallagher, “New Model for E-Commerce,” Information Week, June 28, 1999, pp. 65–74. 5. Teri Robinson, “Reinventing the Business Wheel,” Information Week, June 21, 1999, pp. 6SS–10SS. 6. Rich Seeley, “Eyes on the Web Commerce Prize,” Application Development Trends, June 1999, p. 47. 7. Beth Davis, “Data Warehouses Open Up,” Information Week, June 28, 1999, pp. 42–48. 8. Samuel Greengard, “How to Profit from Business Intelligence,” Beyond Computing, January–February 1999, pp. 24, 28.

PART IV Effective Business Intelligence Systems Found in a Company’s Functional Areas

7 Strategic Intelligence in Corporate Planning MORE EFFECTIVE CORPORATE PLANNING USING STRATEGIC INTELLIGENCE A number of leading futurists predict that the 21st century will be times of great change. Many of the anticipated changes are not minor perturbations, but major adjustments in business and social environments. Several of the driving forces behind these changes are global competition, the continual restructuring of business organizations, the aging of the U.S. population, continued variations in the inflation (deflation) rate, the volatility of the stock markets, globalization of capital markets, periodic energy shortages, and accelerating technological changes of all types. Future decisions will be more complex than in the past and, to be effective, must merge quantitative and qualitative analyses. Solving the problems of the future and developing new opportunities for the typical company requires the use of advanced computer systems, such as business intelligence systems, to provide top-level managers and their staffs with a more effective approach to strategic intelligence in corporate planning. At the outset, it would be helpful to distinguish between strategic planning and strategic intelligence. Strategic planning is more about breaking down a company’s mission and its objectives into measurable goals. In turn, the expected consequences of these goals are articulated in the form of short- to longrange plans and reports that appear in the form of budgets. From this perspective, strategic planning centers on financial measurement and improvement over time. This point will be evident throughout the chapter. In contrast, strategic intelligence centers on understanding the total picture of where the organization is going today and tomorrow. It is an integral part of executive visioning in a changing world. Strategic intelligence is a forward-looking perspective and an

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articulated vision of the direction that a company should take at the appropriate time and place. As such, it is a guiding force that allows corporate managers the ability to keep their hands on the pulse of the business every step of the way. To assist the typical manager in obtaining a good understanding of this important area relative to business intelligence systems, the first half of the chapter examines the need for top-level managers and their staffs to have a real feel for strategic intelligence in order to make sense out of the coming changes in business from the short run to the long run. For the most part, strategic intelligence for top-level managers and their staffs needs to be tied in with executive visioning and possible disruptive technologies. All of these newer directions and others set the stage for corporate planning factors that lend themselves to effective strategic intelligence. In addition, the utilization of business intelligence systems in long-range strategic planning as well as short-range and mediumrange strategic planning are illustrated in the second half of the chapter. It is expected that future corporate planning will motivate an even greater need for strategic intelligence. Tie-in of Strategic Intelligence in Corporate Planning to Tactical, Operational, and Financial Intelligence Business intelligence, as stressed throughout the text, involves the integration of core data, information, and knowledge with relevant contextual facts to detect significant events and illuminate forthcoming problems and promising opportunities. It includes the ability to monitor business trends, to evolve and adapt quickly as situations change, and to make intelligent business decisions based on uncertain judgments and contradictory facts. It relies on exploration and analysis of related and unrelated data, information, and knowledge to provide relevant insights, identify trends, and discover opportunities. Because business intelligence requires high-quality inputs, organizations must understand the need for and value of a high-quality data resource, the starting point for analysis. The real issue for companies is how to clean up disparate data and produce a high-quality data resource that truly supports business intelligence. Basically, there are several types of business intelligence. More specifically, they start with strategic intelligence at the highest level, which is linked to tactical intelligence. In turn, tactical intelligence at the middle-management level governs operational intelligence for company employees at the lower levels of an organization. All three of the levels are tied in with financial intelligence about a company’s operations. Because strategic intelligence occurs at the highest level, it is oriented toward many sources that are based outside the organization. From this viewpoint, there is a relationship between a company’s critical success factors (to be explored later in the chapter), which are related to outside factors germane to a specific industry and being successful in that industry. In turn, these factors are helpful

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in assisting top management and the corporate planning staff determine what strategic direction the company should take today and, more importantly, tomorrow. Included in this future direction is the employment of problem finding that not only looks at future problems within the context of the current time period but also centers on developing important opportunities for the company immediately and within the next several years. To help develop appropriate strategic intelligence at this highest level, a number of BIS software packages (as noted later in the chapter) are extremely helpful to assist top management and the corporate planning staff in getting a handle on patterns of the immediate past, present, and future operations. Analysis allows these high-level professionals to spot and understand significant trends that impact the total organization. Additionally, other software, such as OLAP and statistical packages, can be helpful. The dissemination of this business knowledge can be made to members in a global organization through the company’s intranets and extranets as well as the Internet’s World Wide Web. For strategic intelligence, the typical time frame is two years and up to five years or more in some cases. For top management and its staff, the business intelligence system must provide specific intelligence upon which corporate strategies and strategic plans can be soundly based. For this task, external intelligence sources that center on economic conditions, technological developments, competitive reactions, and like matters assume paramount importance. These external intelligence sources do not have to possess the greatest accuracy, since corporate strategies and strategic plans are broad rather than very detailed in nature and because they require approximate indications of future trends rather than exact statements about the past or present. However, when combined with internal intelligence, this provides a sound basis for developing broadbased strategic plans that are linked to a company’s mission, specific organizational objectives, and measurable goals. Relationship of Knowledge Management Systems with Business Intelligence Systems in Corporate Planning Essentially, knowledge management systems are useful for making comparisons, analyzing trends, and presenting historical and current knowledge. They enable decision makers to analyze the patterns quickly in order to see what are the most important trends. From this perspective, they represent an accurate predictive approach for decision makers. Knowledge management is not a product per se, but a set of business practices that have as much to do with corporate culture and behavior as they do with technology. Knowledge management translates into effective marketing, better design, satisfied customers, and better production methods. When people talk about the functions that knowledge management requires, they talk about capturing knowledge, reusing it throughout the organization, and collaborating and optimizing business decisions, with the ultimate goal of improving the company’s bottom line. Knowledge man-

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agement focuses on the way an organization’s employees work together and make the most of their available intellectual resources. As knowledge flows over networks, it enables events to take place more efficiently and effectively.1 Today, effective knowledge management centers around the value of the knowledge that is created, stored, and disseminated by the organization. Hence, the question can be raised: How much of the accumulated information and knowledge that exists in the company is actually being used? The answer generally clusters around 35 percent. That is, managers regularly report that their organizations are using only about a third of the aggregate information and knowledge that already exists in their companies. In fact, managers are reporting that their companies are wasting about two thirds of the intelligence that could help their companies succeed. This one-question survey is not empirical science, but it does produce a consistent finding, supported by the examples people will offer in subsequent discussions across the widest spectrum of industries, ranging from software and financial firms to aircraft and auto manufacturers. Additionally, consultants who specialize in identifying and extracting value from a firm’s intellectual capital say that, based on their experience, these figures are correct.2 In view of these findings, there is a great need for organizations to employ more of their intellectual assets. This can be accomplished by utilizing business intelligence systems effectively—that is, knowing what questions to ask because of a better understanding of a company’s operations and storing the appropriate information and knowledge to answer these questions rather than all possible information and knowledge. Accumulating information and knowledge for its own sake is not the proper approach for decision makers. Typically, developing important business questions can be difficult. Although decision makers might know what the important questions are, traditional methods and tools may not be able to deliver the answers. To overcome this problem, an effective approach is to merge the strengths of knowledge management systems with business intelligence software so that the real benefits of both can be realized. Such an approach is found today among some of the leading software developers. For example, the world’s largest privately held software company, SAS Institute, has added a knowledge management extension. Data warehouse and decision support vendor SAS Institute has integrated knowledge management software from Intraspect to create a Collaborative Business Intelligence (CBI) solution. As business intelligence systems have evolved, SAS has added a collaboration component as a natural extension. SAS software brings together the wide range of data that is spread throughout the organization and converts that data into understandable business intelligence. With its CBI solution, SAS is making it practicable for organizations to capture the elements of the decisionmaking process and to make that corporate knowledge and intelligence available to a much wider audience, including front-office decision makers. CBI will allow organizations to protect and increase corporate knowledge and intelligence, and thereby fuel innovation. Typically, these decisions need a collaborative environment where they can be documented, discussed, and refined. Overall, SAS pro-

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vides access to the analytical data, while Intraspect brings knowledge in context for a better understanding of a company’s operations.3 SHORT- TO LONG-RANGE STRATEGIC INTELLIGENCE IN A CHANGING WORLD Because change can be the engine of growth, the challenge lies not in embracing this business tenet, but in anticipating change, adapting to it, and generating fresh ideas that exploit it. Because strategic intelligence in the area of corporate planning is a logical means for adapting to change, it centers on setting or changing organizational objectives and goals as deemed appropriate, obtaining the resources to meet these objectives and goals, and determining the strategies and programs to govern the use and disposition of these resources. Because it occurs at the highest level and is related directly to top-level executives and their corporate planning staffs, strategic intelligence merges the appropriate external and internal factors that are critical to setting the proper present and future direction for the organization. In turn, it provides input for lower- and middlelevel managers. Typically, multiyear strategic planning is intended to align resources and activities with a company’s organizational mission, but conflicts generally arise between the strategic plans and the annual budgets as operational managers focus on short-term financial goals over long-term objectives. On the other hand, strategic intelligence uses tools and methods to model and forecast. In place of planning specialists, learning organizations may rotate all managers through planning departments as part of their professional development. Strategic intelligence centers on a changing world and how best to keep the organization in step with changes from the short run to the long run. Strategic intelligence relative to future market opportunities and products to fill them are basic to long-range strategic plans. A distinctive characteristic of this highest planning level is the use of marketing facts to discover opportunities and then develop effective strategies and programs to capitalize on these opportunities. Similarly, the focus is on bringing future problems back to the present for solution. The long-range strategic plans, which embrace all aspects of the organization and its environment, provide a basis for more detailed mediumrange strategic planning. Medium-range strategic plans, sometimes called tactical plans, are concerned primarly with fiscal planning to place the organization in the best financial position for the next several years. This fiscal planning involves developing the operating programs and associated budgets for the next several years. On the other hand, short-range strategic plans, or detailed operational plans, are related to the financial plans of the current year only. For an organization that has practiced formal planning on a regular basis, it is normal for every major functional area to prepare annual plans for the coming year. Essentially, this financial planning is brought together from a detailed examination of the key measures

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of the business, such as product line profitability, variable and fixed costs, inventory turnover, manufacturing capacity, and financial ratios for the coming year. Using Strategic Intelligence to Make Sense Out of Chaotic Times and Disruptive Changes To help the typical company make some sense out of today’s chaotic times, such as the velocity and volatility with which trade, capital, and currencies move around the globe, it is necessary for decision makers to employ strategic intelligence to its fullest. The American chief executive officer of Britain’s Cable & Wireless notes that “the Chinese character for crisis combines characters for danger and opportunity.” There is promise as well as menace. The best businesses can do is to apply several approaches to make their companies winners in the accelerating shakeout of industries around the world. As one approach, a company can intensify its intelligence gathering both internally and externally. In this tumultuous period, using published statistics to guide decisions is not the way to go. By the time employment figures rise or fall or retail sales rise or fall, it is too late. Typically, decision makers possess valuable untapped marketing intelligence within their own companies, if only they can get to it. The chief executive officer of DuPont holds a bi-weekly phone conference with 20 top managers around the globe to stay abreast of changes in customers, competitors, and local economies and politics. He asks different, pointed questions each time: What is happening to customers and their customers? What about the political will of local leaders to deal with the downturn? What should be done now to meet changing competitive rules? The sessions are not just for the CEO’s enlightenment, but also for others. By hearing the answers from their peers, managers broaden their perspective of the global landscape. Another approach is to seize new opportunities created by the crisis and, at the same time, stay relentlessly on strategy, since all ships are being tossed by the same storm. The unsound may run into trouble and that can open up opportunities if a company is alert. At least three major financial services companies—AIG, Travelers, and Merrill Lynch—have acquired distribution systems in Japan as that country’s finance industry has struggled. Cargill has been trying to crack the Japanese market for 30 years. Now the troubles of a Japanese competitor may finally give it a chance: Cargill announced recently that it would buy Toskoku, a Japanese food-trading company that has filed for bankruptcy. Tough times can also be ideal for forging new business relationships that will serve long-term goals but that previously just had not seemed urgent. Current conditions have led Orion Capital, a Connecticut-based property and casualty insurer, to investigate strategic partnerships with several insurance carriers, distributors, and service firms. Still another approach is tightening up operations. There is no substitute for

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good business fundamentals—customer satisfaction, cost, quality, cycle time, and brand. But some ways of achieving them are smarter than others. For example, DuPont has centralized and streamlined its efforts to combine purchasing by diverse units around the world to get better prices from vendors—a process it calls “vendor convergence.” Managers who see an opportunity to combine purchasing with another unit decide whether it would be the right move for all the businesses involved. They have ultimate authority, which they normally would not have, to make these deals happen.4 Disruptive changes in a typical company’s markets are tied in with making sense out of chaotic times. Disruptive changes can be caused by a number of factors related not only to a company’s markets but also to a company’s size. When a company is young, its resources (i.e., its personnel, equipment, technologies, brands, and the like) define what it can and cannot do. As it becomes more mature, its abilities stem more from its processes, such as product development, improved manufacturing, and financial capabilities. Because companies, independent of the people within them, have capabilities, those capabilities also define disabilities. As a company grows, what it can and cannot do becomes more clearly defined in certain predictable ways. In the largest companies, values, particularly those that determine what are its acceptable gross margins and how big an opportunity has to be before it becomes interesting, define what the company can and cannot do. Because resources are more adaptable to change than processes or values, smaller companies tend to respond to major market shifts better than larger ones. Hence, it is suggested that companies capitalize on opportunities that normally do not fit in with their processes or values. The bottom line is that all companies start with an understanding of what they are capable of doing and create a framework that managers can use to assess the abilities and disabilities of their organizations as a whole.5 Utilization of Strategic Intelligence in Executive Visioning An important element underlying strategic intelligence from the short range to the long range is executive visioning. Executive visioning is often tied in with problem finding. An executive view entails farsightedness along with the eagerness to look ahead from a practical viewpoint. Effective executive visionaries are not necessarily those who can predict the 21st century and beyond accurately. But rather, they are decision makers who can draw a conceptual road map from where the company is now to some imagined future, who can say, “This is how we get there.” Visioning implies a change from the status quo, which helps explain why visionaries are overrepresented in the ranks of entrepreneurs and why they come in handy to an organization in deep trouble—think of Mr. Lee Iacocca saving Chrysler. Vision is not for the complacent. While the executive visionary sees things a bit differently, this individual is no mystic. The person’s sources of information are down to earth—customers and sup-

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pliers, for example—and extend beyond his or her gut-level feelings. The most visionary executive can take in large amounts of information and knowledge, and not just from inside himself or herself. Typically, a broad grounding in a particular industry is almost always a prerequisite to successful direction setting. It is helpful to look at the early career of an executive who comes to be regarded as visionary. Usually, such individuals found an assignment or a series of assignments that enabled them to see the company from many different perspectives—that is, they served as vice president of marketing, then production, then finance; or had a tour as executive assistant to the president. For example, Jan Carlzon held all sorts of positions in the travel business before becoming head of SAS, and Louis Gerstner studied the financial services industry as a McKinsey consultant before taking over American Express’ credit card and traveler’s check businesses. From another view, there can be situations where the executive does not need to be steeped in an industry to conceive a vision of its future. Upstarts like Steve Jobs dwell on the dangers of falling prey to the Standard World View, thereby missing opportunities that arise when that standard world begins to show a few cracks. The skeptics concede, though, that someone who has grown up in the business stands a better chance of realizing his or her vision. Executives with vision typically share a couple of other characteristics. They have a high degree of self-confidence. It takes a lot of inner strength to imagine a future at variance with the common expectation and to sustain that image in the face of responses ranging from incredulity to derision. The visionary may also be a bit of a loner. Ego strength can mean that the individual has less need of other people. But executive vision has a way of exciting others somehow. An executive visionary appeals to the emotions and aspirations of people in a way that goes beyond the usual carrot and stick approach. But it is not enough for the executive to possess vision by itself. The executive visionary must be able to communicate what he or she has dreamed, and the company must have the required skills needed to execute that dream. The leaders of the organization must act consistently with the vision in everything they do. Too often in the past, top management teams worked up a statement of corporate vision, promulgated it, and then thought their work was done. What they overlooked, and what dooms this kind of superficial effort, is the need to plan and manage this vision over time. A business intelligence system, backed up by appropriate information systems, is an excellent vehicle for assisting in the fulfillment the executive vision. Survival Strategies for Local Companies Competing in Global Markets Today, visioning includes taking into account survival strategies for local companies competing in global markets. Survival strategies for local companies when multinational companies arrive can take several directions. For one, local

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companies can defend their territory by leveraging their local assets in market segments where multinationals are weak. For another, locals can focus on upgrading capabilities and resources to match multinationals globally, often by keeping to niche markets. A more proactive approach for local companies is to focus on expanding into markets similar to those of the home base, thereby using competencies developed at home. In a worst case scenario, when globalization pressures are strong and a local company has no competitive assets that it can transfer to other countries, it needs to retreat to locally oriented links within the value chain. But if globalization pressures are weak, the company may be able to defend its market share by leveraging the advantages it enjoys in its home market. Many local companies have assets that work well in other countries. Those that operate in industries where the pressure to globalize is weak may be able to extend their success to a limited number of other markets that are similar to their home base. Also, those operating in global markets may be able to contend head-on with multinational rivals. Overall, by understanding better the relationship between their assets and the industry in which they operate, executives from emerging markets can gain a clearer picture of the options they really have when multinationals move in for the long haul.

The Opportunities of Knowledge-Based or “Smart” Products and Services In order for companies to prosper in this 21st century, they need to have a definitive strategy regarding knowledge-based or “smart” products and services (refer to Chapter 1). Essentially, smart products filter and interpret information that allows the user to act more effectively. These products can be identified by such characteristics as being interactive and smarter the more they are used. Also, they are capable of being customized to fit a customer’s changing needs and can be tied in with the capability of preventive maintenance, where deemed appropriate. For example, diapers that change color when wet and tennis rackets that glow where they strike the ball are smart versions of frequently used products. Typically, smart-based products and services have relatively short life cycles. Patent protections on intellectual property are still not nearly as developed as they are on “hard” technologies, so the half-life of proprietary information is short. For example, the foreign exchange advisory services offered by commercial and investment banks are highly specialized and the products are often customized for corporate clients. Because those products depend on the existence of certain market conditions, their viability is short lived. Yet because information about the markets is widely disseminated, proprietary products can be copied quickly by competitors. In order to maintain their proprietary edge, banks must constantly upgrade their products. The managerial challenge for

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those running foreign exchange advisory services is getting their professionals in New York, London, and Tokyo to cooperate so that they can develop the next generation of offerings faster than their competitors can. An integral part of smart products and services is that a company’s customers become learners when they use smart products, which both obliges and helps them to learn. Companies will move toward making their offerings smarter because they will profit from doing so. Essentially, when their customers use those products, they will be engaging in an educational process. Seeing customers as learners requires a major change in thinking. Hence, companies will come to think of their customers as learners and of themselves as educators. They will promote the learning experience for profit and their customers will profit from that experience. Companies that know how to convert information into knowledge in the form of products and services for their customers will be very successful.6 Corporate Planning Principles Underlying Strategic Intelligence Numerous principles that start with this chapter actually run through the next chapters, thereby providing an underlying framework for a typical company (manufacturing or service oriented). Rather than discuss all of these principles, only the first ones in Figure 7.1 are discussed. An important corporate planning principle is a recognition that change is a constant. Generally, fundamental changes to an organization can take up from five to 10 years to complete, while superficial improvements can be achieved in a much shorter time frame. Because change is considered to be a constant in today’s business world, it is necessary to consider this factor in determining (a) a company’s mission, (b) identifiable objectives, and (c) measurable goals and strategies. A company’s mission gives direction to its organization efforts. A company’s mission, for example, might be to be the dominant supplier to the most profitable market segments and to provide a high-quality employee work environment. Identifiable objectives, regardless of how general they may be in their conception, become a measure of success in very real terms. They should be looked upon not as a reactive approach to setting the direction for a company but rather as a proactive force to realize successful organizational objectives. These objectives, such as competing in profitable market segments and being the low-cost producer in the company’s market segments, provide the basis for determining measurable goals, such as increasing market share by 12 percent in high-profit market segments plus reducing manufacturing costs by 8 percent per unit and achieving a 22 percent return on investment. In turn, these goals provide a basis for developing appropriate strategies that realize the company’s mission. For example, strategies could include expanding market research activities to identify high-profit market segments and develop one or more product lines that fit the requirements and needs of these high-profit market segments. The successful

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Figure 7.1 Corporate Planning Principles Underlie Strategic Intelligence

linkage of a company’s mission to its objectives, goals, and strategies requires knowledge of a company’s total business operations. As such, the principle of effective utilization of a company’s resources applies here and underlies the entire strategic planning function.

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CORPORATE PLANNING FACTORS THAT ARE RELATED TO EFFECTIVE STRATEGIC INTELLIGENCE Because change is regarded as a constant, it creates important opportunities. But if ignored or unanticipated, it can be devastating. The business community showcases change and its consequences like nothing else. The $30 billion-ayear package delivery business, for example, would be significantly smaller had Federal Express and UPS not anticipated a surge in worldwide demand for timeguaranteed delivery. To assist corporate management in adapting to all types of changes, including changing computer technology, important factors that are useful to better understand strategic intelligence are set forth in this section. These center on the following: (1) capitalizing on E-commerce via the Internet worldwide, (2) enlarging one’s view of core competence to develop new opportunities, (3) using competitive intelligence to find out what competition is doing, (4) developing and measuring a company’s critical success factors (CSFs), and (5) using corporate planning software for strategic intelligence. It should be noted that problem finding underlies all of these factors and is noted briefly below. Underlying effective strategic intelligence centers on problem finding in which there is a proactive approach such that external and internal environmental factors that affect the organization, from the short range to the long range, are taken into consideration. The most important external environmental factors focus on customers, suppliers, the government, investors, the public, financial institutions, and competition. The most important internal environmental factors center on organizational strengths and weaknesses, organizational objectives and goals, functional areas of the organization, and the organization’s personnel. A thorough evaluation and understanding of these factors is an integral part of the strategic planning process. Overall, the raw material of strategic intelligence is needed by top management and its corporate planning staff to initiate problem finding. Likewise, it is the means by which strategic decisions are made to further organizational objectives and goals. To have an effective planning process, top-level managers and corporate planners need to identify potential problems in the future and bring them back to the present time for resolution. Problem finding tends to center on finding solutions to problems that may be deemed impossible to solve. Going one step further, it is necessary to identify opportunities related to future problems. As in the past, top-level managers and corporate planners must also be involved in problem finding to allocate and use the organization’s resources effectively. An important benefit from problem finding is that, if a typical manager is warned early enough to take corrective action, the manager can prevent a molehill from becoming a mountain. What is it worth to a manager to be warned of a business problem sometime sooner? What it is worth to the manager and to the company is the avoidance of a crisis. Many times, it is too late to react to problems that are already out of control. This strategic advantage from using

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problem finding within a BIS environment, while difficult to quantify, is very significant and real for the typical company today. For more information on problem finding, reference can be made to Chapter 3 and a number of publications by the author.7 Capitalizing on E-Commerce Via the Internet Worldwide A most important strategy for the long run is centered around electronic business technologies, which have changed the ways of the business world. Ecommerce provides direct access to new markets, strengthening relationships with customers and other business partners, cutting costs by eliminating unnecessary paperwork and processes, and empowering employees with better education, communication, and information access. All elements of a company’s supply chain, for example, can be linked via E-commerce. If a person buys a pair of Nike running shoes, that information can be transmitted back to the plant in Taiwan where the shoes are made, and the shoes can be express delivered directly to the person’s home. Similarly items purchased electronically from several suppliers in the same region could be shipped in a batch using electronic interfaces. A whole host of intermediaries in the chain who used to make their living coordinating and moving information can be eliminated. Intranet-tointranet communication is blurring the lines between companies. The notion of where a corporation starts and stops is very different today. As another example, a company’s expertise might be harvesting timber or processing lumber, but the company also needs to move its products to the construction industry. Traditionally, all those steps were brought together in a soup-to-nuts operation. Now, given interconnectivity, someone else can run a company’s truck fleet, but it will still operate like one’s own fleet. To capitalize on E-commerce via the Internet and the World Wide Web, companies must go beyond their present efforts. In a current research study, executives were asked to name the most critical technologies for adoption in the next five years. Eighty-one percent of North American information system executives chose the Internet and the Web, and 63 percent chose E-commerce. There is some irony in the choice of the Internet and the Web as the most critical technology. Eighty-six percent of North American respondents reported that their companies have a Web site. However, 65 percent of them do not believe that their company has an effective strategy for using the Web to achieve their business objectives. The real fact is that only 12 percent of North American companies with a Web site have achieved any kind of optimal utilization.8 Going beyond these research results, companies must come to grips with this important fact: E-commerce on the Internet will go way beyond what is happening today. The truth is that the greatest share of E-commerce revenue will not go to high-profile retail sites such as Amazon.com and eBay. These are consumer commerce ventures, selling one product at a time to individual customers. This is an admittedly huge market, but it is not the largest. The bulk of

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E-commerce revenue will come from transactions between businesses, in which both purchase volumes and dollar values already dwarf the consumer commerce market. The business-to-business E-commerce market presents an opportunity so big and appealing that there will be many minimonopolies. The vastness of these opportunities is what has spurred interest in on-line industry marketplace— vertical markets in which companies within a given industry can easily buy and sell goods and services with one another. For example, Chemdex (www.chemdex.com) is an on-line industrial chemical marketplace that lets buyers and sellers of industrial reagents more easily find one another, look up information about the chemicals they need, and make online purchases. What it takes to make a good marketplace is an E-commerce infrastructure tailored to the particular needs of the industry’s buyers and suppliers, and a rich supply of information about the products available throughout the industry. This means that entrepreneurs with intimate knowledge of specific industries have a great opportunity to create such marketplaces and cash in on the trade they generate.9 A better known example is the automobile industry where Ford and General Motors have joined Daimler-Chrysler in a new company that centers on a single exchange for electronic procurement, planning, fulfillment, and collaboration. This recently formed company is the result of pressure from auto parts and materials suppliers, who resisted the prospect of supporting multiple single manufacturer trading exchanges. Although Ford’s AutoXchange and GM’s TradeXchange were operational, Daimler-Chrysler had an exchange on the drawing board prior to the formation of the new company. The main thrust for the bringing together of the auto manufacturers were the suppliers who were asking, “What are you doing? You’re going to make us support three standards.” In effect, their suppliers did not want to operate over different standards and exchanges. The Big Three automakers have developed a single, equally owned exchange and invite other auto manufacturers to participate on an equity basis. The merger creates strange bedfellows beyond the Big Three. Oracle, whose application software underpins Ford’s AutoXchange, and Commerce One, whose MarketSite software powers GM’s TradeXchange, have developed an architecture for effectively integrating their competing and proprietary exchange software. Underlying this architecture is XML (eXtensible Markup Language) as a common language. Thus, within any given industry, such as in the examples above, both sellers and buyers can benefit from E-commerce in terms of time and cost savings.10 Enlarged View of Core Competence to Develop New Opportunities In order to create new markets for products and services, early and consistent investment in what is called “core competencies” is one important factor. In turn, corporate imagination and expeditionary marketing are the keys that unlock

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these new markets. A company that underinvests in its core competencies, or inadvertently surrenders them through alliances and outsourcing, generally robs its own future. But to realize the potential that core competencies create, a company must also have the imagination to envision markets that do not yet exist and the ability to stake them out ahead of the competition. In a few words, a company must anticipate what the customers want before they are aware of what they want.11 A company will strive to create new competitive space only if it possesses an opportunity perspective that goes far beyond the boundaries of its present businesses. This perspective identifies, in broad terms, the marketing territory top management hopes to stake out over the coming years, a terrain that is unlikely to be captured in anything as precise as a five-year marketing plan. The initial enthusiasm that several Japanese companies brought to developing highdefinition television (HDTV) grew out of just such a vision. Creative considerations of the many new opportunities that might emerge if HDTV could be made a reality led them beyond the traditional boundaries of the color television business to identify potential markets in cinema production, video photography, video magazines, electronic museums, product demonstrations, and training simulations, among others. As this example demonstrates, a company’s opportunity perspective represents its collective imagination of the ways in which an important new benefit might be harnessed to create new competitive space or reshape existing space. Commitment to an opportunity perspective does not rest on ROI (return on investment) calculations but on an almost visceral sense of the benefits that customers will finally derive should pioneering work prove successful. The more fundamental the envisioned benefits and the more widely shared the enthusiasm for the opportunity, the greater the company’s perseverance will be.12 Although there is a need for core competencies to create new markets, there is also a need for an enlarged view of these competencies. Conceiving a company as a group of core competencies rather than as a group of products and services is one way to extend the opportunity perspective considerably. Because Motorola sees itself as a leader in wireless communications, it is not just a producer of paging devices and mobile telephones, but rather the company’s charter permits it to explore markets as diverse as wireless local area computer networks and global positioning satellite receivers. Ajinomoto, a giant grocery products company, is not only in the food business but also applies the skills it has mastered in fermentation technology to produce an elastic paper for Sony’s top-end headphones. The point to be made from these examples is that if company managers are not able to think outside current business boundaries, they will miss important new opportunities that depend on the combination of skills from several sources. Core competence with the proper application of knowledge should prosper and flourish over the years to the betterment of all parties concerned. Dominance in core products and services, then, allows companies to shape this evolution so as to keep one step ahead of competition.13

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Using Competitive Intelligence to Find Out What Competition Is Doing An integral part of a typical company’s strategic intelligence is competitive intelligence. Although it has been around for years in one form or another, competitive intelligence increasingly is being seen as a necessity by many companies trying to keep up with rapid changes in their markets. Whereas knowledge management deals primarily with the collection and organization of information within an organization over the short run to the long run, competitive intelligence is all about collecting outside information about competitors’ strategies, emerging technologies, regulatory issues, customer or supplier activities, or changes in the market in order to take appropriate action. Competitive intelligence also includes using every legal and ethical means to gather information about the business plans and practices of competitors so that a company’s performance can be improved in the marketplace. The bottom line of competitive intelligence is that a company’s management knows where competitors and markets are going to go next. Overall, finding out what a company’s competition is doing so it can respond can be related to competitive intelligence software. Cipher’s IntelAssist (Intelligence Assistant) is one of the first groupware products optimized for competitive intelligence professionals and their constituents in the corporate environment. This software not only shows what the competition has done historically but also is useful for predicting what the competition will probably do next. Other useful software to accomplish competitive intelligence includes Microsoft Access, a database management package; a knowledge management application from Wincite Systems; and Lotus Notes. Currently, the real action in competitive intelligence is focused on services rather than software. Among the companies using the World Wide Web to deliver competitive intelligence services to corporate clients is Current Analysis (an on-line competitive intelligence firm specializing in the information technology industry). The core expertise of Current Analysis (based in Sterling, Virginia) is in-depth analysis of vendor actions from a tactical as well as a strategic perspective. This firm, which promises a turnaround time of 24 to 48 hours following an industry event, offers four specific on-line services (www.currentanalysis.com). Other consulting firms offer a wide range of competitive intelligence information and reports. Development and Measurement of a Company’s Critical Success Factors For any business, critical success factors (CSFs) are the limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance. They are the key areas where things must go right if the organization is to flourish. If results in these key areas are not adequate, the organization’s efforts for the period will be less than desired. As a result, the critical

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success factors are areas of activity that should receive constant and careful attention from management. The current status of performance in each area should be continually measured, and that information should be made available to the higher levels of management. Typically, critical success factors support the attainment of measurable organizational goals. These goals represent the end points that an organization hopes to reach. Critical success factors are the few areas in which good performance is necessary to ensure attainment of those goals. For example, the automobile industry has four industry-based CSFs: having the right styling for the times, having a good-quality dealer system, having effective cost control over selling and manufacturing operations, and having the capability to meet current energy standards in terms of average mileage per gallon for all cars produced. Though the automobile manufacturers must pay attention to many other factors, these four areas represent the underpinnings of successful operations. From a broad viewpoint, the principal sources of critical success factors have been identified by John F. Rockhart at MIT’s Sloan School of Management. They are as follows:14 • Structure of a particular industry. Each industry by its very nature has a set of CSFs that are determined by the characteristics of the industry itself. • Competitive strategy, industry position, and geographic location. For smaller organizations in an industry that is dominated by one or two large companies, the actions of the major companies will often produce new problems for the small companies. The competitive strategy for the latter may mean establishing a new market niche, getting out of a product line completely, or redistribution resources among various product lines. • Environmental factors. As the economy changes, potential factors change, energy problems become more acute, and the like, critical success factors can change for an organization. • Temporal factors. Internal organizational considerations often lead to temporal critical success factors. Inventory, for example, which is rarely a CSF for top management, might become a high-level CSF if there is far too much or too little stock.

Although these four sources for identifying CSFs are determinable, critical success factors are different for an individual industry and even for companies within that industry. Going one step further, the concept of key performance indicators (KPIs) is a way of formalizing and describing critical success factors. Fundamentally, a comprehensive CSF system has four basic components, which are shown in Figure 7.2. First, executive visioning that centers on broad-based intelligence of a company sets the company’s overall direction and is linked to its corporate objectives and goals. Second, general company objectives are related to overall corporate goals, which are then broken down into appropriate measurable goals

Figure 7.2 From Executive Visioning to Corporate Objectives and Measurable Goals to Critical Success Factors (CSFs) to Key Performance Indicators (KPIs) and Financial Ratios within a BIS Environment

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for divisions and business units. These goals provide the means for developing appropriate corporate strategies and programs. Third, each business unit identifies a number of critical success factors that must be performed well to achieve its goals and strategies as well as carry out its programs. These activities are then assigned to the people responsible for their completion. Fourth, each business unit establishes a measurement system to quantify success. These measures are the key performance indicators, which normally include a number of financial ratios. Reference to KPIs and financial ratios is also found in Chapter 10. For example, a company has decided that one of its corporate goals is to improve customer satisfaction by 10 percent this year. At the corporate level, a periodic survey of customers would be a key performance indicator, since corporate managers are responsible for the overall corporation effort. Measurable activities that contribute to improvements might be improved product quality, improved customer service and support, improved delivery times, and more customer-suggested product improvements. Each of these activities suggests its own set of key performance indicators. The most important part of this process is that the KPIs are measures that the people responsible for them can actually control and be held accountable for. And because of this combination of responsibility, control, and accountability, these KPIs, including financial ratios, are certain to be relevant and important to the managers assigned to them. An alternative to CSFs are information leverage points, which are those areas of a company that are really critical to its operations. They are the most important pieces of information in a company, since they can tell management whether the company will succeed or fail. Uncovering these points and applying them require some basic rules of thumb. For example, one rule suggests that a decision maker go where the money is. What are the key drivers of cost, revenue growth, and profitability in a company? It is surprising how little good information companies have about their money’s source and its destination. That does not mean that one has to focus on standard accounting information. The real task here is to construct a relatively simple model that describes how the company makes money. In turn, there is a need to attach coefficients or weights to the different elements of the model. Another rule suggests using statistics, but only simple models. The average sale or the medium wage of a production line worker is calculated. In turn, those figures are multiplied by the number of things measured, and a useful measure of the big picture emerges. Still another rule centers on information that really matters to customers, vendors, employees, and so on. This could mean visiting workers on the production line, getting together with customers, even talking to a competitor at a trade show. Information leverage points seldom make themselves known to those who sit in offices. Based on these sample rules, a creative decision maker should have no difficulty in identifying information leverage points. It should be kept in mind that the information that matters today may not matter tomorrow and, therefore, uncovering those points is a continuous process, not a one-time project.15

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Using Corporate Planning Software for Strategic Intelligence Today, there is a wide range of corporate planning software for strategic intelligence that is available to top management and its corporate planning staff. Examples of business intelligence software for intelligent and strategic applications include Oracle’s Oracle Business Intelligence System (BIS) and a suite of Strategic Enterprise Management (SEM) products. The Oracle Business Intelligence System is a collection of enterprise business indicators (Reports), supported by a suite of analytical workbooks (Discoverer), all integrated with an alert mechanism to proactively communicate operational performance to the management team. It is a Web-based application. Users work from a configurable home page where they view actual business activity and graphs of performance measures and variances and launch all reports. The Business Intelligence System also provides a library of key performance indicators for benchmarking actual performance against multiple targets, such as industry best practices, key competitors, personal goals, and corporate commitments. Basically, it is a family of separate yet integrated intelligence modules for Financials, Purchasing, Human Resources, and Process Manufacturing. The Business Intelligence System provides integrated information access across all applications through predefined reports, analytical workbooks, and performance measures. It communicates critical business information to all users in the enterprise, giving executives, managers, customers, suppliers, employees, and others timely information for making informed decisions. Because the long-term strategic objective of any company is to increase shareholder value, Oracle SEM facilitates this objective. It is a suite of analytic applications that support the implementation of strategic management techniques such as activity-based costing (ABC) and management, value-based management, and Balanced Scorecard. By leveraging these techniques, executives can coordinate strategic planning, measure progress against the plan, identify and recommend improvement opportunities, and compensate based on performance. A complementary addition to ERP systems, Oracle SEM lets executives incorporate advanced management analytics as an integral part of enterprise-wide information systems. In addition, Oracle BI tools (i.e., Oracle Discoverer, Oracle Reports, and Oracle Express) are designed for the Internet. Any user can access corporate information in a secure environment from anywhere, at any time, using a standard Web browser. The tools are designed to address the full range of user requirements for information publishing, data exploration, and advanced analysis. An example of knowledge discovery software that can be of assistance to management and its corporate planning staff is KnowledgeX. Essentially, this software can discern hidden relationships among people, organizations, and positions. It is able to take information, organize it, and extract from that material the connections and webs of relationships. Using Intelligent Knowledge Exchange, KnowledgeX can perform a text processing task called “SmartParse”

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to do the selection and extraction of textual information. If it encounters terms it already knows, it will automatically assign the correct set of connections to that term. If it does not know the term, the user can define it once and all future uses of that term will be connected according to rules. KnowledgeX is object oriented. It allows multimedia information to be accessible from within the program. Once the relationships are established, KnowledgeX has the ability to display these connections in a graphical way or as a text outline. The graphical display is easy to grasp and often reveals relationships that had been buried within the text and not obvious to the researcher. By creating maps showing relationships among customers, companies, individuals, and organizations, KnowledgeX makes it easy to grasp how these people and organizations are interconnected. The more information that the knowledge base contains, the better that KnowledgeX is at finding unknown and unnoticed relationships. In addition, KnowledgeX can combine information entered by many people on the network and find the connections within that information. As such, fragments coming from other people can be converted into something like a centralized knowledge source. That is, KnowledgeX takes information and knowledge coming from many sources and reveals relationships that had never been known before because the pieces were too fragmented and dispersed among too many people. From this perspective, this knowledge discovery software is quite useful in bringing together diverse elements needed for resolving critical strategic planning issues. Typical business planning software packages include the following. Avantos ManagePro helps managers plan and track goals and progress, while fostering success through focused management activities. It includes goal planning and tracking tools, such as the Top Level Goal Planner, for planning and delegating key business objectives, strategies, and tactics. The Goal and People Status Boards enable managers to monitor the status of primary business goals and obtain at-a-glance reinforcement of where to focus attention. Jian BizPlan Builder is a complete business plan template on disk with more than 90 typed pages of example text that are formulated into word processing files. Templates use standard spreadsheet applications like Excel or Lotus 1-2-3 to calculate financials and generate graphs. The step-by-step format guides the user, explains issues, and gives clear and sensible advice. Once a company’s business plans have been developed (from the short-range to the long-range), software can be employed that is useful in determining the company’s overall financial performance. Such software goes beyond monthly actual versus budget reports that are routinely produced by today’s information systems. Currently, companies are employing the use of corporate scorecard software, which is a sophisticated business model that helps a company understand what is really driving its success. In effect, it acts like the control panel on an airplane—that is, the business equivalent of a flight speedometer, odometer, and temperature gauge all rolled into one. A corporate scorecard keeps

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track of a company’s financial progress as well as its softer measurements. These range from customer satisfaction to return on investment and need to be managed to reach a company’s final destination (i.e., profitable growth). A scorecard, for example, might graph customer service to determine if it is improving or deteriorating and, at the same time, tally product defects to determine if they are rising or falling and where. Going a step further, scorecard software, which is usually distributed throughout a company’s computer network, lets company employees across the entire organization be certain they are talking about like items when they get together. If customer satisfaction is declining, sales, manufacturing, and research and development will all be reading the same score, and thus will be able to tackle the problem from a common-ground perspective. Today, there are two views on utilization of scorecard software. One is that a company’s yardsticks should be purely financial. Managers should employ indicators like revenue growth and return on investment to guide a business. The other is that a corporate scorecard should be balanced. A company, for instance, should not only keep close watch on performance numbers like gross profit margins on new products but also use softer measurements. Other softer measures include the number of new products, product development cycle times, and the rate of on-time deliveries. The bottom line from this view is that it forces a company to evaluate critically those drivers that really determine its performance over the short run to the long run.16 Although the above corporate planning software for strategic intelligence and its variants are widely used today, no software or technique can replace experience and knowledge about a business or its markets that is necessary for effective strategic planning. Personal business intelligence and knowledge has the potential to increase the quality of strategic planning. However, software can provide a means for conducting complex data analysis to discover patterns, relationships, and associations. Such patterns and the rules inferred from them can be used to guide decision making and even predict the outcome of those decisions by corporate planners. For best results, both are needed for successful strategic planning. CORPORATE PLANNING FUNCTIONS THAT LEND THEMSELVES TO STRATEGIC INTELLIGENCE In order to understand business intelligence better in corporate planning, marketing, manufacturing, and finance, reference can be made in this chapter and succeeding ones to either a manufacturing-oriented company or a serviceoriented company. In either company, the corporate planning system is linked directly with other systems. That is, planning activities are related to external environmental factors through a company’s mission to its organization’s objectives and goals. In turn, these important relationships are related to a company’s internal environmental factors (i.e., marketing, manufacturing, and finance that are linked to human resources). All of these external and internal environmental factors form the basis for short- to long-range strategic planning within a BIS

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operating mode. The BIS’s capability to extract on-line trends, facts, information, knowledge, and the like now makes this system a forward-looking one to answer questions about the future. From this viewpoint, effective strategic planning not only makes great use of multidimensional analysis but also makes use of whatever analyses are helpful to answer tomorrow’s questions in view of today’s projections. To reap the real benefits of a BIS operating mode, an effective strategic planning approach needs to translate visioning down to a practical level. That is, executive visioning can be used to help managers build a consensus around its strategies and programs so that it guides action at the lower or local levels. This necessitates letting managers communicate these strategies and programs up and down the organization and, at the same time, linking them to each business unit and individual objectives and goals. This planning model enables the company to integrate its business and financial plans at the lower levels. Also, this model allows for feedback, since it gives the company the capability to study alternatives for strategic improvement. Strategic improvement consists of continuous feedback—that is, testing the environment on which the strategies are built and making adjustments where deemed necessary. Since short- to long-range strategic planning in a typical company makes use of this broad-based approach, this will be the essential focus of the functions that are covered in the next sections of this chapter. Typically, a company’s corporate planning staff, which undertakes strategic planning activities and is responsible directly to the president or chief executive officer (CEO), has relatively few members, exists at the corporate level, and operates on a full-time basis. This corporate planning staff operates in an environment characterized by product and technological change. Also, the character of a company’s departmentalization seems to influence the organization structure for strategic planning. An important part of the corporate planning staff’s time is spent on determining and analyzing a company’s critical success factors and related key performance indicators, including financial ratios. For example, this staff might determine CSFs to be: (1) prices (responsiveness to competitive pricing), (2) cost control (reducing the cost of plant and office operations), (3) inventory turnover (improving the times the inventory turns over yearly), and (4) product mix (having the right products for the times). These CSFs are in line with those set forth for other industries and can be related to key performance indicators and financial ratios. Understanding a company CSFs, using strategic intelligence that is obtained from its current operations via its KPIs and financial ratios, may indicate that there are small to large changes occurring that must be addressed by top management. LONG-RANGE STRATEGIC PLANNING Corporate planners within a BIS environment view decision making as a continuous decision process, since it centers on executive visioning that provides the overall direction for setting appropriate corporate objectives and measurable

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goals, employing strategies and programs to achieve these desired objectives and goals, allocating a company’s resources in an optimum manner, and specifying the critical success factors, which are tracked by key performance indicators and financial ratios (shown previously in Figure 7.2). The implemented plans are then monitored to keep them on course and to correct any deviations from the original or revised plans. Revisions are usually necessary, since change is integral to the nature of strategic planning and its decision process. Since there is a need to evaluate planning alternatives continuously for achieving stated objectives and measurable goals, the capability to study various aspects of the long-range plan in its various stages becomes critical. In practical terms, this means producing many runs or variations of the same plan, using different sets of assumptions and perhaps different logic. In evaluating resources, for example, various resources can be applied, and numerous questions concerning their nature and size can be posed. Do we have the necessary resources to achieve our objectives, or must they be acquired? If resources are to be acquired, at what cost and in what time span? Will they be operational in time to achieve the corporation’s objectives within a given schedule? What are the optimum mixes of resources to satisfy different objectives under different sets of conditions? What are the likely effects of miscalculating availability and cost of equipment, personnel, or funds to acquire the needed resources? What can happen for every 1 percent of error in the forecast and, therefore, how critical is it that we hold strictly to the plan? Similarly, in evaluating strategies, for example, various questions can be asked concerning improved revenues, lower costs, higher reliability, meeting customer requirements, relationship to other products, enhanced image in customer eyes, better information, and relationship to organizational plans and derivative programs. The anticipation of consequences of given assumptions and the evaluation of alternatives are major activities of a company’s managers at the highest level and the corporate planning staff. The utilization of information and knowledge, along with its resulting intelligence, is helpful because it provides the means whereby the final strategic plan mirrors the real world as closely as possible. In turn, strategic intelligence can help corporate planners evaluate alternatives and (usually) choose the best one. Such evaluation has come to be known as the “what if” approach—“what” is likely to happen “if” the company takes or does not take a certain course of action. As will be seen in the materials to follow, a BIS operating mode can prove to be extremely useful for a company’s corporate planners. Integrate Changing Times with Long-Range Corporate Strategies and Resources An integral part of the entire strategic planning process is the integration of changing times with long-range strategies and resources. A Labor Department study, for example, forecasts a slowdown in future economic growth. It estimates

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that the country’s gross domestic product, adjusted for price change, will grow at annual rates of just over 3 percent. That would be about 25 percent below the average growth rate of about 4 percent that has prevailed during much of the time since World War II. In addition to the external long-range factors, a typical company’s executives and their corporate planning staff should consider the internal factors that affect the long run. Essentially, they include the factors mentioned above (marketing, manufacturing, financial, and human resources). The interplay of these internal environmental factors with the external environmental factors can have a dramatic impact on the company’s strategic plans. In fact, incorrect information concerning the internal and external environmental factors may lead a typical company down the wrong planning path. Three different situations are normally used to adapt to changing times, as reflected in a company’s long-range strategies. First, a company can invest available resources in order for the business to grow and thereby strengthen its profitable position. Second, it can protect business strengths in order to maintain a strong position in a moderately attractive and mature market. Third, it can divest the business in order to exit from a weak position in a relatively unattractive market. Based on these three typical situations, allocation of a company’s resources differs. Similarly, measurement and analysis using KPIs and financial ratios also differs based upon the situation. In all three situations, the employment of new business intelligence can help management gain a more complete understanding of a grow, protect, or divest long-range strategy. Once a company defines its current state and its long-range strategy, it can and should match its people to that strategy. These three typical strategies require distinct sets of managerial characteristics and management styles. Broadly speaking, an entrepreneur is needed for the grow situation, a competent manager for the protect situation, and a critical manager for the divest situation. Not only do these three types of organizations differ in the optimum management style of their executives but they also differ in the way they are organized and in the climate that exists within each organization as well as the current economic climate. For example, in the grow situation, planning for problem solving and problem finding is longer term in its outlook and there is a great deal of delegation of authority. In the protect organization, on the other hand, there is considerably less freedom to act independently in and planning is much more medium term in nature. The successful manager in either situation is one who thrives on the company’s culture and feels most comfortable within its organizational structure. Related to a company’s long-range strategies and resources are a number of underlying concepts. A popular concept is this: the essence of strategic unity between business and technology is manifested in changing roles and structures, high expectations, and a collaborative spirit. From this view, long-range business strategies are broad visions of the future that are adaptable to a changing environment. They encourage corporate planners to think about the future in creative and productive ways. Such planners see their jobs as getting others to

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question conventional wisdom. To perform their jobs, corporate planners have to resort to provocation or shock tactics, such as raising difficult questions and challenging conventional assumptions. A number of the corporate planning principles set forth previously (Figure 7.1) may be of help in changing the status quo. The essence of these principles may well provide the impetus to improve a company’s long-range strategies and the employment of its resources. Overall, developing long-range business strategies and finding the resources to carry out these strategies is a process of discovering and understanding broad visions of the future, as opposed to the strategic planning process, which is about turning these visions into action for changing times.

Scenario Planning Needed for Effective Use of Corporate Resources To get a handle on changing times over the long run, top management and their corporate planning staff need to engage in scenario planning, where different outcomes can be evaluated. Whether there is a grow, protect, or divest situation, it is helpful to look at the financial results under good, average, and bad economic conditions. Companies who expand their imaginations to see a wide range of possible futures will be in a much better position to take advantage of expected opportunities that will typically come along. Basically, scenario planning simplifies the avalanche of information into a limited number of possible states. Each scenario tells a story of how various elements might interact under certain conditions. When relationships between elements can be formalized, a company can develop quantitative models. It can then evaluate each scenario for internal consistency and plausibility. Although a scenario’s boundaries might at times be somewhat unclear, a detailed and realistic narrative can direct attention to aspects that would otherwise be overlooked. Scenario planning differs from other planning methods such as contingency planning, sensitivity analysis, and computer simulations. Contingency planning examines only one uncertainty. It presents a base case and an exception or contingency. Scenario planning explores the joint impact of various uncertainties, which stand side by side as equals. Sensitivity analysis examines the effect of a change in one variable, keeping all other variables constant. Moving one variable at a time makes sense for small changes. If, however, the change is much larger, other variables (such as money supply, interest rates, and so forth) will not stay constant. Scenario planning, on the other hand, allows for changing several variables at a time without keeping others constant. It captures the new states that will develop after major shocks or deviations in key variables. Scenario planning is more than just the output of a computer simulation model. Instead, it attempts to interpret such output by identifying patterns and clusters among the millions of possible outcomes that a computer simulation might generate. It often includes elements that were not, or cannot be, formally

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modeled, such as new regulations, value shifts, or innovations. Hence, scenario planning goes beyond objective analyses to include subjective interpretations. In addition, scenario planning attempts to compensate for two common errors in decision making—underprediction and overprediction of change. Most people and companies are guilty of the first error. For example, think in terms of a hundred years ago and how hard it was to imagine the factors that propelled society into today’s new technological world, where automobiles, airplanes, televisions, computers, and so on are commonplace. Yet a small group of futurists overpredicted, expecting levels of change that failed to materialize, notably in medicine, artificial intelligence, and space travel. Scenario planning allows companies to chart a middle ground between underprediction and overprediction. It helps expand the range of possibilities that can be seen. It does this by dividing knowledge into two areas: (1) elements that are knowable and (2) elements that are uncertain or unknowable. The first component casts the past forward, recognizing that the world possesses considerable momentum and continuity. Assumptions about demographic shifts and substitution effects of new technologies can safely be made. Obvious examples of uncertain aspects are future interest rates, oil prices, results of political elections, rates of innovation, and so forth. It is not important to account for all the possible outcomes of each uncertainty; simplifying the possible outcomes is sufficient for scenario planning. Because scenario planning depicts possible future events but not specific strategies to deal with them, it is necessary for top management and corporate planners to tie in company strategies with these scenarios.17 Using Strategic Intelligence to Better Understand Long-Range Corporate Planning Typically, a company’s strategies that tie in with various scenarios need to be translated into actions that take the form of long-range (i.e., five-year) strategic plans. To assist in the development of these long-range plans, corporate planners need to begin with a knowledge and understanding of existing products, divisions, margins, profits, return on investment, cash flow, availability of capital, research and development capabilities, skills and capacities of personnel, and soon. For a manufacturing-oriented company, this intelligence is typically extended to an in-depth analysis of manufacturing operations that are linked to centralized operations at the corporate level. There is the need to examine the past few years’ performance as well as the current year’s performance as part of an initial overall review process. Evaluating significant aspects of past and present operations is the basis for determining how well the organization objectives and goals are being met. In like manner, plans for the coming five years, viewed through different scenarios based on short- to medium-range plans for improving operations, become an essential part of getting started on long-range corporate planning.

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A typical five-year plan for a manufacturing-oriented company includes the external environmental factors that are generally not controllable, such as customers, government, public, competitors, suppliers, investors, and financial institutions. On the other hand, four internal environmental factors that are controllable by the company center on the following: • Marketing planning focuses on expanding the present product lines and entering new product markets. It also focuses on increasing use of selling outlets and/or distribution to sell the company’s products, changes in pricing policy and pricing practices to effect higher sales, and consideration of new advertising media for more effective penetration of the company’s markets. • Manufacturing planning centers on major facilities contemplated and improvements in processing efficiency, including the percent of capacity that is now and will be employed with present facilities and machinery as well as the steps that are being undertaken to use any excess capacity. • Financial planning relates to projected sales by product lines, contribution (sales less direct manufacturing costs) by product lines, indirect manufacturing costs plus sales and general and administrative expenses, net profits before federal income taxes by product lines, fixed and working capital needs, return on investment by product lines, and comparable financial ratios and analyses. • Human resources planning, which is related to marketing, manufacturing, and financial plans, centers on projected requirements for key management personnel and production labor when considering turnover and future growth.

To develop projected strategic plans under different scenarios for a five-year period, the corporate planning staff employs the company’s corporate databases and data warehouses to analyze meaningful long-range data, information, and knowledge in order to discover pertinent intelligence about a company’s operations over time. In turn, this output is used to finalize its five-year strategies and programs under the most likely scenario. To better understand strategic intelligence as a way of getting a handle on long-range corporate planning, reference can be made to Figures 7.3 and 7.4, which display three likely scenarios for a typical company (i.e., good, average, and poor economic conditions). These represent the consensus of top management working with the corporate planning staff. The dollar amounts for total sales, cost of goods sold, gross margin, selling and general expenses, and profits under good, average, and poor economic conditions five years hence are shown in Figure 7.3(a). In Figure 7.3(b), the graph of this data is shown, followed by the pie charts for good and poor economic conditions in Figure 7.4. Although not shown, a number of other values, graphs, and pie charts could have been illustrated for the company’s geographical regions—North, East, South, and West. Additionally, the geographical regions could have been ranked five years hence. The attendant circumstances will normally dictate what further analyses are necessary

Figure 7.3 (a) Total Sales, Cost of Goods Sold, Gross Margin, Selling and General Expenses, and Profits under Good, Average, and Poor Economic Conditions Five Years Hence and (b) Graph That Compares the Above Amounts Five Years Hence

Figure 7.4 A Pie Chart Comparison of Cost of Goods Sold, Selling and General Expenses, and Profits under Good and Poor Economic Conditions Five Years Hence

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for a thorough understanding of a company’s operations five years into the future. A thorough understanding of Figures 7.3 and 7.4 and related illustrations provides an effective overall long-range strategic measurement framework for a typical company. Ever since Peter Drucker wrote that what is measured gets done, decision makers have considered performance measurement an essential part of their jobs. The potential effectiveness of a measurement framework for strategic intelligence needs to consider the following criteria in the form of these questions: • Is it, or can it be, connected to a company’s strategy? • Is a balance of financial and non-financial measures included? • Are key measures tied in with a model that expresses the causal relationship between them so the focus is on key performance factors and financial ratios? • Can it be linked to important management processes (i.e., planning, management reviews, budgeting, etc.)?

Typically, underperforming companies are more often the result of hundreds or even thousands of decisions made by individuals throughout the company than any grand strategic mistakes. Decision results at all levels impact the strategic capacity of the company. Too frequently, people must rely on a variety of generic inputs that are not targeted to their specific needs. An effective strategic intelligence capability must offer decision makers the collective history, facts, insights, and applicable analyses of the organization. A most difficult part of strategic intelligence is continually recording the analyses, the decisions made, and the results of those decisions so that future decision makers can learn from the past. This feedback loop is essential not only to populate the intellectual assets of the organization, but also to enable the business intelligence system to monitor and make adjustments to key underlying business assumptions and rules. Decision makers need to be informed when the company is where it should be and when it is off track. Hence, when companies succeed, they need to know why so that there can be a positive approach to a company’s decisionmaking behavior. SHORT-RANGE AND MEDIUM-RANGE STRATEGIC PLANNING Short-range strategic planning, sometimes called operational planning, is a derivative of medium-range strategic planning which, in turn, comes from longrange strategic planning. For a typical company, short-range strategic planning is concerned with the efficient use of available capacities. It is a detailed financial plan that specifies both the company’s objectives and goals for the coming year (and how it will be attained) and the operational procedures for managing

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daily operations. As such, the short-range plans outline the specific steps to accomplishing the medium-range plans. Also, they play a major role in implementing business strategies by translating long-range plans into action. Additionally, short-range plans center on detailed objectives and specific, measurable goals and strategies of the company and the means for achieving them, usually in the form of flexible budgets or profit plans by product groups. Management needs cost and revenue margin information so that it can identify areas of strength and weakness. Knowledge about a company’s product margin or contribution and knowledge about its competitors over time is also needed to measure the profit impact of alternative courses of action. Approved shortrange strategic plans become budgets so that actual results can be measured and compared monthly for more effective control. Overall, these plans of a short duration represent top-down planning and budgeting that links performance to strategic vision. Essentially, these short-range strategic plans represent continuous plans that are owned by department heads, who will be held accountable for results. Use of Business Intelligence to Develop and Evaluate ShortRange and Medium-Range Profit Plans For a typical company’s products, an annual profit plan is an integral part of corporation-wide strategic planning. In a similar manner, overall profit plans are determined for two, three, and four years hence. Typical output in terms of short-range reports (current year) include: periodic (or monthly) balance sheet and income statement, monthly flexible budgets, monthly budget exception reports, and periodic (or monthly) KPIs and financial ratios. Medium-range reports (two to four years in the future) include: projected balance sheet and income statement, projected cash flow, KPIs and financial ratios, projected source and application of funds, and projected products, manufacturing facilities, and personnel requirements. As information becomes available that reflects changing times, profit plans for the coming year must be revised to reflect the changes and expected changes in the business environment. Effective profit planning, therefore, must be a continuous effort rather than a periodic one within a BIS operating mode. Although budgets for profit planning are generally prepared by the accounting department, the responsibility rests with the corporate planning staff, which must not only select the appropriate financial information for specific planning decisions but also combine this information from corporate databases and data warehouses in useful and meaningful ways. Often it is necessary to employ knowledge discovery techniques to extract financial trends and patterns. Staff members must also review and coordinate the estimates provided by the functional managers involved in a particular decision. They must provide a measure of the profit impact of alternative courses of action and advice on the meaning and significance of financial analysis. In summary, long-range strategic plans within a BIS

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operating mode are translated into medium-range plans for the next several years and finally into short-range strategic plans—that is, annual profit plans (including budgets) for the coming year. Flexible budgets or detailed profit plans for the coming year must be developed to take into account planning for marketing, manufacturing, and finance as well as human resources that have impact on the current year. Similarly, overall profit plans for medium-range strategic plans can be developed. To assist the corporate planning staff, a series of “what if” questions about planning need to be asked and answered. Sensitivity analysis can be used to determine the impact the change of one or more variables might have on the final profit plans. Managers and their staffs can interact with the company’s information and knowledge to answer “what if” questions and undertake sensitivity analysis. For example, a six-month analysis of six new products can be undertaken that starts with their profits, which are shown in Figure 7.5(a). These data are graphed in Figure 7.5(b). In turn, the profitability of these six new products over the forthcoming six months can be ranked as follows:

Finally, pie charts for a company’s best and worst profits in terms of the six new products are found in Figure 7.6. Basically, profits increase over the months due to a number of factors, including larger sales volumes due to the acceptance of the product, a reduction in production times, and a reduction in scrappage as the learning curve improves. In addition, the percentages of profitability could have been set forth for the six products, along with a graph and an appropriate ranking of the products, for a different view. Going beyond the preparation of short-range and medium-range profit plans, it is helpful to measure actual performance against these profit plans. In terms of the short range of the current time period, the scorecard software mentioned previously in the chapter can help management at this point. Besides getting an overview of how a company is doing overall, specific areas of a company can be measured and evaluated for its performance. For example, the scorecard software can check on defect rates plant by plant and see how each plant’s quality is improving. It can link measurements such as on-time deliveries to certain financial indicators. The software can measure the percentage of sales due to new product introductions as well as measure and gross margins on new products, along with corporate-wide indicators such as revenues and return on in-

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Figure 7.5 (a) Profits of Six New Products in the Forthcoming Six Months and (b) Graph That Compares a Company’s Six New Products in the Forthcoming Six Months

vestment. It should be noted that different companies at different times have different needs and aims. For example, a service company that has just merged would not build a strictly financial model that focuses, say, on productivity. Output per employee is not a driver for that business. The real value of scorecard software is that it forces a company to reexamine assumptions about what really drives performance. It forces a company to focus on and become much more explicit about what matters to its customers and, ultimately, what matters to a company’s total operations.

Figure 7.6 Pie Charts for a Company’s Best and Worst Profits in Terms of Six New Products over a Six-Month Period

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Undertaking Financial Forecasts for Planning an Expansion To assist in short-range strategic planning, business intelligence can be helpful in the review of quarterly financial forecasts to plan an expansion. For example, top-level managers can apply for a loan to finance an expansion, although they lack the historical-financial data necessary for a statistical analysis of earnings, The solution is to construct a four-quarter earnings forecast, combining present market conditions with their best knowledge about the future. The gathering process is started by polling the managers for number estimates and market assumptions. For this type modeling process, there is almost complete consensus among all managers on all assumptions other than unit sales and selling price. The importance of unit sales and selling-price growth, coupled with the range of estimates received, makes the use of multiple projections prudent. Consequently, the model provides the best-case and worst-case scenario forecasts for review by top-level managers and their staffs. Basically, the model incorporates assumptions for several variables, including growth factors for unit sales and selling prices for the next four quarters. Also, current sales and the actual cost of goods sold, sales commissions, administrative expenses, and federal income taxes are needed for the current quarters. Using the estimates based on assumptions, projected quarterly income over four periods can be displayed for high-growth, medium-growth, and low-growth projections in unit sales, selling prices and income. These can be used in the model for the best-case, average-case, and worst-case forecasts. Additionally, sensitivity analysis allows the company’s managers to examine the impact of a change in one variable. A more rigorous multidimensional analysis can be performed by varying a number of variables to see what impact each change has on net profit on sales. In this way, managers can evaluate how sensitive the model is to changes in given variables. Another forecasting approach is to utilize one of the popular software packages available today. For example, Forecast Pro for Windows allows users to have the package—via its expert system—determine the best forecasting model based on the software’s observation of the data. As such, the software package not only recommends a forecasting model but also explains why a particular model has been selected. This feature provides an accurate model as well as teaches the decision maker what to look for when analyzing a data series. Comparison of Company against Competitors and NonCompetitors Going beyond assistance in the preparation and evaluation of final flexible budgets and review of quarterly financial forecasts, a BIS approach is also helpful in measuring a company’s operations against competitors as well as noncompetitors. To enable decision makers to accomplish such analysis, there may be a need for business intelligence from external databases. That is, specific

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data, information, and knowledge for such analysis must be stored on line for later use. When decision makers develop plans that center on improving productivity for manufacturing operations, adequate business intelligence is available to set the wheels in motion for developing short- to long-range strategic plans for beating or, at least, meeting competition. Similarly, external financial business intelligence on competition can be stored on line, allowing decision makers to measure their own corporation’s performance against competition. In this manner, they can see where the corporation is experiencing good, average, or poor performance in its major areas of operation against the best of its competitors. It should be noted that benchmarking against the competition poses problems. For one thing, comparisons with competitors may uncover practices that are unworthy of emulation. For another, while competitive benchmarking may help a corporation meet its competitors’ performance, it is unlikely to reveal practices for beating them. Moreover, getting information about competitors is obviously difficult. Finally, it has been observed that people are more receptive to new ideas that come from outside their own industry. A non-competitive investigation can give executives information about the best functional practices in any industry. These may include technological advances unrecognized in their own industry (like bar coding, which originated in the grocery industry but has since been widely applied). Adoption of these practices can help a corporation achieve a competitive advantage. To place the preceding discussion in its proper perspective, one of Xerox Corporation’s most valuable and memorable benchmarking experiences was with L. L. Bean, the outdoor sporting goods retailer and mail-order house. It was carried out by the Xerox Logistics and Distribution (L&D), which is responsible for inventory management, warehousing, and transport of machines, parts, and supplies. Historically, Xerox’s L&D’s productivity increases had been 3 to 5 percent per year. It had become clear that improvement was necessary to maintain profit margins in the face of industry price cuts. The inventory control area had recently installed a new planning system, and the transportation function was capitalizing on opportunities presented by deregulation. Warehousing was next in line for improvement, and the distribution center managers wanted a change. They identified the picking area as the problem in the receiving-through-shipping sequence. A new automated storage and retrieval system (ASRS) for materials handling had appeared on the scene and was the subject of hot debate in Xerox’s distribution function. The corporation had just erected a high rise ASRS warehouse for raw materials and assembly parts in Webster, New York, in the same complex as a large finished goods distribution center. Internal benchmarking evaluations by L&D showed that heavy investment in capital equipment for ASRS could not be cost justified for finished goods. They needed a different way to boost warehousing and materials handling productivity, but what? The L&D unit assigned a staff member half time to come up with a suitable

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non-competitor to benchmark in the warehousing and materials handling areas. The staff member combed trade journals and conferred with professional associations and consultants to find the companies with the best reputations in the distribution business. He then targeted those companies with generic product characteristics and service levels similar to Xerox reprographic parts and supplies. The staff member eventually singled out L. L. Bean as the best candidate for benchmarking in terms of their warehouse operations. The staff member was particularly struck with L. L. Bean’s warehouse system design. Although extremely manual in nature, the design minimized the labor content, among other benefits. The operation also did not lend itself to automation of handling and picking. The design therefore relied on very basic handling techniques, but it was carefully thought out and implemented. In addition, the design was selected with the full participation of the hourly work force. It was the first warehouse operation designed by quality circles. To the layperson, L. L. Bean products may bear no resemblance to Xerox parts and supplies. To the distribution professional, however, the analysis was striking. Both companies had to develop warehousing and distribution systems to handle products diverse in size, shape, and weight. This diversity precluded the use of ASRS. Later, a Xerox team visited Bean’s operations in Freeport, Maine. Besides the person in charge of benchmarking in L&D, the team consisted of a headquarters operations executive and a field distribution center manager. These two people represented the line employees who would ultimately make any changes. The findings resulted in L&D incorporating some of L. L. Bean’s practices in a program to modernize Xerox’s warehouses. These practices included materials location arranged by velocity to speed the flow of materials and minimize picker travel distance as well as enhance computer involvement in the picking operation. Xerox plans to put together a totally computer-managed warehouse. EFFECTIVE STRATEGIC COMPETITIVENESS BIS APPLICATION—LOYALTY CONSULTING To enhance its strategic competitiveness, Loyalty Consulting, a consulting firm in North York, Ontario, Canada, has embraced business intelligence. Using IBM’s Intelligent Miner software with an RS/6000-based DB2 data warehouse, the firm is able to devise highly targeted direct mail and marketing campaigns for its clients. Loyalty Consulting chose these products for competitive advantages they could provide. The predictive modeling performed by Intelligent Miner determines who should receive solicitations for items ranging from financial products to magazine subscriptions. It can also determine whether an individual is a good or a bad credit risk and then market appropriate products to that person. In some cases, it has been able to slash marketing costs for Loyalty’s clients by about 50 percent while boosting the overall ROI by as much as 400 percent.18

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Typically, business intelligence products allow a company to understand and act on things it already knows. For example, a company might realize that 20 percent of its customer base is the most profitable, but it cannot determine which 20 percent. Business intelligence tools can help the company find that segment and devise programs to maximize sales. In fact, the use of complex algorithms lets a company correlate customer transactional behavior, such as how recently a person has purchased a product, how frequently a customer buys, and the size of the transactions, to factors such as shareholder value, growth strategies, and other business concerns. Overall, these tools enable people to discover all sorts of relationships and trends that they generally would not see.

FUTURE CORPORATE PLANNING WILL MOVE TOWARD GREATER USE OF STRATEGIC INTELLIGENCE In the future, the employment of strategic intelligence in the area of corporate planning is expected to increase for a number of reasons. First, companies need to think globally and then act locally in response to the local markets they serve. Second, sharpening a company’s competitive intelligence is necessary for growth and survival in the long run. Third, the impact of E-commerce on a typical company is expected to increase at an unprecedented rate. Fourth, the average company is in the early stages of a total revolution in organizational structures. That is, companies may be run in the future as virtual corporations. Fifth, there may well be a shift away from core competence to a focus on figuring out where the opportunities for a company lie. Sixth, there will be an open atmosphere in which innovation is encouraged among all company employees. Still many other reasons can be given for the shift to strategic intelligence in corporate planning matters. As with present corporate planning, in the future, decision makers at the very top will need to have basic intelligence to assess the overall health of the company. Armed with that intelligence, they will be ready to ask questions about a company’s future direction. Knowing what questions to ask about a company’s future direction is most important when retrieving strategic information and knowledge from the company’s databases and data warehouses. For example, a decision maker can ask questions about the company’s future profitability and receive information about the profitability or sales volume of the company as a whole. If the individual is satisfied, then the decision maker would go on to the next metric of interest. If something seemed out of line, for example, if sales volume significantly exceeded expectations, then the decision maker could look further into the sales volume measures, possibly requesting a breakdown by product line or division. In turn, a number of graphs, pie charts, and rankings of products, along with their sales amounts and percentages, can be presented to the decision maker for a better understanding of a company’s future performance (i.e., future strategic intelligence).

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SUMMARY This chapter’s central focus was on strategic intelligence at the corporate planning level to assist corporate managers and their corporate planning staffs in the proper acquisition, use, and disposition of an organization’s resources. Because corporate strategic planning is an important focal point of a company’s total operations, its purpose is to decide what to do in terms of strategic plans based on a company’s mission and its objectives and goals, how to implement specific strategies and programs on time, and when to perform them to meet short-, medium-, and long-range strategic plans. From this total understanding of a company’s operations, important relationships and feedback that exist among these basic elements were explored throughout the chapter. As a means of giving direction to a typical company, short- to long-range plans, strategic intelligence in a changing world was discussed in the first part of the chapter. Next, significant corporate planning factors that are related to effective strategic intelligence were explored. A BIS approach was presented in the long run and the short run for a typical company to demonstrate the usefulness of strategic intelligence to support managerial decisions. Also, a typical example of strategic competitiveness was set forth. NOTES 1. Robert J. Thierauf, Knowledge Management Systems for Business (Westport, CT: Quorum Books, 1999). 2. Nicholas Imparato, “The Big Waste,” Intelligent Enterprise, January 26, 1999, p. 14. 3. Dan Bolita, “BI and KM Marriage Now Official,” KM World, March 1999, pp. 6, 47. 4. Ram Charan, “Managing Through the Chaos,” Fortune, November 23, 1998, pp. 283–290. 5. Clayton M. Christensen and Michael Overdorf, “Meeting the Challenge of Disruptive Change,” Harvard Business Review, March–April 2000, pp. 67–76. 6. Stan Davis and Jim Botkin, “The Coming of Knowledge-Based Business,” Harvard Business Review, September–October 1994, pp. 165–170. 7. Robert J. Thierauf, A Problem-Finding Approach to Effective Corporate Planning (Westport, CT: Quorum Books, 1987); User-Oriented Decision Support Systems: A Problem-Finding Approach (Englewood Cliffs, NJ: Prentice-Hall, 1988); Group Decision Support Systems for Effective Decision Making (Westport, CT: Quroum Books, 1989); Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1993); and On-Line Analytical Processing Systems for Business (Westport, CT: Quorum Books, 1997). 8. Sandy Reed, “Aligning Business and Technology Goals on the Web: Easier Said Than Done,” InfoWorld, April 12, 1999, p. 85. 9. Dylan Tweney, “Better Claim Your Space: The Internet Land Grab Will Produce Many Minimonopolies,” InfoWorld, June 7, 1999, p. 54.

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10. Tim Wilson, “Auto Hub Merger: A Model for Others,” Internet Week, March 6, 2000, pp. 1, 48. 11. Gary Hamel and C. K. Prahalad, “The Core Competence of the Corporation,” Harvard Business Review, May–June 1990, p. 79. 12. Gary Hamel and C. K. Prahalad, “Corporate Imagination and Expeditionary Marketing,” Harvard Business Review, July–August 1991, pp. 81–82. 13. Ibid., p. 83. 14. John F. Rockhart, “Chief Executives Define Their Own Data Needs,” Harvard Business Review, March–April 1979, pp. 86–87. 15. Tom Davenport, “Think Tank: Finding the Information That Matters,” CIO, June 1, 1996, pp. 24–26. 16. Joel Kurtzman, “Is Your Company Off Course? Now You Can Find Out Why,” Fortune, February 17, 1997, pp. 128–130. 17. Paul J. H. Schoemaker, “Scenario Planning: A Tool for Strategic Thinking,” Sloan Management Review, Winter 1995, pp. 41–56. 18. Samuel Greengard, “How to Profit from Business Intelligence,” Beyond Computing, January–February 1999, p. 29.

8 Tactical Intelligence in Marketing GAIN A COMPETITIVE ADVANTAGE BY UTILIZING A BIS OPERATING MODE To better understand a business intelligence system operating mode in marketing, it is first necessary to focus on a company’s customers and the service they receive. Information that is currently collected about customers and about products and services that can be provided to meet their current needs is related directly or indirectly to those marketing endeavors that focus on meeting customers’ needs over time. From this perspective, effective business intelligence systems in marketing pay off in improved service to customers, better products and services, quicker problem solving for developing new products and services, reduced market research and development costs, and enhanced customer relations, to name a few. As such, a forward-looking company has the ability to gather, analyze, and distribute marketing intelligence that keeps it ahead of the competition. Today, underlying many of these important attributes for a forward-looking company is the Internet and the World Wide Web, which continue to transform global markets. Typically, these changes are affecting the very core of many markets in terms of meeting customer needs. The global electronic marketplace on the Internet and the World Wide Web enable customers and businesses to buy and sell products or services from new sources, wherever and whenever they are ready to do so. This new environment has profound implications for the way companies market their products and services. The survival of many companies in this integrated and international marketplace will depend upon how well they develop comprehensive and integrated advertising, sales, promotion, and distribution strategies. In the market environment created by the

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rapid expansion of the Internet, customers are better educated, more demanding, and have greater control of the purchasing process from inititation to completion. Based on this newer business intelligence orientation in marketing, the whole marketing function needs to be rethought from a short-range to a long-range viewpoint. Initially in the chapter this discussion focuses on the customer. This background serves as a means for exploring the marketing factors that are related to tactical intelligence. Next, typical marketing areas that lend themselves to business intelligence systems are explored. Areas examined in more detail relate to marketing strategy and product pricing. Finally, the relationship of marketing intelligence to competitive wisdom is highlighted. Tie-in of Tactical Intelligence in Marketing with Strategic Intelligence At the tactical intelligence level (as discussed previously in the text), there tends to be a mix of external sources with internal sources. Whereas the sources for the operational level are based on internal sources for an organization, tactical intelligence tends to be a blend of the two. Lower- and middle-level managers and their staffs use tactical intelligence to help them oversee their functional areas and give direction to their operations in the near future. Tactical intelligence can center on such items as which products to offer a company’s customers or what inventory levels to maintain. The time frame is generally confined to the coming year and slightly beyond. This is in contrast to strategic intelligence, where the accent is on utilizing external sources over a longer time frame—say, up to five years and beyond. However, the output of strategic intelligence is used as input for tactical intelligence, whether it is in marketing or otherwise. In another view of tactical intelligence, the focus is on external and internal sources. Marketing managers, for example, would be concerned about the overall sales performance of their regions versus competing firms. They would therefore need internal information and knowledge on quarterly and yearly sales as well as information and knowledge relating to external competitors. In a similar manner, other functional level managers would be concerned about the current and future performance of their organizational units. They would need external information and knowledge on important matters that affect their units, such as problems with suppliers, sales declines, or increased demand for one or more products. In addition, these managers would need internal knowledge— that is, plant costs and the periodic performance of their units as well as anticipated performance to take advantage of specific opportunities. In the development of tactical intelligence, the Gartner Group has applied a name to the reduction of the “short-term” aspect of tactical intelligence to its realistic limit—zero latency. As noted previously in the text, zero latency refers to an infrastructure that reduces information and knowledge float, which is the time it takes to receive information and knowledge used for making decisions,

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from weeks or days to minutes or seconds. Whether it is the new concept of zero latency or the earlier concept of just-in-time, the point is to provide timely tactical intelligence. An operational data store, typically updated daily from its operational (legacy) sources, but moving to hourly or even real time via a messaging middleware solution, can be a cornerstone of a zero-latency strategy for tactical intelligence.1 Relationship of OLAP Systems to Business Intelligence Systems in Marketing As with any functional area of a company, business intelligence in marketing can be an important source of power for marketing managers. Typically, these managers need to have the right tactical intelligence available. On-line analytical processing (OLAP) systems are the starting point for business intelligence systems in marketing. OLAP technology gives end users easy access to large volumes of numerical data on line. Usually, data is transferred from on-line transactional processing systems to a multidimensional database for summarization. OLAP functions between the back-end databases and data warehouses and the frontend or end user. The multidimensional engine that drives OLAP technology enables the system to analyze large volumes of data quickly. Retrieval times are aided by the use of efficient data storage algorithms that preaggregate data, saving it in fewer places but with more intelligent directional markers. A most important feature of OLAP is its drill-through capability from the highest level to the lowest level. As such, OLAP is a user-friendly, quick, and flexible client/server solution that lets users drill down into information housed in databases and data warehouses for analysis. To get at the detail level, seamless integration of these data is necessary, whether they are transaction processing databases for everyday data or data warehouses for historical data. Thus, there is a need for an open architecture that allows the user to obtain the desired data for analysis from any database or data warehouse in an organization. Based on the preceding essential features found in OLAP systems, on-line analytical processing systems can be defined as an open systems architecture that provides the user with rapid retrieval of data from company databases and/ or data warehouses for the purpose of summarization and multidimensional analysis. Typically, the end result of the analysis is information and knowledge per user-defined dimensions for getting at the whys of the problem(s) under study. Where deemed necessary, a drill down and/or dice capability is utilized to get at the appropriate level of detail in the analysis.2 As will be seen in this chapter, business intelligence systems in marketing make great use of data summarization and multidimensional analysis. That is, marketing data can be summarized along different dimensions. In turn, the drill down and/or dice capability is utilized to get at the specific details necessary for a better understanding of what is good, bad, or indifferent in terms of marketing operations. However, the business intelligence system aspects include the

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capability for marketing managers, for example, to evaluate company sales in order to identify market changes or to analyze customer data in order to select a specific marketing strategy. In effect, business intelligence systems provide for a more thorough understanding of marketing operations than is normally possible with OLAP systems. They get at not only the whys of the problem(s) under study, but also the wherefores and the various details of what happened. RETHINKING THE MARKETING PROCESS WHERE THE CUSTOMER IS THE FOCUS At the outset, it would be advisable to take a look at the traditional approach to marketing that centers on product, price, place, and promotion in light of the influence of changing times on these four items. For a company to be successful in the past, it was necessary for it to offer the right products at the right price in the right place, with the right promotion. Needless to say, a successful company must change with the times, since there are fundamental changes in demographics and consumer preferences ahead in this 21st century that will force companies to rethink their marketing mix. The newer approach that includes accent on the Internet and the World Wide Web must shape the marketing process to a company’s customers and the customers to the process. In the retailing field, for example, marketing executives must be responsive to altering product offerings at the appropriate time. Traditionally, retailers selected their merchandise assortments for breadth and depth. To meet everchanging consumer preferences, however, retailers need to change their thinking. For example, retailers such as the Limited Stores and The Gap have stuck with their original formulas of offering a small focused assortment in great depth. A stroll through any of the regional malls reveals many similar specialty merchants—stores that sell exclusively sunglasses, nuts, bathing suits, and so forth. These specialty stores represent the traditional departments in a department store. However, these changes for the times may be inappropriate in the near future when the Internet and the World Wide Web will likely have a dramatic impact on the widening of product offerings for a typical customer. Regarding pricing strategy and promotion, retailers, for example, had a method to carve out a niche for themselves. The meteoric rise of discounters in the past reached its peak primarily because of heavy competition in that market. Currently, growth appears greatest among those firms that dominate their categories. The reason is that they have become market leaders within a chosen segment. The ability of these super discounters to offer both broader and deeper assortments within a limited category ensures them steady traffic without extensive promotional price-cutting. Many pricing trends focus on everyday low prices and high/low promotional pricing. Although promotions encourage comparison shopping to obtain the best price, many people with demands on their time cannot do that and instead will trade potential savings for time. This partially explains the success of Wal-Mart. From a management perspective, every-

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day low pricing means labor savings because of reduced marking and remarking of price tags and fewer stock shortages on the promotional items. However, from a typical customer’s perspective, this may no longer be adequate, since the individual is capable of surfing the Internet for a further comparison of prices. The changing demographics are best illustrated by the emergence of new channels of distribution and the resurgence of some old channels of distribution. The demand for convenience, not just price, has allowed non-store retailers to flourish. Currently, some of the important trends are: hypermarkets, mail-order catalog/direct mail, and manufacturer’s outlets. Since the technology is changing so rapidly, it is impossible to know exactly what the future will bring. However, it is recognized that electronic shopping will have a great impact on the channels of distribution. Gateway Software is a case in point concerning shaping the marketing process to customers and not the customer to the process. Gateway’s Web site (www.gateway.com) offers a good example of a business that is not quite getting this point. To buy a PC from their Web site, customers must first decide whether they are looking for a business or a home PC. What happens when one does not know which is needed? That situation might at first seem naive, but it is not. Consider, for example, small-office/home users or people working from home for large organizations. They might need the network and backup capabilities of a business PC but also want the multimedia features of a home PC. The point is not that Gateway should not offer a choice between these major system types; it should because the options are useful for many visitors. Rather, the point is that Gateway should also offer an equally accessible option to develop one’s own configuration. From another perspective, if customers have to construct a process that forces them to think Gateway’s way, a little explanation can go a long way in making customers feel better about doing business with Gateway.3 Using the Internet and the World Wide Web to Expand Marketing Opportunities The Internet and the World Wide Web (as noted throughout the text) have opened up new opportunities for a typical company to market goods and services. In turn, appropriate marketing intelligence can be gathered about a company’s customers. However, determining how to take advantage of the opportunities this new channel is creating is another matter for a typical company. Essentially, the Internet and the World Wide Web pose a challenge for established businesses. The opportunities presented include providing for direct, ubiquitous links to anyone anywhere, thereby allowing the Internet to be used to build interactive relationships with customers and suppliers as well as deliver new products and services at very low cost. The bottom line in terms of the Internet and the World Wide Web is that customers can not only complete

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business transactions but also find value in doing so for themselves and their organizations. To determine what opportunities and threats the Internet and the World Wide Web pose, top managers and their staffs should focus systematically on what this technology can allow their particular organization to do. Essentially, the Internet and the World Wide Web present several opportunities. The first of these is the establishment of a direct link to customers or to others with whom companies have important relationships (such as critical suppliers or distributors) in order to complete transactions or trade information and knowledge more easily and at lower cost. Next, the technology lets companies bypass others in the supply chain. For example, a publisher could bypass retailers or distributors and sell directly to readers. Also, companies can use the technology to develop and deliver new products and services to new customers. Lastly, a company could conceivably use the technology to become the dominant player in the electronic channel of a specific industry or segment, controlling access to customers and setting new business rules. Overall, the utilization of the Internet and the World Wide Web has become a requirement for companies today. To do otherwise is sheer folly and a recipe for disaster. To take real advantage of marketing opportunities on the Internet and the World Wide Web, it is necessary for top management to take charge of managing customer relationships. More specifically, this means doing business in different ways. It is necessary to compete by offering the customer convenience, by holding the customer’s attention, and by offering good prices and excellent service. The best way to serve the customer is to change with the times. The company needs to keep track of the stage of its relationship with each customer and optimize its interactions for each stage of the customer life cycle. Managing customer relationships based on the customer life cycle is also a good way to measure success. Instead of measuring success by the profitability of a company’s product lines, success in terms of the lifetime profitability of each customer can be measured. Needless to say, the market opportunities offered by the Internet and the World Wide Web are challenging the traditional brick-and-mortar store. The Net technology has made retailing more complicated and more competitive. Consumers can sidestep their corner stores and patronize shops across the country or around the world. Eventually, they might forsake retailers altogether, shopping directly from manufacturers. However, corner stores can use this new technology to magnify the benefits of their location. They can use their Web sites to show, for example, the retail topography of their local town and to highlight their particular products. Also, new in-store technologies can yield customerincreased efficiency and more knowledgeable customer service. The real essence of new technology versus traditional stores is the creation of real value for customers and, at the same time, the strengthening of customer relationships.

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Customer Relationship Management Where the Customer Is Primary The traditional approach to relationship marketing was dependent on personal contacts. This is less viable today in a networked economy of electronic commerce. Currently, total customer relationship management (CRM) is much more than just marketing or sales or customer service. It includes the customization of a Web site based on a customer’s preferences, characteristics, or previous behavior as well as versioning a help desk in the customer’s native tongue. It also includes offering or denying house credit at point of service based on previously known payment history. Using analytical measures such as product affinity and propensity scores, channel and media preferences, and attrition risk scores at the point of sale or service, CRM helps define for a company its best customers. Overall, total customer relationship management covers the entire sales and service experience. Typically, customer relationship management combines a set of business disciplines (finding out who a company’s customers are and what they want) with technology (storing that information in a database and using specialized software to sort it out). In turn, that marketing intelligence can be used to find ways to make more money from customers as well as to improve the quality and efficiency of service. A customer relationship department serves as a liaison and advocate group for customers, ensuring that all areas of the organization are prepared for customer interaction and optimization and that customer relationship strategies are successfully implemented across the organization. As CRM vendors expand and improve their products’ Web-based features, companies are seeing a wave of new versions and releases. Yet, despite these improvements, various analysts and some vendors claim that Web-enabled solutions (which are a hybrid of client/server systems and the Web) are not enough. Truly, competitive CRM solutions must be Web-based—that is, these solutions must utilize the Web as their platform. Traditional Web-enabled CRM systems have been oriented around managing activities inside a company, whereas Ecommerce is oriented around customers. The real issue is building a long-term relationship with customers. And paramount to E-commerce is the realization that as far as customers are concerned, moving to a competitor is only a click away. In a Web-based operating mode, all information and knowledge about customers, products, marketing, positioning, collateral, and competitors resides on the Web. Essentially, CRM, where the customer is the focus, centers on the analysis of available business intelligence, demographic data, lifestyle information, and purchase behavior, thereby creating detailed electronic profiles of individual customers so that companies can market more effectively. The net result is enhanced customer satisfaction and improved rates of customer retention.

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Using Customer Satisfaction to Build Customer Loyalty Today, as in the past, a most important goal of any company is not getting customers but keeping them in the short to long run. So this question can be asked of marketing decision makers: Are the company’s customers satisfied? Although the answer is probably yes, that answer is not good enough anymore. Customer satisfaction means that expectations have been met. But even reasonably satisfied customers can be tempted by competitors offering a lower price or better service. By aiming for customer loyalty instead of simple customer satisfaction, marketing managers can reduce the high cost of churn that results from efforts to replace customers who defect. And loyal customers can turn into a company’s best marketing tool—their praise has much more credibility than any advertisement. The traditional measurement of customer satisfaction alone is of doubtful value in producing consistent bottom-line results. The goal should be customer loyalty, driven by effective contact management and problem resolution—and, if applicable, problem finding. Today, companies need to elevate the objectives of customer contact from ensuring basic satisfaction to generating a bond that can survive minor problems, increase the lifetime value of the customer, and lead to favorable word of mouth. According to Hepworth (a Toronto firm specializing in customer retention strategy), companies with strong customer loyalty have programs based on the principles of respect for the customer’s time, effective problem resolution, and a constant emphasis on creating a dialogue rather than just a sale. These goals can be achieved through the effective use of technology to understand customer preferences, respond to customer queries, and share information and knowledge throughout the organization. On the negative side, Hepworth measured revenue at risk, a figure based on the percentage of customers who would be unlikely to repurchase from a vendor because of a problem, ineffective contact handling, or negative word of mouth. For the 25 percent of companies with the highest customer loyalty scores, poor customer handling placed only 3 percent of revenue at risk. For companies in the bottom 25 percent, unhappy customers placed more than 12 percent of revenue at risk. The average for companies in the Hepworth database is 8.5 percent. Hepworth also reports that individuals interacting with business-to-business tell 2.8 people about a negative experience with a vendor. When a consumer company is involved, three people are informed. The essential message is that companies need to lead customers from just customer satisfaction to customer loyalty. Otherwise, their competitors will do so by keeping their former customers happy.4 Underlying Marketing Principles with Emphasis on PIMS Over the years, a number of important marketing principles based on information and knowledge of customers directly or indirectly have been developed.

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Figure 8.1 Marketing Principles Based on Knowledge of Customers

They are set forth in Figure 8.1. A most important source for many of these principles is Profit Impact of Marketing Strategies (PIMS), which is a computerized approach for planning market strategy, run by the Strategic Planning Institute. It is a pool of information on the marketing experiences of its members.5 Several hundred corporations submit data annually on a total of about 3,000 of their business units, each of which is a distinct product-market unit. Each member provides the most intimate details on items such as market share, investment intensity, product quality, and total marketing and R&D expenditures. Using computer simulation, the company can test its own strategies against the real experiences of comparable companies, including competitors. What it receives are answers to questions such as: What is the normal profit rate for a business or a product line given its combination of circumstances, and why? If

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the business continues on its current track, what will its future operating results be? What will happen to short- and long-term performance if certain strategic moves are made? What changes will create the best profits or cash returns? One of the findings that has emerged from the PIMS computer models of the real-life experiences of its corporate members is that there is a set of operating rules that govern all businesses. Some 37 factors—including market share, capital intensity, and vertical integration—jointly explain 80 percent of the success or failure of any business; only 20 percent of a business’s return-on-investment can be attributed to factors that are unique or special, such as the quality of working relations. Other PIMS findings indicate that a larger market share is strongly linked to higher profits and product quality is also very positively related to return on investment. When product quality is low, it does not pay most companies to have high marketing expenditures. High research and development spending hurts profitability when market position is weak but increases it when market share is high. In this case, copying competitors’ products rather than inventing their own is probably the best bet. In addition, higher investment intensity (i.e., ratio of total investment to sales) is associated with lower ROI, and high investment intensity combined with low market share is an ROI disaster. Typically, what a member wants from PIMS is to find out what it will cost to make a particular strategic move and how much better off the business will be afterward. For example, consider return on investment, which PIMS considers one of the best measures of how a business is doing. The PIMS models can forecast how much ROI for a business line will change because of a strategic move involving more marketing, research and development, capital equipment buildup, or whatever—both what the return on investment will be immediately following the move and what it will be several years in the future. Such results have emerged from the PIMS computer models of the real-life experiences of its corporate members. MARKETING FACTORS THAT ARE RELATED TO EFFECTIVE TACTICAL INTELLIGENCE To have global success today and tomorrow, companies need to focus on corporate visioning (refer to the prior chapter) rather than a “me-too” product attitude. Visioning of the marketing function is envisioning markets for products and services that do not exist today. For example, “smart” or knowledge-based products are coming that gather and interpret information over time to enable users to act more effectively in their daily routines. A typical company should generate new products and services so that it is out there ahead all on its own, as Raytheon should have been with the microwave oven and variations of it many years ago. In the 21st century and beyond, marketing battles will be won by companies that can build and dominate fundamentally new markets. Creative new smart products, such as speech-activated appliances, household

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micro robots, and self-parking cars not only make the inconceivable conceivable but also allow a company to influence the direction of a present well-established market. As more and more companies close the gap with their rivals on costs, quality, and delivery, they need to have the capability to stake out new territories with products and services. Hence, there is a need for a new direction that is tied in with the ability to analyze where the company has been over time via a business intelligence system as well as where it should be headed. Some of the important factors related to tactical intelligence are given below. They are: (1) listening to customers and observing their behavior over time, (2) enlarging one’s view of market research and analysis, (3) using database marketing to discover more about a company’s customers, and (4) using marketing software for tactical intelligence. Additionally, it should be noted that tactical intelligence over a company’s marketing operations is directly related to problem finding in that marketing managers can use this approach to solve simple to complex marketing problems and explore new opportunities. Since marketing managers are leaders who communicate with workgroups, motivate employees, and supervise ongoing organizational activities, for them the computer is an extremely important tool that can assist them in decision making. From this view, marketing managers are able to increase their effectiveness by employing business intelligence systems to get a grip on future opportunities that are afforded by newer directions in marketing, such as the smart products mentioned previously in the text. Marketing managers need a knowledge of surrounding environmental factors, a vision of their responsibilities, and a sense of initiative to cope with change. A business intelligence system, then, can assist them in the analyses of problem solving and problem finding as well as resolving problems and identifying future opportunities for developing new products and services.

Listening to Customers and Observing Their Behavior over Time Companies that succeed in educating customers to what is possible develop both marketers with technological imagination and technologists with marketing imagination. For example, in one Japanese company, senior technical officers spend as many as 30 days a year outside Japan talking to customers. The aim is not to solve technical problems or to close sales, but to listen and observe customers and absorb their thinking over time. In another example, a Japanese chief engineer of a major new business development program lived for a time with an American family thought to be representative of the customers his company hoped to win. In each case, the goal was not to improve the flow of information between marketers and engineers nor to manage the balance of power between the two groups, but rather to blur organizational and career boundaries by ensuring that both communities had a large base of shared ex-

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periences. The net result was a good mixture of market and technical imagination which, in turn, underlies tactical intelligence over time.6 An important part of listening to and observing customer behavior is leading a company’s customers where they want to go before they know it themselves. For example, NEC pursues a telephone that interprets between callers speaking in different languages, while Motorola envisions a world where telephone numbers are attached to people rather than places and where a personal communicator allows millions of out-of-touch business travelers to be reached anywhere. In a similar manner, smart or knowledge-based products, such as diapers that change color when wet or a tire that alerts the driver of its low air pressure, diagnose and interpret the immediate environment to enable customers to act more effectively.7 Hence, the successful introduction of new products and services centers on listening to and observing customer behavior to meet their changing needs. Enlarged View of Market Research and Analysis From an enlarged view of market research and analysis, a company needs to satisfy and, at times, exceed the expectations of its customers. But how does a company know what customers want? Unfortunately, because of the way most traditional market research and analysis is conducted, it has fallen short of its important objective. At the core of the problem is the practice of using marketing research to confirm that a decision already made is the right decision rather than using market research and analysis to identify alternative choices and support the process by which the best alternative is chosen. An enlarged view of market research and analysis also includes exploring new opportunities that require indepth insight into the customers’ needs, lifestyles, and aspirations. Companies that are competent in utilizing market research and analysis from this perspective are more successful than those that do not. Recently, market researchers examined previously published academic and industry-sponsored studies encompassing 200 products and 65,000 consumers in an effort to determine how well data collected on consumers’ purchase intentions actually predicted subsequent sales. They found that although consumers gave accurate responses when asked whether they would try a product initially, contrary to expectations, surveyed consumers were not very good at predicting whether they would buy new products or existing nondurable goods (like ice cream or detergent). Given the widespread use of purchase-intention surveys for new products, the market researchers were surprised to find that the approach appears to be better suited to predicting sales of existing products and variations on current lines of durable goods. In fact, the market researchers found that people are not generally reliable predictors of their own long-term purchasing behavior for any type of goods, new, or old, durable or not. Asking consumers if they intend to buy a computer in the next year of two, for instance, will not generate responses that match up

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very accurately with actual sales. Basically, measures of intentions are better predictors of how many people will buy the product in the short term than how much any of them will buy over the long term or how much will be eventually sold. It should be noted that the way marketers frame their questions can increase the odds of getting accurate responses—the more specific, the better. Even more effective than making questions specific is asking consumers about their purchase intentions in a context that is as close as possible to the one they will actually be in when buying the product in question. To that end, the market researchers recommend that marketers first ask a number of background questions aimed at making respondents sensitive to their own behavior. So, for example, before springing a new flavor of low-fat ice cream on a sample of consumers, marketers should ask them how often they buy ice cream, what other things they eat for dessert, and whether they have been trying to cut down on fat. Marketers should even make consumers aware of the other brands of ice cream they would be giving up by selecting their product. Typically, this is how consumers really make decisions in a supermarket. That is, they do not buy in a vacuum. They pick a product off the shelf that is sitting next to others. Ultimately, perhaps even more important than asking the right questions in the right way is realizing the limits of what the answers can reveal. If marketers really want to know how much of their product consumers will eventually buy or whether consumers will keep coming back for more after the initial purchase, they need to understand that it is not enough simply to ask. Overall, purchaseintention surveys can take market research and analysis only so far. Marketers would also do well to observe consumers in real buying situations.8 From another view, an expanded view of market research and analysis can center on virtual reality systems, as noted in a book by the author. Advances in three-dimensional modeling and computer graphics make it possible for market researchers to recreate an actual retail store on a computer screen. Virtual shopping simulations are easy to undertake and low in cost. Being extremely flexible, market researchers can change the assortment of brands on display within seconds to minutes. The virtual store gives market researchers the freedom to exercise their marketing knowledge early in the product development process without risk. As such, virtual reality can be used as a strategic tool that can change the way companies approach issues ranging from entering new markets to responding to new competition. A capability to compare one marketing approach against another gives market researchers new knowledge within a short time frame that is not possible with prior approaches.9 Using Database Marketing to Discover More about a Company’s Customers As markets grow more competitive and companies focus on competitive advantage, it becomes increasingly clear that marketing management needs to keep current customers happy while trying to find new ones. Typically, database

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marketing is a customer-centric approach that helps a company discover what individuals are buying, know how often they have been contacted, and see what marketing strategies have been most effective for individual customers. Giving customers what they want is more essential than ever and database marketing is a key to reaching that goal. Recent studies have shown that approximately 80 percent of recurring revenues come from 20 percent of a client base. As such, database marketing provides the tools to discover who makes up that 20 percent of this base so that marketing management can target campaigns to those customers or look for replacements. Database marketing can be looked upon as an extension of the data warehouse concept (as set forth in Chapter 5). In a broad sense, database marketing is a set of activities ranging from promotional selling to strategic market analysis that centers on the collection, analysis, and use of individual customer attributes and behavior patterns. In the past, marketing databases were built on mainframes or midrange systems. The trend is to distribute query and analysis functions through client/server architectures. Client/server configurations provide flexibility, high speeds, and user-friendly interfaces. Additionally, they can easily be linked to PC-based applications for additional analysis and presentation, all at a fraction of mainframe computer costs. Some companies develop marketing databases strictly as analytical tools to maintain, consolidate, and analyze information captured by other transactional processing systems, while others use them to automate both transaction and analysis functions. Typically, companies gather large amounts of data on customers and their buying habits. In turn, marketing database software helps companies merge data from separate files, thereby yielding new information about customer attitudes and preferences, while computers and analytical tools help marketers discover knowledge to gain a deeper understanding of marketing trends in consumer behavior. In this process, raw data is transformed into useful information and, through the use of sophisticated tools, converted into useful marketing knowledge. Overall, database marketing is enabling companies to learn more about their customers so that they can better anticipate what products and services customers are likely to buy. Using Marketing Software for Tactical Intelligence To get a handle on useful marketing software found in a BIS operating mode, it would be helpful to review what business intelligence is all about in marketing. Business intelligence is about synthesizing useful marketing knowledge from large data sets for a better understanding of a company’s marketing operations. It involves integration, summarization, and abstraction as well as ratios, trends, and allocations. It is about comparing marketing generalizations based on data with model-based assumptions and reconciling them when they are different, and it is about good, creative thinking facilitated by data and the monitoring of the creative ideas that an organization implements. It is about using all types of

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data wisely, understanding how to calculate derived data, and continually learning and modifying marketing goals and working assumptions based on datadriven models and experience. In short, business intelligence should function as a virtuous cycle of decision-making improvement of marketing operations. From this perspective, BI needs to include at least OLAP because of its aggregation and multidimensional calculation capabilities. Initially, however, a suite approach to business intelligence is presented from Business Objects. Business Objects is best known for its flagship decision support tool—now Objects 5.0—an integrated query, reporting, and OLAP tool for client/server environments. The key to Version 5.0 is the addition of analytical reporting, which combines such factors as report distribution and management with ad hoc access to corporate data, report creation, and OLAP function. A new tool in the suite is Set Analyzer, which brings set-based analysis technology to sales and marketing environments. Set Analyzer allows non-technical users to execute effective queries quickly against large amounts of data. Set Analyzer’s underlying technology breaks queries into steps, such as add, keep, or start. The user can click on a toolbar to add or delete selected elements. Also, there is Broadcast Agent—an enterprise and broadcast server that provides a common distribution backbone for users of Business Objects 5.0 and Web Intelligence. Broadcast Agent can trigger time-based, regularly scheduled reports and, using intelligent agents, can automatically trigger reports and natural language messages based on unusual business events or conditions. While business intelligence is recognized for processing structured data for decision-making support, those systems are heading toward processing both structured and unstructured data. BI solutions have demonstrated the ability to help companies uncover, analyze, and understand information and knowledge derived from a wide range of data sources. Beyond OLAP, which spots trends and patterns, there is MOLAP (multidimensional OLAP) which offers multidimensional data analysis. Multidimensional viewing allows a company to generate reports simultaneously from various perspectives (e.g., customer, financial, product) giving comparative perspectives. This slice and dice analysis is very helpful to marketing decision makers. Because they can make decisions by comparing things, MOLAP gives decision makers the ability to visualize a business from many different angles. Additionally, rational OLAP (ROLAP) handles larger data sets of relational data but places larger demands on performance. A cross between MOLAP and ROLAP is offered as hybrid OLAP (HOLAP) which balances performance demands with larger data sets. Hence, OLAP and its variants are viable software options for marketing decision makers.10 To assist marketing decision makers in discovering their customers “needs” and “wants,” transforming these into products and services, and developing effective methods for their sales and service, there is an emerging category of software tools, called marketing automation, that focuses on applying software technology to aid in this process. Specific tasks tackled by some of these pro-

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grams include: intelligent marketing assistance, lead management, campaign management, and data mining. Each of these software areas is treated below. Intelligent marketing assistance is provided in real time so that the software detects interests and buying habits of customers in order to deliver more precisely targeted marketing messages. With intelligent marketing assistance, telephone sales representatives instantly know far more than the name of the caller. They also know the caller’s recent purchase activity and other detailed demographic information. Furthermore, the system instantly recommends a custom sales script. On the other hand, lead management includes all tasks associated with acquiring prospect leads from such diverse sources as Web sites, trade shows, media advertising, and telemarketing campaigns. More importantly, a lead management solution provides tools to distribute leads among the various sales channels. Rubric, developers of Rubric EMA, supplies a comprehensive suite of tools for creating automated campaign management, including continuous relationship marketing processes guided by intelligent workflow; automated fulfillment and management of collateral requests, events, Web-based inquiries, Web-based lead generation and distribution; and fully automated revenue tracking. Rubric applications integrate all marketing data and processes into a single package that is more efficient than other solutions. As discussed previously in the text, data mining software is capable of slicing and dicing a company’s database looking for patterns in order to provide the most useful “nuggets” to guide marketing efforts. Data mining solutions help a company provide sophisticated analysis functionality for predictive modeling, trend analysis, application scoring, and customer segmentation. The software helps identify the most profitable customers, detect and define customer attrition trends, determine optimal timing for product rollouts, model customer buying behavior, and discover previously unrecognized patterns in customer data that lead to new marketing opportunities. Reference can be made to Chapter 4 for current vendors. Other marketing software useful for tactical intelligence centers on formulating a useful marketing plan. Such software includes Marketing Plus and MarketingBuilder Interactive. Marketing Plus helps marketing decision makers formulate viable marketing plans and produce charts and graphs, and gives them a direction to follow. One of the centerpieces of the program is the Strategy Pyramid, a pyramidal flowchart that is a visual representation of a company’s marketing goals. The program takes care of difficult work by providing on-line advice as well as formatting the information inputed. A viable and effective plan is produced in a matter of hours. Similarly, MarketingBuilder Interactive leads marketing decision makers through an on-line interview to get pertinent information regarding the company’s background and market and financial history. Then a number of documents, charts, spreadsheets (such as Direct Mail Analysis, Internet Marketing Manager), and press releases can be chosen. In a reasonable amount of time, a marketing plan, promotional materials, and a comprehensive

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budget for promotions in various mediums are produced. Although the interactive features only work in Windows 95/98, the document templates in Windows 3.1X and Mac systems can be accessed. To assist in developing a viable marketing plan, it is helpful to forecast future sales that will impact the marketing plan. Today, there are an overwhelming number of statistical packages to assist in the forecasting process. One such package that is widely used is Forecast Pro, which is an expert system’s forecasting package that has a number of features. One of its most noteworthy features is that it can be run in an expert system mode. Thus, the user has a choice between consulting a rule-based expert system, which will recommend an appropriate forecasting method based on the characteristics of the time series data, or directly accessing the underlying forecasting methodologies. The system is designed to switch easily between the automatic (expert system selection) and the manual modes. When run in the expert system mode, Forecast Pro provides a useful audit trail that explains the logic for choosing the selected method and collects all of the important diagnostic statistics generated during a session. The model menu is sufficiently encompassing so that likely model choices are considered. Diagnostic tests for both the estimation and the hold-out periods are more than adequate, especially in the regression model selection mode. Still another example of tactical intelligence software useful for improving customer relationships is in the area of CRM. Achieving that goal can be easier with the introduction of the Internet and its technologies into customer relationship management applications. An Internet-enabled CRM application allows a business to reach customers in ways that enhance customer loyalty at a reduced cost. Using sophisticated, database-driven, dynamically generated Web pages delivered by scalable Hypertext Transport Protocol (HTTP) servers, a business can offer more services on a 24-hour basis than a room full of call center employees can deliver using only the telephone. The unique attributes of the Web, mainly its pervasiveness and standardization, creates an ideal environment for delivering 24-hour customer support by Web-enabled CRM solutions instead of higher cost support. Microsoft offers a wide range of technologies to help build, design, and manage high-quality Web sites that help businesses market, sell, service, and support their customers. With the Microsoft Windows Distributed Internet Applications architecture, CRM solutions can be rapidly adapted to meet changing customer requirements, such as the self-service Internet channel. MARKETING FUNCTIONS THAT LEND THEMSELVES TO TACTICAL INTELLIGENCE Marketing areas that lend themselves to business intelligence are varied, as will be seen in this second half of the chapter. The capability of sales and marketing decision makers to spot trends and patterns in the marketplace and understand them thoroughly will be apparent in the following discussion. The

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capability of marketing managers to analyze key aspects of the changing marketing landscape and make key decisions about the present and future directions of markets will make a very important difference between being somewhat successful to being extremely successful. A company that undertakes a creative approach that is proactive to current and prospective customers can utilize a business intelligence system to help get a better handle on its customer base. A company cannot reap the rewards of marketing knowledge and, in turn, marketing intelligence simply by buying some hardware and software. Instead, it must take a longer range view and acknowledge that ongoing marketing knowledge and intelligence is critical to new product development and related services. Related to these factors is the development of appropriate marketing strategies for the times. In addition, the pricing of products and services is critical to a company’s success. Other areas that typically lend themselves to marketing intelligence include advertising, market research, and physical distribution. Due to space limitations, only the areas of marketing strategy and product pricing are covered below. MARKETING STRATEGY In this 21st century, characterized by expanding global markets, faster product cycles, and shrinking profit margins, companies are spending millions of dollars annually on appropriate marketing strategies. To be successful, companies must constantly focus on identifying market changes in these fast-changing times. Marketing decision makers must search vast amounts of corporate data, information, and knowledge for answers that enable their companies to develop an intelligent overall marketing strategy. Marketing decision makers need a business intelligence system that delivers a wide range of tactical intelligence to their desks every day and that allows them to spot market trends, rank products, analyze sales personnel performance, or quickly create ad hoc views of sales by account or region. To be useful, the business intelligence system must be timely, flexible, and intuitive not only in marketing but also in its related functions. Development of an Overall Marketing Strategy That Uses Tactical Intelligence To develop an overall marketing strategy that makes use of tactical intelligence, it is initially necessary to understand who the customer really is. As a starting point, it would be helpful for marketing managers to internalize the marketing principles set forth previously in Figure 8.1. As stated previously in the chapter, the customer is the most important entity in the distribution channel. As such, marketing managers must consider the present needs and future wants of their customers in every step of the distribution channel. It is helpful to spend a typical day in the life of customers (small, medium, and large) from the standpoint of meeting their needs today and their wants tomorrow. For example,

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even though a company ships all of its orders 95 percent of the time, the customer views it differently, since the order must be 100 percent complete before the item can be introduced into production. This is where on-line analytical processing can come into play, since multidimensional analysis can be used to help understand the customer’s viewpoint. In this example, multidimensional analysis of three variables (percent of shipments received on time, percent of shipments received late, and days late for remaining items) may give marketing managers a better perception of its operations through the eyes of a typical customer. Although the results may not be very positive, OLAP is capable of presenting a realistic picture of a typical customer to marketing managers. In turn, the results can help marketing managers gain a better understanding of the customer from a good, bad, or indifferent perspective. An effective way to develop an overall marketing strategy using tactical intelligence can be found in marketing efforts of well-known companies. For example, Procter & Gamble has undertaken a global initiative to make its marketing efforts more effective as well as efficient. In the 1980s and 1990s, P&G lost its focus as it digested major acquisitions, such as Richardson-Vicks and Tam-brands, and expanded into new overseas markets in China, Asia, Central Europe, and Russia. The company’s restructuring, called Organization 2005, has been designed to regain that focus, accelerate growth, and allow more and faster product innovation. To meet projections, Procter & Gamble has reorganized the way it does business, slashing management layers, streamlining and simplifying manufacturing, and radically altering the way most executives are compensated. Globally, P&G is building even closer ties to a reduced number of outside companies that do everything from artwork to package design to printing materials for promotional campaigns. Procter & Gamble has looked at its whole system of developing and executing its marketing programs with its suppliers to find ways to improve flexibility, save time, and get the results it seeks at a lower cost. Additionally, P&G is leveraging its size as the nation’s largest advertiser in terms of spending by using one agency to make all of its media purchases. Thus, Procter & Gamble has developed a global strategy that relies heavily on business intelligence, especially in marketing, to meet its five-year sales growth and earnings per share. To help carry out an overall marketing strategy, companies are using push technology (a form of electronic communication) to assist marketing management. Push is better than E-mail for marketing personnel who receive a lot of data and updates because E-mail tends to collect. Push updates replace the notification that came ahead of it, preventing confusion and unnecessary E-mail overload. If marketing personnel receive volumes of E-mail and important marketing messages are getting lost in the eternal wave of E-mail, then push helps sort the wheat from the chaff by highlighting important messages. If a marketing message is urgent, for example, it will blink. If it is a vital marketing message, many push tools automatically open a window, forcing the recipient to read it immediately. At the more sophisticated end of the spectrum, push far surpasses

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E-mail by incorporating vast amounts of diverse and complex marketing information and distributing it to the appropriate parts of the organization. The bottom line is that tactical intelligence can be relayed to the appropriate marketing decision makers to give a company a competitive advantage. One of the leaders in this enlarged version of push is Back Web Technologies (San Jose, California), whose Web address is www.backweb.com. This company markets a client/server knowledge center called Infocenter 4.0. The Infocenter enables companies to automatically gather and filter information from many sources and organize it in one location. Using this solution, companies have the ability to create customized marketing tactical distribution solutions for intranet and extranet applications. For example, Back Web can be used to deliver product information and pricing updates to a company’s sales force as well as alerts and status reports on deliveries. If it looks like a ship date is going to be missed, the Back Web system sends an alert to the appropriate sales representative, who can then proactively inform the client and alleviate concerns. The system can also use push to send delivery notification reports directly to vendors and, in turn, to their clients so that all parties are up to date on an order’s progress. From this perspective, marketing personnel can apply tactical intelligence to everyday operations. Tie-in of an Overall Marketing Strategy with Customer Relationship Management Essentially, customer relationship management requires an evolution from the mentality of “making an immediate sale” to one of “managing the life cycle of a customer.” This holistic approach to managing a customer’s life cycle through technology goes way beyond call center and sales force automation packages. CRM now encompasses all customer-facing applications and includes field service, E-commerce ordering and self-service applications, catalog management, bill presentation, marketing programs, and analysis applications. Numerous corporate initiatives drive CRM applications suite configuration, which can take many forms. Most often the primary goal is revenue growth through improved customer satisfaction. Another reason to invest in CRM is reduced cost of sales and distribution. A company can accomplish this cost reduction by raising the percentage of customers that can order via Web applications, which decreases the number of direct sales people and distribution channels the company requires. Another frequently noted cost reduction area is the minimization of customer support costs. Providing more complete information and knowledge to customer service representatives lets them respond to virtually any customer inquiry. From another perspective, solutions in the area of electronic CRM range from sales force automation solutions that are like giant PIMs with shared databases to ERP-like enterprise-wide solutions with ties into remote sales teams, call centers, E-commerce sites, main offices, legacy applications, financial data, man-

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ufacturing inventory, and marketing resources. However, a true CRM solution must deliver comprehensive customer information to every employee who interacts with the customer. For example, the Imparto Web Marketing Suite (from Imparto Software, Mountain View, California) manages Web-site content, captures leads and executes Web marketing campaigns and delivers customer information to the customer. This is where electronic CRM or Web-enabled or Web-based CRM expands the capability for a company to build stronger and more connected relationships with its customers. In terms of Web-enabled and Web-based applications, it is important to understand the distinction between the two. Many of the first client/server CRM tools touting links to the Web were simply Web-enabled, meaning they were still based on the underlying client/server architecture but had a Web interface added. Such Web-enabled tools simply generate HTML (Hypertext Markup Language) pages from the client/server screens. On the other hand, Web-based tools are designed to run in an Internet or intranet environment rather than a client/ server environment. There is also an in-between world of products that do a little of each. Some CRM solutions run on the client/server network and provide a browser front end to a user’s data. Typically, many actually work behind the scenes, thereby saving specific data when necessary and routing it to the right place on the network. In light of the foregoing comments about customer relationship management—in particular, electronic CRM—customers’ needs and how marketing managers develop an overall marketing strategy to fulfill these needs ultimately center on customer satisfaction using these tools. Typically, retaining existing customers is usually much more profitable than focusing on acquiring new customers. Even though many companies state that customers are important, often company employees do not rate customer satisfaction as a top priority for their company. Many times, there is a large gap between what a company says it does and what its customers perceive it as doing. An in-depth study and understanding of customer needs and how the company has or has not met those needs can be used to determine the degree of customer satisfaction or lack thereof. An effective overall marketing strategy goes beyond the traditional approach that focused on immediate results, such as quick sales for immediate customer satisfaction. Rather, the focus today is on long-term customer satisfaction, where repeat sales and customer life cycles are found in a customer relationship management operating mode as part of an overall marketing strategy. Customer Analysis to Select a Specific Marketing Strategy Using facts about customers that are acquired over time, marketing managers of a typical company can create a specific marketing strategy. They can start with market surveys, market tests, and market audits performed by the market research department. But none of these answer the important questions for mar-

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keting managers, such as what would happen to profits if the number of users increased next year for the company’s products—that is, if the “proportion usage” increased over the current year? Similarly, what would happen to profits if the “rate of usage per year” for the present number of users increased next year over the present year? To answer these two questions and develop a particular marketing strategy, it is necessary to analyze past and future possible demand for the company’s products. For example, marketing managers need to look at the number of newly formed families who are concerned about the product. As noted above, there are two possible strategies for increased demand: (1) increase the proportion of buyers in this market segment or (2) increase the usage rate of those who presently buy such a product. In addition, these strategies can be developed over optimistic, average, and pessimistic conditions. In this example, multidimensional analysis can be used to analyze the components of the problem in order to answer the preceding questions. Generally, historical data on the number of persons, proportion buying, and rate of buying per year, along with the unit price, variable costs percentage, and the number of buyers expected, come from a knowledge of the company’s customers over time. To increase the proportion-using category, say, from 60 percent to 65 percent under pessimistic conditions (first case), 70 percent under average condition (second case), and 75 percent under optimistic conditions (third case), appropriate multidimensional analysis can be undertaken in terms of contribution to fixed costs and profit by products and cases. An alternative is to accept, say, the 60 percent proportion and find a way to increase the usage rate from 1.95 to 2.15 people, for example, under pessimistic conditions (fourth case), 2.25 people under average conditions (fifth case), and 2.35 people under optimistic conditions (sixth case) next year by products. In each of these six cases, a strategy can be translated into an estimated contribution to fixed costs and profit by products and time periods. Based upon the circumstances, the best marketing strategy can be selected, such as an increase in buyer usage, that gives the greatest return for the company’s products under study for future time periods. However, it should be noted that there might be shifts in the company’s markets over time. Hence, knowledge of the company’s customers may indicate the need to redo the study to ensure that the company is pursuing the best marketing strategy. Sales Analysis to Identify Market Changes To demonstrate a typical application of tactical intelligence in marketing, reference can be made to sales analysis in order to identify market changes today as a way of predicting tomorrow. In terms of sales by geographical areas, a three-part level-of-inquiry format can be utilized—that is, (1) total geographical sales, (2) detailed geographical sales, and (3) exception geographical sales.

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Figure 8.2 Total Sales History for One Geographical Area of the United States over the Last Five Years

In the process of this analysis marketing managers can manipulate the data by looking for future trends, performing audits of the sales data, and calculating totals, averages, changes, variances, or ratios. At the highest level of marketing analysis, total geographical sales is the overall sales performance for the current year as well as past years. This level of marketing inquiry can be extended to include an overall performance summary that describes the sales budget and actual month-to-date, last month-todate, year-to-date, and last year-to-date sales by geographical areas. If a given geographical sales performance is deficient compared to the budgeted figures, control can then be transferred to successive levels of detailed reports relative to the area of interest. Also, projected total sales can be generated by geographical areas. The second level of inquiry, detailed geographical sales, allows for optional levels of detail to be obtained. Normally, the inquiries requested of this structure are triggered for further analysis by the total sales inquiry. As with the first level, data are available for the same time periods and by the same sales categories at the detailed level. Also, this inquiry format is programmed to enable the sales managers to format report structures because they might be interested in certain sales ratios or sales-to-expense ratios. An example of detailed geographical sales is found in Figure 8.2 where the total sales history for one

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Figure 8.3 A Graphical Comparison of a Company’s Total Sales Amounts for One Geographical Area of the United States over the Last Five Years

geographical area of the United States over the last five years is shown by amounts. In turn, a graphical comparison is depicted in Figure 8.3. When the data are ranked over the last five years—from best to worst—the following dates and sales amounts are noted:

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Based on these sales figures, the best sales by month over the last five years are in April of year 5 (current), or $28,300. On the other hand, the worst sales by month over the last five years are in May of year 1 (oldest), or $11,200. Comparable analyses can be developed for the other geographical areas of the United States and other geographical areas of the world. In all cases, the focus of the analysis is on the highest sales periods for one geographical area versus other geographical areas. Similarly, it is necessary to look at the lowest sales periods. Such an analysis might help determine where goods can be shipped from one part of the world to another to smooth out production and sales. Lastly, the third level of inquiry, exception geographical sales, is generally more complex and flexible than the first two. The exception inquiry is structured to highlight a certain condition or conditions. Typical inquiries might be “Which products in a certain geographical area of the United States are above 110 percent of forecast sales and below 90 percent of forecast sales for the current month?” or “Which salespersons in a certain geographical area of the United States are below their sales quotas for the current month?” This inquiry level provides not only tactical intelligence to individual sales managers about the effectiveness (or lack of it) of the company’s marketing effort but also provides a forum for a group discussion by marketing managers such that each geographical sales area of the United States can be compared to other areas. An alternative to the above approach to sales analysis and other analyses is the combining of OLAP and data mining—that is, the focus initially is on dimensional reorganization and, then, subset discovery. For dimensional reorganization, the focus is on creating the appropriate dimensions for the areas under study. Data-driven categorizations and organizations have vast potential for simplifying the analysis. The data can come from customer segments, use patterns, buying patterns, and the like. In terms of subset discovery, there is the search for geographic aggregates that represent what actions to take with respect to classes. The complexity and size of the data requires an OLAP context, but the search for the right regional aggregates would be greatly facilitated by the use of data mining or optimization methods. Decision trees can also be used to identify subsets. They generate the break points of the independent variables that best forecast a dependent variable. PRODUCT PRICING Pricing of a company’s products (as noted previously in the chapter) is one of the four elements comprising the marketing mix. Because of the complexity of the marketing environment, there is a great need for setting correct prices. Pricing is a problem when a new product is being introduced, when a price change is contemplated in the face of uncertain customer and competitor reactions, or when a company must react to a competitor who has just changed its price. Pricing is also a problem when sealed bids must be submitted. And it is a problem when the company’s product line is characterized by substantial de-

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mand and cost interdependencies. In view of these problems, new product pricing can best be determined by applying appropriate statistical or mathematical models as well as data mining tools to proposed selling prices and estimated demand. Such an approach can be an integral part of venture analysis of a new product or service (as discussed below). Additionally, there is a need to evaluate pricing as related to competition, along with the relationship of product quality with price charged. It should be recognized that low-quality products mean that premium prices cannot be charged. Similarly, a weak market position has a detrimental effect on prices charged to customers. Although price should not be the complete marketing strategy, it also cannot be ignored altogether. So how can a small company that might not qualify for the supplier discounts or achieve the economies of scale of larger businesses stay competitive? The answer lies in one or many of the following: (1) carve out a niche so as to “own a market,” (2) work smarter than the competition with innovative practices that affect the customer, (3) focus on value for the customer’s money and not on price, (4) target the right customers who are willing to pay more for better quality, and (5) build customer loyalty to the company and not to price. Some areas of pricing that are related to a business intelligence system are given below. Pricing Products over Their Life Cycle One of the most extensive and sophisticated marketing mathematical models is venture analysis. It is an investment planning system for analyzing new opportunities and encompasses such techniques as probability, decision theory, and the time value of money as well as mathematical modeling. Because it is a massive system of gathering, relating, appraising, and projecting all data pertinent to a complete business venture over its life cycle, this mathematical technique stores many kinds of information. All costs involved in the product project are developed. Manufacturing costs include raw materials, direct labor, depreciation, and overhead. These data are modeled for each step of the production process. For the pricing and promotional effort, product prices are estimated at various desired levels to determine the optimum price. All promotional costs involved in marketing the product are broken down by media selected and projected. Research and development costs and general and administrative costs are scheduled to make the data complete. An important part of the venture analysis model is knowledge about the proper determination of prices at various stages over the life cycle of the product by marketing modelers. Utilizing a pricing approach within venture analysis, the model relates numerous price, advertising, personal selling, and sales promotion combinations for the product under study and pertinent facts about competing products. Essentially, the venture analysis model hypothesizes about the degree to which competitors will react to a price change and in what form this reaction will occur. Using an appropriate statistical and/or mathematical model,

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it analyzes which blend of marketing decisions will go best with a given price. In turn, it determines what effect the given price will have on the sales of other products in the product line. The pricing aspects of a new product, then, are included in the venture analysis model along with the prices charged by competition. However, it is up to the marketing managers to review these prices and make appropriate adjustments based on their knowledge about what is reasonable as well as their experience and judgment. Otherwise, unrealistic prices will be used to evaluate a product over its life cycle. In turn, the product will be priced unrealistically in terms of its competition. Of paramount importance for venture analysis is knowledge about specific variables, assumptions, constraints, and like items to be considered for inclusion in the model by marketing modelers. For example, in terms of variables, an itemization of all candidate variables based on the subjective judgments of marketing executives is necessary. Knowledge of the marketing and pricing environment and the relative importance of various factors is necessary. It is imperative to build a data warehouse of historical marketing data, including pricing data, covering not only this company’s new products but also all new products introduced in the industry over the past few years for which adequate sources data are available. It is from these data that the statistical and/or mathematical relationships subsequently expressed in the model are derived. Thus, it is apparent that considerable pertinent data are needed before a new product can be evaluated effectively. Utilizing the venture analysis model for a new product or service, a marketing modeler can meet with a company’s marketing managers, who will supply various estimates as they are called for by the model, including the estimated size of the target group, recent product trial data, repeat purchases, the promotional budget, size of investment, target rate of return, product price, and gross profit margin for each product under study. The model will analyze this information under computer control and display a forecast for price along with the total number of customers, company’s market share, windfall profits (if applicable), period profits, and discounted cumulative profits. The marketing managers can alter various input estimates and readily ascertain the effect of the altered data on sales and profits. Additionally, prices can be varied over the life cycle of the product. That is, the first year(s) allows a company to charge higher prices due to the newness of the product. As competition moves in, prices are generally lowered and profits tend to fall per unit. When the product is removed from the market or changed to meet emerging customer needs, there can be a change in the pricing structure of the product or service to meet changed conditions in the venture analysis model. Although the foregoing analysis has centered on one product or service, in actuality there are generally a number of products or services that undergo the venture analysis modeling. After all analyses deemed appropriate for the new products or services under study have been performed by the marketing modeler,

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marketing managers compare alternative product or service ventures to determine which is the most viable, the one that will be accepted. For example, a company can compare its return on investment for five proposed products over five years using the returns shown in Figure 8.4(a). Based on these returns, a graphical comparison for these five proposed products over years 1 through 5 can be displayed as shown in Figure 8.4(b). In turn, a ranking of the five products in terms of their returns is as follows for year 5 (best year):

Finally, the best and worst returns on investment for the company’s five proposed products over five years are: Best Product 2, Year 5

36.5%

Worst Product 1, Year 1

18.0%

Overall, proposed product 2 gives the highest return in investment over the next five years for upgrading the company’s product line. If new knowledge is used in this example, it may well be that the proposed product 2 is not the best choice. Currently, variations of venture analysis are offered by a few software vendors. For example, Cincom Systems (Cincinnati, Ohio) is marketing a suite of software products called Control:Acquire to automate and support the tasks required to bid on new business. This software package manages an entire sales cycle from prospecting for business to estimating costs and developing proposals. One feature of the software is called Workbench Configuration. It can process product requests from customers so that sales staff and customers have a complete picture of the components needed to meet a customer’s needs. The Workbench feature can lead a customer through the selection of product parts and tasks. The software can then compare those choices to a knowledge bank of product information to make sure that the customer has selected a buildable product. A second feature, called Estimating, is designed to give timely and accurate cost estimates when developing proposals. A third feature is Proposal Management, which automates the job of maintaining information on sales proposals and can also send sales orders. A fourth feature, Work Breakdown Structure Planner, automates the task of scheduling and monitoring the implementation of a customer’s order. Essentially, this product is designed to reduce sales overhead costs, order-to-shipment cycle times, and expensive order revisions.

Figure 8.4 (a) Return on Investment for a Company’s Five Proposed Products over Five Years and (b) Graph That Compares a Company’s Five Proposed Products over Five Years

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Relating Web-Based Advertising to Pricing In the past, a company’s advertising message to present and potential customers focused on a wide variety of approaches. These included personal selling to customers, along with some type of sales campaign to back up this type of selling effort. In addition, there was the selection of the best set of newspapers, magazines, radio, TV, and billboard ads to reach the intended audience. Important variables influencing the media selection process centered on: (1) the availability of time or space in each medium, (2) the advertising budget, (3) exactly what target group the company wished to reach with a given message, (4) the value of each repeat exposure, (5) the quality of the advertising medium, and (6) the cost of running a selected medium. Needless to say, it was no simple task to formulate an effective advertising program that identified the key variables and quantified the relationship among them. Today, advertising has taken off in another important direction. Anyone who has followed the evolution of advertising on the Internet has noticed that performance-based advertising (which lets a company measure the success of each ad placed) has become something of a standard. This has happened for a simple reason. On the Internet, an advertiser can not only measure the number of people who see the ad but also track the number of people who “click through” an ad to get more information from the advertiser’s own Web page. Armed with such data, an advertiser does not have to settle for spots that do not always deliver customers. Unlike a print or television ad buyer, but like a direct marketer, the advertiser can pay only for messages that reach the most receptive audience. Initially, to exploit Web-based advertising, some ad sellers tried to play the system by using the technology to artificially inflate click throughs on their sites. But buyers looked beyond the click throughs to analyze the purchasing behavior of their customers. To overcome this problem, AdKnowledge (Palo Alto, California) and Personify (San Francisco, California) developed market analysis packages that told E-merchants not only which advertisement produced click throughs but also which customers actually bought, how much they bought, and what the margins were on each sale. Companies of all sizes are making use of Web-based advertising to sell their products and services. For example, Procter & Gamble is making use of several measurable and interactive efforts by Yahoo! that are designed to help connect Yahoo! users with P&G brands, including Pringles, Pepto-Bismol, and Pampers. Pringles is focusing on reaching on-line game enthusiasts. The snack product is featured in Yahoo! Games with 12 classic card, board, and tile games. Players can win points by playing the games, and points can be redeemed for a chance to win Pringles prizes. In addition, the companies have developed a multi-brand program in Welcome Home from Yahoo!, a direct marketing program featured in Yahoo! Real Estate and Yahoo! Loans. Among the Procter & Gamble brands featured on the pages are Cascade, Dawn, Folgers, Cheer, Bounty, Dryel, Mr.

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Clean, Febreze, and Crisco. Overall, Yahoo! has developed several innovative marketing programs for P&G’s brands that enable it to reach consumers in new ways that become a relevant and integrated part of their lives. To help users shop the best price advertised, Andersen Consulting created BarginFinder, an intelligent agent that comparison-shops for the best price among suppliers of music CDs on the Internet. Users tell the agent which CD they want, and the agent reports back on the price offered by several sites. Andersen Consulting sees this easy comparison shopping as likely to put price pressure on suppliers. Another factor that will create price pressure is the potential for unconventional selling practices. For example, the Internet might be the site of auctions for products not previously sold that way. It could also be used by buyers much as want ads are used today. All in all, Web-based advertising has the potential to equal the more traditional forms of advertising. Relating Product Quality to Pricing The Profit Impact of Market Strategies (PIMS), discussed previously in the chapter, yields knowledge about what works, what does not, and why. Although PIMS principles are set forth by Robert Buzzell and Bradley Gale in their book, the most important single factor affecting a business unit’s performance in the long run is the quality of its products and services relative to those of competitors. Buzzell and Gale illustrate the linkage between relative quality and business performance in their Perdue chicken example.11 Chickens have as strong a claim to commodity status as pork bellies or crude oil. The performance of each competitor was the same as each product and service attribute. (This was actually ranked by the PIMS study.) This placed Perdue and its representative competitor at the 50th percentile on relative quality, neither ahead or behind. With no difference in performance on product and service attributes, the customer basically bought on price. After Frank Perdue took over the chicken business from his father, he pulled ahead on almost every non-price attribute that counts in the purchase decision. His research showed that customers in his served market preferred their chickens plump and yellow. Careful breeding and the judicious use of food additives enabled Frank to produce meatier, yellower chickens than competitors. Over time, Perdue initiated capital investments to improve the real and perceived quality of his chickens. Needless to say, Perdue executed an effective advertising campaign dedicated to communicating the quality of its chickens. Although such analyses were not called on-line analytical processing at the time, today it would be construed as such. Essentially, Perdue invested heavily to create a real quality difference in its product. According to extensive PIMS research and as noted above, the most important factor in the long run affecting a business unit’s performance is quality of its products and services relative to those of competitors. Perdue built its advertising around a big idea, namely that Perdue chickens were superior. In

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turn, Perdue charged a premium price for its chickens. Major advertising research indicates the price of a product or a service must be consistent with the image it is trying to build. In the short run, superior quality yields profits via premium prices. PIMS businesses (3,000 business units in total) that ranked in the top third on relative quality sold their products or service, on average, at prices 5 to 6 percent higher relative to competition than those in the bottom third. In the long run, on the other hand, superior quality is the most effective way to grow. Quality leads to both market expansion and gains in market share. The resulting growth in volume means that a superior-quality competitor gains economies of scale advantages over the competition. EFFECTIVE MARKETING BIS APPLICATION— TELEBRANDS In the example to follow, the basic elements of business intelligence systems should be somewhat apparent. When the Fairfield, New Jersey merchandise company Telebrands advertises on television, the company’s marketing managers use business intelligence technology to determine quickly the success of an advertising campaign. Say, a TV advertisement fails in Kansas City but succeeds in Seattle, Telebrands wants to be able to adjust the campaign within hours in order to concentrate on the biggest producers. Also, the company wants to know how a particular product is selling on a particular station on any given day and whether customers can find the products on the shelves of national chain stores. Basically, the data chain begins with General Automation’s UNIX-based Sequoia mainframe, which runs the Sequoia version of the PICK database management system. The mainframe feeds a data warehouse and three data marts, which run SAS Institute’s Executive Information System. The warehouse and marts reside on Compaq Prosignia Windows NT servers and are accessed from Windows Desktop PCs, also Compaqs. The data marts feed the SAS multidimensional database (MDDB) viewer, which enables users to drill down through the cube and view data summaries, which are updated daily. With this kind of detail, Telebrands’ employees can make informed business choices. For example, they can decide when to move a product from television to retail stores, what outlets to target, and what prices to set. The business intelligence system helps Telebrands channel partners. By uploading from retailers’ POS systems, Telebrands knows if stores have bought merchandise too aggressively, in which case Telebrands can adjust inventory and pricing, thereby allowing for maximizing profits for the stores. The systems also lets Telebrands systematically apply the lessons of the past. That is, if a product is introduced that is similar to one sold four year ago, a marketing and sales plan can be developed based on those earlier successes. Using historical data from legacy systems and data from the company’s di-

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visions in the United States, United Kingdom, Canada, Hong Kong, and India, the SAS Executive Information System software handles the global aspects of Telebrands’ business as well. It measures the performance of sales reps and their territories globally. For example, Telebrands noticed that a leading kitchen and housewares retailer was not ordering one of Telebrands’ hottest selling products. Sales figures—color-coded red on a PC—flagged the situation. Overall, the company’s business intelligence tools, with the assistance of software vendors, help managers make more informed decisions for boosting the company’s sales and profits.12 MARKETING INTELLIGENCE IS RELATED TO COMPETITIVE WISDOM Tactical intelligence in marketing (i.e., marketing intelligence) is tied to competitive wisdom. As discussed in Chapter 1, wisdom is the ability to judge soundly. It requires intuitive ability, born of experience, to look beyond the apparent situation to recognize the big picture, along with exceptional factors, and anticipate unusual outcomes. In the area of marketing, this can be taken to mean marketing personnel see things in competitive situations that other marketing people do not see. This requires getting marketing personnel out of their job-specific myopias and getting them involved in the big picture. Salespeople talking to customers about other suppliers, marketing personnel attending scientific conferences, and marketing staff perusing comments and complaints posted to Internet-based newsgroups can help these marketing groups recognize good competitive information that can be turned to the company’s advantage. All of these marketing people are potential analysts. They must be able to observe what is happening in the company at large as well as beyond its borders on an ongoing basis. From this view, competitive wisdom of marketing personnel needs to be actively sought, recognized, nurtured, and refined over time. There is nothing unusual about the observation that business information and knowledge is not limited to standard reports, such as market-share statistics and profit-and-loss statements. On the other hand, the gathering process is eclectic, and while managers will always need repetitive reports of regularly occurring information and knowledge, they also need direct observation inside and outside the company, meetings, informal notes on the Internet, and impromptu conversations around the water coolers. Outside the company, an ever-growing treasury of information and knowledge sits in databases, government agencies, trade journals, and scientific papers, just waiting to be exploited. Every company, in reality, has access to essentially the same marketing intelligence. The challenge is not simply to locate critical information and knowledge and derive appropriate intelligence for competing in the marketplace but also to apply it better and faster than the competition. That requirement leads to the conclusion that a successful company has the capability to absorb a wide array of external data, information, and

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knowledge and, in turn, combine it with up-to-the-minute internal reports to produce sound marketing intelligence. In effect, competitive wisdom views every employee as an enlightened intelligence gatherer, capable of recognizing important information and knowledge and passing it quickly to the right person in the right format for making the right decisions.

SUMMARY The need to rethink the entire marketing mix in order to make the most effective use of business intelligence systems was the initial focus of this chapter. In addition to understanding the need to rethink the marketing process, marketing factors that are related to tactical intelligence were set forth. They included listening to customers and observing their behavior over time, enlarging the view of market research and analysis, using database marketing to discover more about a company’s customers, and using marketing software for tactical intelligence. Essentially, this exposition centered on market leaders who know what customers want when developing a company’s products and services. Or to state it another way, the company knows what its customers want before the customers themselves know and influences the direction that the market will take today and tomorrow. In the second half of the chapter, applications that show the relationship discovered by rethinking the marketing function to better understand a company’s marketing functions in the areas of marketing strategy and product pricing were presented. Additionally, the integration of marketing intelligence with competitive wisdom was highlighted.

NOTES 1. Susan Osterfelt, “Tactical Intelligence,” DM Review, January 1999, p. 20. 2. Robert J. Thierauf, On-Line Analytical Processing Systems for Business (Westport, CT: Quorum Books, 1997), pp. 14–15. 3. Mark L. Van Name and Bill Catchings, “Looking Forward: Shape Process to Customers, Not Customers to Process,” PC Week, February 15, 1999, p. 36. 4. Nick Wreden, “From Customer Satisfaction to Customer Loyalty,” Beyond Computing, January–February 1999, p. 12. 5. Robert D. Buzzell and Bradley T. Gale, The PIMS Principles: Linking Strategy to Performance (New York: The Free Press, 1988). 6. Gary Hamel and C. K. Prahalad, “Corporate Imagination and Expeditionary Marketing,” Harvard Business Review, July–August 1991, pp. 85–86. 7. Stan Davis and Jim Botkin, “The Coming of Knowledge-Based Business,” Harvard Business Review, September–October 1994, pp. 165–170. 8. Andrea Ovans, “Market Research: The Customer Doesn’t Always Know Best,” Harvard Business Review, May–June 1998, pp. 12–13. 9. Robert J. Thierauf, Virtual Reality Systems for Business (Westport, CT: Quorum Books, 1995), chap. 7.

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10. Thierauf, On-Line Analytical Processing Systems for Business. 11. Buzzell and Gale, The PIMS Principles. 12. Samuel Greengard, “How to Profit from Business Intelligence,” Beyond Computing, January–February 1999, pp. 23–24.

9 Operational Intelligence in Manufacturing MAKING MANUFACTURING MORE EFFICIENT AND EFFECTIVE BY UTILIZING A BIS OPERATING MODE Today, worldwide competition demands a renewed emphasis on the quality of products and services. Before a product is manufactured, it needs to be examined in great detail at the design stage. In turn, the manufacturing process is engineered to be stable and reliable. If the design is good and so is the process, quality is inherent. It is necessary not only to build a product faster and cheaper but also make the product better. This approach to quality emphasizes the need for cost effectiveness of productivity in all areas of a company—from design to the assembly line. An important part of product and service quality is an emphasis on smart manufacturing. Smart manufacturing is the ability of backend production systems to listen to instructions and produce quality custom products. Customers can do the ordering themselves over the Internet. As a direct result of increasing global competitive pressures, companies are addressing product development challenges by focusing on what each does best and turning to partnerships to supplement internal abilities. This trend is manifested through joint manufacturing ventures, increased outsourcing, supply chain partnerships, and agile partnerships. Essentially, this approach links best-ofbreed partners in virtual corporations capable of world-class performance. An integral part of this newer approach to manufacturing is to facilitate product and process development over the Internet as well as sharing manufacturing capabilities. It is from this perspective that there is a need to rethink the manufacturing function from a broader perspective to make it more efficient and effective within a BIS operating mode. As discussed in the chapter, this rethinking centers

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on many items, particularly computer integrated manufacturing (CIM), enterprise resource planning (ERP), supply chain management (SCM), advanced planning and scheduling (APS), and total quality management (TQM). This background ties in with the important factors that are related to effective operational intelligence in manufacturing. Next, appropriate operational intelligence applications in purchasing and supply chain management along with production planning and execution and total quality management are presented. The utilization of the Internet to tie in manufacturers with suppliers is presented to demonstrate an effective means of obtaining and using business intelligence. Overall, a business intelligence system approach to manufacturing allows for improving customer satisfaction up and down the supply chain. In addition, it gives managers and their staffs a better understanding of manufacturing operations, which was not generally found in prior systems. Tie-in of Operational Intelligence in Manufacturing with Strategic Intelligence A starting point for operational intelligence is strategic planning at the corporate level. The focus at the highest level is on the establishment of periodic and/or annual manufacturing goals for the company and its various manufacturing facilities (owned or non-owned). If the time period is the coming month, production can be fine tuned to meet the changing economy and demands of the changing markets. Operational plans can be developed that sets specific monthly production goals that can be linked directly to key performance indicators and financial ratios. In turn, these plans can be employed for loading the company’s manufacturing facilities on a day-to-day basis. Hence, operational plans not only look back to strategic planning for their overall direction but also provide the necessary input for daily production planning and execution, which will be covered in detail toward the end of the chapter. For a thorough understanding of a company’s manufacturing operations (i.e., operational intelligence), operational knowledge of a company’s day-to-day operations in specific manufacturing departments is needed. For example, a manufacturing supervisor has to know if material wastage is exceeding the standard, if costly overruns that exceed the standard are in the making, and if the standard time allocated to a specific job has been exceeded. All of these standards are based on knowledge of past operations. Accuracy of detailed past knowledge is particularly important at this level of managerial activities, since lower-level managers may find it necessary to take on-the-spot action to rectify upcoming unfavorable situations. Essentially, the time frame for operational control relates to daily operations but also can be related to weekly, monthly, or quarterly operations.

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Relationship between Decision Support Systems and Business Intelligence Systems in Manufacturing In the past, there was great accent on decision support systems (DSSs). Essentially, a decision support system is designed to satisfy the needs of a manager at any level in a computerized environment. Such a system is designed to support the problem-finding and problem-solving decisions of the manager and incorporates features found in management information systems and in quantitative models of management science. Such a system emphasizes direct support for the manager in order to enhance the professional judgments required in making decisions, especially when the problem structures tend to be semistructured and unstructured. Emphasis is placed on helping the manager make decisions by being at the center of the decision-making process rather than on actually making decisions for the manager. This interplay results in a total effort that is greater than the manager or computer operating independently, thereby providing synergistic decision making. Also, the information is presented in a useful form rather than as a mass of all information that might be useful. Overall, decision support systems provide data and analytic models to help decision makers increase their confidence in solutions that range from structured to unstructured that include difficult-to-define variables.1 More recently, data marts and data warehouses have become an important part of decision support systems, thereby being a vital means to drive decision support capabilities to the next level. Data warehousing underpins the best decision support systems and enables companies to become more scientific, consistent, and fact-based in their decision making and improve the quality and speed of decisions. Improving a company’s decisioning capability is tantamount to increasing its competitiveness. Whether for customer management, cost control process improvement, or product profitability, decision support is essentially about harnessing the power of information and knowledge. Currently, decision support systems operate in a world of distributed object computing and extended enterprises enabled by secure, wide area networking. Going a step further, packaged operational systems are being installed to manage business functions and intranets are being implemented that are modeled on the Web. Application servers that wire desktops to applications and databases are being installed via low-cost browser clients. Additionally, modern platforms accumulate and manage unprecedented volumes of disparate operational data and provide access paths to users. But an expanded view of what constitutes decision support is emerging that ties in with business intelligence systems. Such systems exploit the popularity of the Internet’s global reach along with intranets and extranets. By tying in DSSs with business intelligence systems, a company’s decision makers are better able to handle decision making in environments that tend to be less structured. That is, a better understanding of such an environment within a BIS operating mode will help decision makers improve their final decisions.

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TAKING AN ENLARGED VIEW OF MANUFACTURING OPERATIONS At the outset, it should be noted that labor costs in manufacturing operations amount to only 10 to 15 percent of total costs, with material costs representing another 30 to 40 percent, and overhead representing 45 to 60 percent. Because overhead is typically allocated by direct labor hours, companies often try to reduce indirect labor and increase direct labor, which amounts to an accounting game, since true overhead still stays the same. In contrast, a company’s manufacturing management should be attacking queue-and-wait times, which have no labor associated with them but which affect lead time, inventory and its associated carrying cost, material handling equipment and storage, insurance, and like items. Hence, a broader perspective of manufacturing operations is needed today and tomorrow much more than in the past. Knowledge and an understanding of appropriate changes can turn a company’s manufacturing operations into a competitive weapon for attacking its current problems and for achieving profitable growth. As will seen below in the discussion on computer integrated manufacturing, enterprise resource planning, supply chain management, manufacturing execution systems, advanced planning and scheduling, and total quality management, business intelligence in manufacturing is useful for the creation and execution of current and future manufacturing activities. This relates to problem solving of current manufacturing operations, internal knowledge integration across functions and projects, innovation and experimentation to build for the future, and problem finding, where it is necessary to integrate external flow with a company’s internal flow in the manufacturing process. Within this framework, the company’s management is open to change and can learn from its past—good, bad, or indifferent. The bottom line is that the total organization, including its manufacturing operations, center around the discovery and use and understanding of information and knowledge on a daily basis. It should be noted that intelligence is not fixed and immutable. Yesterday’s brilliant insight can quickly become today’s competitive noose. For example, consider the automobile industry, where quality is still defined as the absence of manufacturing defects. In the years gone by when there was a large gap between the product quality of American and Japanese vehicles, that was certainly a valid measurement because it had a direct impact on customer satisfaction. Today, the difference in defect rates among the major automakers is somewhat indiscernible to customers. Typically, research indicates that customers feel that a good measure of vehicle quality is a balance between minimizing the things gone wrong with those attributes that customers consider things gone right.

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Expanded View of Enterprise Resource Planning Ties In with Supply-Chain Management As a starting point, it is helpful to look at the total framework that underlies all manufacturing activities in a typical company. Such a framework today and in the future centers on a broad-based view that can be found in computer integrated manufacturing (CIM) that has been updated to enterprise resource planning (ERP) and supply chain management. Originally, CIM focused on blending manufacturing with marketing, finance, accounting, personnel as well as other functional areas, where deemed necessary. It provided the levels of planning and control for manufacturing, along with the flexibility to change with the times. The basic objective of CIM was to change management’s thinking by establishing a framework within which manufacturing operations were defined, funded, managed, and coordinated. This framework required specific mechanisms for production planning, cost control, project selection and justification, project management, and project performance monitoring. Today, the role of the enterprise view is to ensure that the levels and types of integration are appropriate. It is found within an enterprise resource planning (ERP) and supply chain management (SCM) framework—an expanded view of the CIM concept. The focus of enterprise resource planning is to facilitate the exchange of manufacturing and related information and knowledge throughout a company. Software vendors usually sell it in suites containing modules such as purchasing, point of sale, manufacturing, inventory, job costing, bill of materials, payroll, and audit trail. If installed properly, typical results indicate that companies can fill and ship customer orders within 48 hours, down from several days, as well as reduce inventory by about 25 percent. In essence, ERP can speed up business processes, reduce costs, increase selling opportunities, improve quality and customer satisfaction, and measure results continuously. Currently, the major ERP vendors, Baan, J. D. Edwards, Oracle, PeopleSoft, and SAP, have developed portals as easy-to-use gateways to ERP applications and data for professionals supporting E-business operations. Several vendors have set up on-line marketplaces where businesses can purchase goods. Also, they have added supply chain and Web transaction software to provide acrossthe-board E-business capabilities. From this perspective, ERP systems can handle high-volume Web transactions and ensure that orders are fulfilled. Manufacturing companies can avoid the high integration costs associated with third-party E-business software by purchasing preconfigured applications that integrate smoothly with existing ERP systems. Related to enterprise resource planning is supply chain management (SCM). The focus is on the whole supply chain, which includes where and how the products are sourced, delivered, and merchanised to the customer. Its primary goal is to supply high-quality, low-cost products with a fast turnaround time. Although this is applicable to many environments, it focuses more on distri-

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bution environments. This integration requires a seamless link that shares information and knowledge among marketing, sales, purchasing, finance, manufacturing, distribution, and transportation. Advances in technology provide this through methods such as E-commerce, faster transportation systems, and distribution requirements planning. By achieving this kind of integration, a company can maximize its supply chain value with a lower landed cost of product from a vendor on one side of the supply chain and pass this value to the customer on the other side of the supply chain. Overall, for manufacturers, efficiency comes from integrating distributed data sources to better link segments of the supply chain. Essentially, as companies shift their focus from their internal processes (like ERP) to their external relationships (like SCM), what is emerging is a virtual enterprise encompassing both suppliers and customers, integrated through a supply network that increasingly uses E-commerce as the conduit. Effective order management is emerging as a key link in that chain, which integrates demand forecasting and planning, inventory and warehouse management, delivery and transportation, and billing. Newer order fulfillment technology maximizes customer service and on-time delivery, while optimizing a company’s assets and resources, thus providing a higher return. If a company can streamline its processes, take orders, configure those orders, build to those orders using their assets optimally, and execute in an on-time fashion, that company is going to compete more effectively. Agile manufacturing, which is the ability to respond quickly to rapidly changing markets driven by customer needs for products and services, can be included within an ERP-SCM framework. Agile companies have the ability to reconfigure operations, processes, and business relationships swiftly. Thus, the agile concept emphasizes ultraflexible production facilities; constantly shifting alliances among suppliers, producers, and customers; and direct feedback of sales data into the factories. Using state-of-the-art design and manufacturing technologies, many companies have gotten better at meeting customers’ needs, responding to changing markets, and producing higher quality products. They have reduced paper flow, prototyped products quickly, shortened cycle times, linked supply lines electronically, created virtual inventory and companies, and given customers more choices. Some results have been widely reported, such as Motorola’s customized pagers and Levi Strauss’ customer-tailored jeans. Others that have not been as widely reported include the Big Three automakers, the aerospace industry, semiconductor makers, the computer industry, and pharmaceutical companies. All have been able to speed up design, manufacturing, and customization using the agile concept to meet changing customer needs. Finally, as noted briefly above, virtual worlds can be found within an expanded view of manufacturing. More to the point, expanded virtual worlds have the potential to assist management in reengineering operations, whether manufacturing or otherwise, to improve the productivity of the company. Basically,

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reengineering focuses on doing more with less so that the productiveness of personnel and equipment is improved. For example, a proposed assembly modeling system can be tested that radically changes the way aircraft are produced. The prototype system makes use of 3-D display and input devices to simulate the assembly process. The system could save money in development costs by eliminating the need for full-scale mockups. This is a good example of the benefits of stereo viewing and desktop virtual reality in manufacturing. Expanded View of Manufacturing Execution Systems Centers on Advanced Planning and Scheduling Before setting forth the fundamentals of an advanced planning and scheduling (APS) system, it is helpful to examine the basics of a manufacturing execution system. Essentially, a manufacturing execution system (MES) can be looked upon as the CIM concept at the lower levels of the manufacturing process. It is a set of software applications that can easily integrate into everyday legacy systems and reside on most platforms. It allows a manufacturer to produce products on the factory floor more effectively by making production visible and information accessible to factory-floor personnel. It is used by operators, setup and toolroom personnel, supervisors, and foremen. An MES can provide information on the following: (1) resource scheduling/allocation and status, (2) operations/detailed scheduling, (3) dispatch production units, (4) document control, (5) data collection/acquisition, (6) labor management, (7) quality management, (8) performance analysis, (9) process management, (10) product tracking, and (11) maintenance management. Together, these systems allow improved production beyond what is possible with previous manufacturing systems such as MRP-II (manufacturing resource planning). MRP-II systems tend to focus on accounting transactions, at best keeping score for the factory floor, but not helping to dispatch production, allocate resources, or pace and otherwise manage production. In contrast, an MES is the data collection and dissemination of production/process information that truly makes it the new E-mail of the factory floor. It includes such functionalities as resource use, scheduling, quality management, and engineering change notices. Typically, an MES requires a lot of data collection points that give users a real-time view of manufacturing. More recently, MES has been expanded into an approach called advanced planning and scheduling (APS), which, in turn, is being tied into supply chain technology. Because automation has taken manufacturing companies to new heights of real-time information gathering and analysis, the problem is no longer how to acquire it but what to do with it for enhanced efficiencies and improved profitability. Basically, APS fits in between MES and ERP. MES touches the factory floor, while APS creates a bridge between MES and ERP. In addition, APS replaces the planning engines in MRP/ERP to become the strategic and tactical planning in supply chain management for everything from locating plants to accessing suppliers. It takes the real-time business process

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from the plant floor to the supply chain, letting a manufacturing company plan beyond its four walls, all in one effort. This strategic planning approach means collaboration/cooperation with trading partners. At the operational level, planning includes order management, scheduling of materials, and more—many times via the Internet. Although there is some question as to where APS ends and supply chain begins and vice versa, the two are very closely intertwined.

Focus on Total Quality Management After being viewed as a manufacturing problem in the past, quality has become a service issue—not just for service-sector businesses like communications, health care, and finance but for the service side of manufacturing companies as well. The focus is on total quality management (TQM)—that is, quality in the offering itself and in all the services that come with it. If product quality is essentially the same across the industry, service becomes the distinguishing factor. Overall, TQM has become a prerequisite for survival today and tomorrow. With TQM, the postwar quality movement has moved into its third stage. When the growing popularity of Japanese automobiles, televisions, and radios forced U.S. manufacturers to take another look at themselves, most companies were still in what quality experts call the first or inspection phase, relying on sampling techniques to get rid of defective items. Too often, however, they did not. In 1980—the year an NBC White Paper introduced audiences to W. Edwards Deming, the American statistician who had shown the Japanese how to use process controls to catch defects at the source—manufacturers who took the issue seriously started moving into the second or quality control phase. Now, with TQM, quality is no longer solely in the quality control department, since it is sponsored by top management and diffused throughout the company. Essentially, employees see a gap between what the company says is important in regard to quality and the company’s follow through. Needless to say, this is less than a rousing vote of confidence in the quality performance of American businesses. Employees want to see better results. In past surveys, substantial proportions of survey respondents said that the quality programs in their companies have had either no effect or a negative effect in specific areas. Workers stated that the two most important ways the company can make it easier for them to do high-quality work are, first, to provide more training in job skills and, second, to offer job security. In addition to these two items, workers would like to see their companies do the following (in order of importance): have a more supportive attitude from top management, train workers in interpersonal working skills, respond faster to employee ideas, offer more up-to-date tools and technology, have a more supportive attitude from middle management, and offer better access to available information.

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Figure 9.1 Manufacturing Principles with Emphasis on Employing Knowledge

Underlying Manufacturing Principles with Emphasis on Employing Knowledge Essentially, the manufacturing principles set forth in Figure 9.1 are based on knowledge of the total supply chain, which encompasses customers, suppliers, manufacturers, wholesalers, and retailers and their interaction among themselves. As a beginning point, in order for the total supply chain to be turned into a competitive weapon, product designers must be teamed with customers as well

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as their own marketing and manufacturing operations. Typically, a company finds it necessary to tear apart and rebuild its approach to product design. This means that designers spend time getting at all the facts involving a proposed new product using such electronic means as groupware. A business team would normally consist of a representative each from marketing, finance, and manufacturing and quality control. The flow of intelligence begins with customer requirements so that the team works on a conceptual design and then goes back and forth with the customer to formulate a detailed design. In turn, the business team designs, manufactures, markets, and sells the product. For instance, say the team wants to design a new vacuum cleaner. After consulting with its representative customers and its retailers, marketing knows just how it wants the product to look (i.e., what shape it should take and how large it should be). The engineer is concerned about the product’s size and shape, not for appearances’ sake, but because the casing needs to house the inside circuitry that makes the product run. After the design process is complete, the computerized system also generates a bill of materials that will eventually feed automatically into the company’s manufacturing resource planning system. Based on this newer design process, an important starting manufacturing principle is that all appropriate personnel within and outside the company participate and are involved in product development. In terms of utilizing a company’s current manufacturing knowledge and resulting intelligence in a productive manner, consider the example of an engineering firm that found itself under pressure as clients began demanding fixed-price contracts instead of traditional cost-plus arrangements. The firm needed to ensure more predictability and efficiency in its design and construction projects. It was clear that project teams working independently were not benefiting from one another’s experience. A group of executives, including the information systems manager, implemented a management process that would allow project teams access to the relevant operational intelligence from inside and outside the organization. The net result was that the company’s management process was highly effective because it focused on key decision points, thereby making the desired resultant operational intelligence easily accessible through an enterprise computing and communications infrastructure at the time that it is needed. MANUFACTURING FACTORS THAT ARE RELATED TO EFFECTIVE OPERATIONAL INTELLIGENCE Important manufacturing factors related to effective operational intelligence are generally complex because there is the need to tie in manufacturing activities to other functional areas of the organization. To produce meaningful and operational information and knowledge for planning and executing manufacturing operations, changes must be made to the decision processes. In addition, newer manufacturing and related software, which allow manufacturing managers to

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control their activities on a now basis, need to be introduced to assist them and their support personnel in performing their daily tasks. The section that follows discusses important factors useful in assisting manufacturing managers and their staffs, including the following: (1) the Internet, which provides for customer creativity that redefines manufacturing, (2) reengineering and E-engineering to improve productivity, (3) utilization of a learning organization in a manufacturing environment, and (4) manufacturing software useful for operational intelligence. Like strategic planning and marketing within a BIS operating mode, a problem-finding approach provides manufacturing decision makers with a broad view of production planning and a wide span of control over manufacturing activities. In an environment of rapid change, this is accomplished by a forward integration of information and knowledge for a better understanding of manufacturing operations. External environmental factors must be integrated with forecasting information and knowledge, which feeds the material requirements plan—the output of which goes into the purchasing and production planning and execution systems. Appropriate information and knowledge is captured at all points that provide input for inventory, purchasing, and payroll. Although much of the need for operational intelligence in manufacturing comes from increased integration, there is also a need for effective management of the production processes. More effective material acquisition policies and better production planning demand more accurate and timely information and knowledge to support their operations. Overall, the integration of manufacturing with other related systems centers on the need for improved operational intelligence for effective day-to-day operations. For a manufacturing-oriented organization, the integration process for operational intelligence starts with a five-year strategic plan. More specifically, in terms of the manufacturing area, it relates to product-line planning, facilities planning, and personnel planning as developed by the corporate planning staff for top management. Next, these manufacture-oriented long-range plans are translated into medium-range plans and finally into short-range plans as the current-year plans. In effect, the long-term strategic plan that locates future marketing problems and solves them in terms of future opportunities is eventually refined into integrated plans for this year’s manufacturing operations. From this view, it is easy to understand why timely planning and control of manufacturing resources through problem finding and the resulting integration can be a vital factor in the long-term success of an organization. To assist in the integration process for effective problem finding and problem solving, the manufacturing and execution system utilizes appropriate manufacturing software, including mathematical and statistical models, along with the operational intelligence developed to date to assist in planning and control of its resources. Some of the operational intelligence centers around the manufacturing principles, such as those set forth earlier in the chapter. It is a logical approach for overcoming common management problems encountered in man-

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ufacturing operations, such as the misuse of available productive capacity. Techniques to plan and control manufacturing resources are readily available, but only a few manufacturers have learned to use this combination of the Internet, computer networking, computer models, company personnel, and appropriate operating intelligence effectively. Those manufacturers that have will be able to utilize their productive capacity in a world of persistent uncertainty and growing complexity. Internet Provides for Customer Creativity That Redefines Manufacturing The Internet has given birth to a new enterprise-wide “world order” that focuses on customer demand which, in turn, specifies what a manufacturer will produce. One of the first ERP companies to recognize this was Symix Systems (Columbus, Ohio), with a program called Customer Synchronized Resource Planning (CSRP). In reality, CSRP is a customized value creation approach (i.e., delivery of products and services that match the value definition of each unique customer served versus serving the homogeneous requirements of a generalized market). For manufacturers to thrive, they must develop newer business practices that allow them to identify individual customer needs and expectations and respond with products and services that represent unique value for each customer. Although it may be years before manufacturing is 100 percent customer centric, customer centricity represents a real shift in the established order of doing business. Inasmuch as competition is rampant and change orders are the norm of the day, manufacturing plans have to be revised quite frequently to meet customer specifications and short lead times. It is the customer’s choice and convenience that is dictating fundamentals to manufacturing, including what goods are produced and how those goods are marketed, priced, distributed, and serviced. Within this newer operating mode, the Internet is the instrument of the many changes taking place in the manufacturing environment. However, the Internet or, more specifically, Web technology, is only part of the story. This newer approach is about everything that happens after the order is placed. The focus is on order creation as customer specifications become more important and product configuration, flexible pricing models, and remote order management take over. In essence, the customer is taking over as chief architect of a manufacturer’s future product development. Fortunately, advanced planning and scheduling (APS) tools have arrived to make customer centricity feasible. APS applications let manufacturers dynamically schedule and reschedule their production on demand. An important caveat here is that manufacturers must know how “fresh” their input is, which means they have to know how current the information is upon which they are acting. Getting better control of the data that is stored out there on servers and using

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Web technology as the delivery mechanism is essential. This customer-driven manufacturing/marketing trend is creating an ever-growing need for information and knowledge up and down the supply chain. In order to outdeliver and outinvent the competition, companies have to create information about customer preference and turn that into a rapid new product development/configuration. In other words, customer-centric manufacturing requires dynamic business intelligence, making such intelligence a necessity for effective everyday operations.2 From Reengineering to E-Engineering to Improve Productivity In the recent past, reengineering was an important direction for business because the most successful and promising companies developed new techniques that allowed them to survive in an increasingly competitive climate. To reengineer (i.e., reinvent their companies), managers need to abandon past organizational and operational principles and procedures and create entirely new ones. No matter what industry companies are in, how technologically sophisticated their products or services are, or what their national origin is, basically they trace their work styles and organizational roots back to the prototypical pin factory that economist Adam Smith described in The Wealth of Nations (published in 1776). Smith’s principle of the division of labor embodied his observation that some number of specialists, each performing a single step in the manufacture of a pin, could make far more pins in a day than the same number of generalists, each engaged in making whole pins. Today’s airlines, car manufacturers, accounting firms, and computer manufacturers, to name a few, have all been built around Smith’s central idea. Typically, the larger the organization, the more specialized is the work and the more separate the steps into which work is divided, whether it’s a manufacturing or non-manufacturing firm. Business reengineering means starting from scratch. That is, it centers on forgetting how work was done in the past and deciding how it can best be done now. Old job titles and old organizational arrangements—divisions, departments, groups, and so forth—cease to matter. What matters, however, is how work is organized, given the demands of today’s markets and the power of today’s technologies. At the center of business reengineering is the concept of discontinuous thinking (i.e., identifying and abandoning the outdated rules and fundamental assumptions that underlie current business operations). Every company is replete with implicit rules of the past, such as that local warehouses are necessary for good customer service or local marketing decisions are made at headquarters. These rules are based on assumptions about technology, people, and organizational goals that no longer hold. To reengineer a company’s business procedures, it is necessary to rethink these procedures and redesign the business processes to achieve improvements so that the company’s employees go from being specialists to generalists in order to improve productivity. A business process is a collection of activities that takes one or more kinds of input and creates an output that is of value to

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the customer. For example, consider the filling of orders, in which the delivery of finished products is the value that the process creates. However, Adam Smith’s notion of breaking work into its simplest tasks and assigning each of these to a specialist tends to lose sight of the larger objective, which is to get the goods into the hands of the customer who ordered them. E-engineering goes beyond reengineering to improve productivity. It utilizes effective E-commerce as its principal means for improving productivity. During the reengineering craze, the resulting increase in productivity showed up first in the high-tech sectors, such as computers and chip manufacturers, and is still coursing through the economy. The Internet and the World Wide Web are inspiring new enthusiasm for computer technology investments. E-engineering has the potential to keep growth at a high rate. For example, the Ford Motor Company has the potential to reap big rewards from E-engineering. Just as Henry Ford’s assembly line created a revolution, the Internet is once again transforming manufacturing operations for Ford, revolutionizing how it thinks about its business and the way it conducts business. For much of the last century, it was hard not to refer to the Ford Motor Company and the Industrial Age in the same breath. The company that practically invented mass production sported such 20th-century features as assembly lines, labor unions, sprawling supplier networks, and a vast dealer universe. However, currently in this 21st century the company plans to set up a massive on-line bazaar, called AutoXchange, for all the goods and services it buys, whether they are paper clips, stamping presses, or finished components. Over the next three to five years, Ford hopes to improve productivity and save billions in the process by replacing an elaborate network of personal contracts and triplicate forms with a global electronic forum where deals can be done almost instantly. General Motors and Daimler-Chrysler have similar plans. The end point is that the auto industry will be linking up a large part of the world’s economy with the goal of cutting spending on parts alone by 10 percent. But other old-line companies from the Royal Dutch/Shell Group to Honeywell International to General Electric are pushing just as hard to get aboard the Net-driven productivity revolution. At General Electric Power Systems, customers and designers are using project collaboration technology to help construct a power plant from the ground up on the Web. They can hold virtual meetings in which blueprints can be exchanged and manipulated in real time. Then customers can use the Web to watch from anywhere in the world as a turbine is built and moves down the production line, ordering last-minute changes as needed. Since the turbines cost an average of $35 million each and contain about 13,000 parts, catching problems as well as errors early is priceless. And after the turbine is delivered, a new Net-powered system called the Turbine Optimizer lets both customers and GE compare the performance of the turbines with other GE turbines around the world. While GE’s new systems should give the company a 20 percent to 30 percent reduction in the time it takes to build a turbine and could improve the annual output of each by 1 percent to 2 percent, that is just the beginning.

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As with reengineering, adapting to the Net-based model requires some important changes. The biggest investments will not be in technology but in reshaping companies to move at Net speed. A number of jobs in such fields as purchasing will be eliminated while skill shortages may keep many companies from cashing in on the payoffs of E-engineering. The hardest part of all may be creating collaboration between a company and its new partners. It should be noted that Internet productivity gains are not limited to large companies. These gains are trickling down to even the smallest companies. The bottom line is that the Internet and the World Wide Web have gone from a threat to an unlimited opportunity for everyday manufacturing operations.3 Utilization of a Learning Organization in a Manufacturing Environment Going beyond reengineering and E-engineering to better manage production processes, it is prudent for manufacturing firms to take into account the concept of the learning organization. In the past, the focus has been on the control organization method in a manufacturing environment. The control organization method centered on structured manufacturing operations in which most contingencies were anticipated. In contrast, the learning organization method is suitable when problem recognition, definition, and solution are likely to differ for most situations. From this perspective, there is the need for the manufacturing organization to learn over time to adapt to these changing situations. A learning organization method centers on organization personnel expanding their capacity to create the results they truly desire over time. New and expansive patterns of thinking are encouraged, and employees are continually learning how to learn and work together better. Essentially, a learning organization method centers on knowing what the organization and organization members do well, learning from that to do it better the next time, and continually looking for improvement. The net result is that a learning organization is able to maintain a competitive advantage in a fast-changing world. Because the process for implementing the learning organization method differs markedly from company to company, it is possible to interrupt a factory’s current system by introducing new equipment, new learning skills and activities, new knowledge-creating or operational intelligence management systems, or new values. In reality, interrupting a current manufacturing system is not the only factor. There is the need to look at interrelated learning skills, management procedures, and values. Values unsupported by appropriate manufacturing systems are a lost cause. That is, management systems that run counter to values are likely to be sabotaged. Similarly, learning activities unsupported by values and management practices tend to be short-lived. If a learning capability is to be developed, the whole manufacturing system must eventually be addressed. The essential message here is that the proper implementation of a learning organization in a manufacturing environment can make or break it over time.4

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Manufacturing Software Useful for Operational Intelligence Today, the primary goal of manufacturing software centers around improved customer service: getting the right product to the right place at the right time and at the right price. Because traditional MRP (material requirements planning) and MRP-II (manufacturing resource planning) systems, even in client/server environments, were found to be too rigid for many users, developers have reslanted their products to newer manufacturing models, in particular, enterprise resource planning (ERP). MRP-II systems of the past were host-based, proprietary, and hard to modify, whereas the ERP systems of today are open systems, based on RDBMS software in 4GL client/server environments. As indicated previously in the chapter, enterprise resource planning is real-time manufacturing software that incorporates the entire supply chain from sales and order to production, inventory, and distribution. Since companies are implementing client/server systems that manage the entire supply chain (from the time a product order comes in to the moment it is delivered), ERP systems represent an integrated set of applications modules that ties together a host of functions. As such, they are highly integrated application suites that support many processes, including sales forecasting, order management, purchasing, production scheduling, inventory management, distribution scheduling, engineering, maintenance, and accounting. These systems become the nerve center of a manufacturer. Overall, enterprise resource planning software from a wide range of vendors has the capability to provide competitive advantage through lower costs and faster response time. Typical ERP vendors are: Baan (Barneveld, the Netherlands), JBA International (Studley, United Kingdom), J. D. Edwards (Denver, Colorado), Oracle (Redwood Shores, California), PeopleSoft (Pleasanton, California), and SAP (Waldorf, Germany). Currently, with the integration of back-end ERP systems with supply chain suites, the above leading ERP vendors are challenging the positions of traditional supply chain vendors, such as Manugistics and i2 Technologies. Although manufacturing software, such as ERP, is a broad-based approach to manufacturing, manufacturing execution system (MES) applications guide manufacturing processes through each step and collect information about what was done to what by whom on which machine. Detailed instructions and captured shop-floor data allow other applications to analyze what has already happened and what needs to happen next. Typical software vendors include those mentioned above plus others, including Business Computer Connections (MES9000), CimVision (MES Factory Manager), and RWT Corporation (On Track MES). In the area of advanced planning and scheduling (APS), there are a number of vendors, including: Chesapeake Decision Sciences (New Providence, New Jersey), i2 Technologies (Irving, Texas), Manugistics (Rockville, Maryland), PeopleSoft/Red Pepper (Pleasanton, California), and Think Systems/ i2 Technologies (Parsippany, New Jersey). Typically, it is necessary to take the output of these manufacturing models

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and go a step further in terms of discovering operational intelligence that can assist manufacturing decision makers at all levels of a typical company. One approach is to utilize business intelligence tools that massage typical manufacturing outputs so that plant managers have a better understanding of their operations from the short term to the long term. For example, the Brio Enterprise Suite from Brio Technology (Palo Alto, California) consists of BrioQuery Designer, BrioQuery Explorer, BrioInsight, Broadcast Server and OnDemand Serve. This suite provides access to intelligence collected from various supply chain elements. Users can access appropriate information from these points using preformatted documents/reports as well as ad hoc/undefined user queries delivered via desktop, E-mail, or intranet. Having information electronically available, manufacturing management and others are offered a rich analytical environment in which to assess the business by products manufactured, channels of distribution, and other intelligence reports. In terms of the World Wide Web for business intelligence tools, the convergence of decision support and the Internet has created an opportunity for organizations to deliver E-business intelligence (i.e., the sharing of intelligence over the Web with customers, partners, and suppliers). By using WebIntelligence 2.0 from Business Objects (San Jose, California), for example, manufacturing and other reports that took months to produce can now be generated on a daily basis using a Web browser. WebIntelligence 2.0 is a helpful Web decision support tool for delivering advanced extranet features for deploying E-business intelligence applications. This business intelligence package offers features for extranet deployments, an OLAP module for drilling in charts and tables, and numerous new options for report creation and distribution. Reference can be made to Chapter 4 for other business intelligence tools that are at home in a BIS operating mode for manufacturing. Another approach is to employ data mining tools in order to extract manufacturing patterns, trends, and rules. Not only are they found in the areas of corporate planning and marketing, they are also useful for discovering manufacturing knowledge. For example, the software from Angoss Software— namely, KnowledgeSEEKER—is used extensively by Hewlett Packard in their United States manufacturing plants as a process control tool to analyze factors impacting product quality as well as to generate rules for production control systems. KnowledgeSEEKER has been used to trouble shoot quality-assurance problems in the production process for the HP color scanner. It identified the critical factors, several of which had been completely unsuspected. This information allowed the engineers to correct the problem quickly. In another case, KnowledgeSEEKER derived the rules necessary to identify situations where a manufacturing process was about to go out of control. This allowed intervening actions to be taken that prevented the problems from every arising.5 Still another approach that is useful in discovering manufacturing knowledge is the utilization of neural networks. Take the example of a farm tractor manufacturer that uses neural networking software to run a machine that produces

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special sand for making cast-iron molds. The idea is that the software ensures that the sand comes out just right. Neural software can be thought of as a sophisticated note taker. As sensory devices constantly measure variables, such as temperature and moisture, the software stores the data and uses it to build a memory. The software retains the experiences that are constantly being repeated and relegates to the background those that happen only rarely. It uses this ability to fine-tune the machine instantly. The software can even predict based on experience, when something is about to go awry, so that it can correct a problem before it occurs. In effect, neural networks learn and reason in a manner comparable to a person’s learning process. From another perspective, information can be the basis for developing knowledge that is service related for manufactured goods. For example, General Motors’ Computer Aided Maintenance System (CAMS) was designed as a tutor to help novice mechanics diagnose problems and repair cars. However, it has evolved into an even more sophisticated system that allows expert mechanics to refine their skills. In the past, a mechanic who had absorbed 500 pages of repair manuals could fix most cars. Today, that same mechanic would need to have read about 500,000 pages of manuals. However, access to CAMS makes today’s mechanics smarter without manuals. While increased knowledge resides in the system and not in any particular person, the fact is that many mechanics are smarter because they now have the experience of all the other mechanics. Because the system is continually improving by learning new techniques from the best mechanics, the net result is better service for the customer. In terms of the production process, mainstream manufacturers are finding that simulating, not just automating, factory-floor operations is an effective way to boost productivity. Essentially, simulation software provides better insights because it can juggle many more aspects of a manufacturing problem than a person can. It can incorporate all kinds of production data into its model, including setup time, downtime characteristics, dropout, and manufacturing process time. Also, simulation software takes into account the dynamics of time-based material flow. Perhaps the biggest impact of the simulation software for a typical company is in how it helps engineers improve the routing of components through the production process. The simulation program is able to evaluate the effect of far more sophisticated routing schemes than a person can. Intelligence can be built into the simulation model so that it can, for example, understand that a part can be processed by stations 1, 2, and 3 in any order depending on which machines are free. The net result is that a wide range of routing options can be evaluated and the optimum routing logic for the plant can be determined. This routing logic can then be built into machine controllers or, for manual stations, provided as instructions to plant supervisors. Overall, simulation provides a tremendous advantage over other tools, such as spreadsheets, by calculating thousands of different scenarios. It can store each result in a database and, in many cases, allow subsequent calculations to be

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based on the previous outcome. When combined with a well-defined process model, this allows the discovery of possible trends and potential process bottlenecks. Typical simulation software packages include AutoMod (AutoSimulations, Bountiful, Utah), QUEST (Deneb Robotics, Troy, Michigan), FactoryCAD (Engineering Automation, Ames, Iowa), WITNESS (Lanner Group, Houston, Texas), PS-Engine (Prosolvia, Troy, Michigan), and SIMPLE⫹⫹ (Tecnomatix Technologies, Novi, Michigan). Going one step further, software providers have taken steps to create a complete “digital factory” solution that enables a manufacturer to envision the manufacturing process in 3-D—from design and assembly to product shipping. Dassault Systems, for example, acquired Deneb Robotics to round out its digital manufacturing process system (DMAPS), while Tecnomatix Technologies absorbed AESOP’s visualization product line into its RobCAD software and computer-aided production engineering (CAPE) solutions. Meanwhile, Engineering Animation (EAI) added Cimtechnologies FactoryCAD planning and management software to its visualization offerings. By using a 3-D rather than a 2-D approach to modeling key manufacturing equipment and processes— including 3-D models of the parts to be assembled, 3-D simulations of the production process and assembly line, and 3-D simulations of the work cells and robots—it is now possible to construct a complete 3-D simulation of the factory in operation. This integrated approach to simulation enables manufacturing decision makers to analyze and streamline existing manufacturing processes, to prepare for retooling more effectively, and to save time by facilitating reuse of valuable manufacturing data and models to jump start future projects. MANUFACTURING FUNCTIONS THAT LEND THEMSELVES TO OPERATIONAL INTELLIGENCE Common manufacturing applications related to business intelligence systems include purchasing and supply chain management plus production planning and execution. These are set forth below. Other manufacturing functions related to a BIS operating mode encompass engineering, manufacturing analysis, quality control, and inventory control. More specifically, engineering efforts prior to daily manufacturing operations are a natural, since management can analyze the ins and outs of engineering projects over time and determine appropriate manufacturing rules of thumb that can be applied to future engineering projects. Similarly, a BIS operating mode can assist manufacturing managers in analyzing manufacturing operations and quality control problems as well as those related to inventories, from the raw materials stage to the finished goods stage. Because in some industries, such as apparel and high technology, market demands change every quarter or two, no manufacturer wants to have a warehouse full of obsolete products. In general, business intelligence systems are useful to most manufacturing managers, not only for overseeing and evaluating factory operations but also in the physical distribution activities that start with incoming materials from

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suppliers and end with those activities that take place after the manufacturing process involving wholesalers and retailers.

PURCHASING AND SUPPLY CHAIN MANAGEMENT Typically, the major purchasing functions of a manufacturing-oriented company consist of buying, maintenance, and follow-up. In these fast-changing times, there is the need to go a step further to get a broader perspective of these functions by using a supply chain approach. This can be accomplished when manufacturing managers oversee and understand purchasing operations better by extending their activities to a supply chain approach. After giving consideration to developing an overall purchasing and supply chain strategy, the purchasing areas to be investigated include vendor and buyer evaluation as well as purchased materials and parts evaluation. In addition, product improvement using value analysis is discussed. Fundamentally, all of these areas are explored within a BIS operating mode. A newer direction in purchasing centers around reengineering and Eengineering in terms of the relationships between the purchasing department and its internal customers, its outside suppliers, and the computer department. Such an approach offers many companies the opportunity to broaden and satisfy their relationships with suppliers and to understand their own internal relationships better. Once they dissect their own business practices, many companies find that purchasing is not an island unto itself, but rather a complex web of interactions with other manufacturing activities and outside sources. From this broadened perspective, there is a movement toward improved productivity not only for purchasing but also for others involved in the total procurement process. In addition, the focus today of the purchasing and supply chain function is on-line procurement using the Internet. Purchasing agents can order appropriate materials and services from an on-line catalog, using only a Web browser. The process of purchasing can be simplified for procurement of the day-to-day items necessary to meet their company’s manufacturing needs. Going a step further, a combined forecasting and ordering approach on the Internet can be useful to a typical manufacturer. That is, a simultaneous collaboration via the Internet between manufacturing, warehousing, finance, logistics, sales, and marketing enable a manufacturer to synchronize purchasing, production, and distribution to customer demand without the platform incompatibility and time-lag difficulties that might otherwise hamper collaborative activities. The advantage the Web offers is that it enables much more collaboration to take place between a corporate headquarters and its manufacturing plants. Without the Internet and the World Wide Web, a sophisticated distributed client/server system is required with software at each location that is tied in with corporate headquarters. That is cost prohibitive and much more difficult to handle.

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Development of an Overall Purchasing and Supply Chain Strategy To better understand the purchasing function that includes a supply chain approach, manufacturing managers, working with their purchasing counterparts, can evaluate critical purchasing areas using tools such as multidimensional analysis, knowledge discovery, and business intelligence. These include assessment of outsourcing among multiple vendors, analysis of purchasing under uncertainty, analysis of availability of raw materials and parts to meet present and future growth, and the capability of obtaining raw materials and parts for new products being developed. In terms of day-to-day operations, critical purchasing areas that also can be evaluated include the measurement of idle machines and/ or personnel resulting from a lack of purchased supplies, the measurement of the extent of successful substitutes of materials and parts, the ratio of rejected purchases to total purchases, and the savings on discounts and quantity purchases. Attention to the foregoing areas provides purchasing and manufacturing managers with the capability to have control over their operations as times change. Going beyond these analyses, which are very useful to purchasing and manufacturing management, a supply chain approach extends a manufacturer’s capability to work with its customers and suppliers. Companies that use the Internet and other computer technology to swap information, knowledge, and new ideas routinely with suppliers create an environment for joint achievement. By sharing such items and their resulting analyses about customer demand, product defect rates, and engineering changes, not only can manufacturing cycle times and inventory levels be reduced but also better products can be built. Leading-edge supply chains can build mutual trust and loyalty. Because a company’s suppliers are also its customers, the ability to serve them well results in great benefits for both. Daimler-Chrysler, for example, is currently concentrating less on purchasing and more on managing the supply chain and managing the flow of material from the chain into its plants. Most of its current savings have come from this approach and most future savings will come from it, too. At Daimler-Chrysler, those who will supply parts for its upcoming minivan line through 2003 or 2004 are already on line. Daimler-Chrysler can advise its suppliers of the major changes it wants to make in the supply base, set up the sourcing, and presource it—without even having conceptualized the vehicle. Thus, Daimler-Chrysler has a futuristic purchasing supply chain strategy in place for supplying its future manufacturing activities. Building upon this tie-in of purchasing activities with its supply chain via the Internet is the realization that the purchasing of goods and services on the Internet is faster, more efficient, and more cost effective in a supply chain approach for manufacturers as well as non-manufacturers. The Internet provides

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organizations with complete line-item detail on each transaction, giving users real-time access to critical product information and knowledge. It reduces the effort put into basic transactions by adopting simplified procedures and implementing best practices. In terms of information and knowledge, the purchasing and supply chain structure is linked with finance to ensure that there is an easy flow between these functions. In a similar manner, this Internet approach along with intranets and extranets allows companies to analyze their operations better by discovering new knowledge about themselves and their customers. Using one of the data mining tools (set forth elsewhere in the text), they can discover current trends and possibly discern upcoming trends, patterns, and relationships that lie hidden in data warehouses (extracted from the Internet, intranets, and extranets). In addition to utilizing information and knowledge from these sources to ensure that the best opportunities in terms of products and services are achieved, the discovery of new knowledge and a better understanding of manufacturing operations can ensure that the best overall purchasing and supply chain strategy is being pursued. After all, a most important part of this strategy is developing the right kind of procurement process, understanding this procurement process, and making sure it pays for itself. As times change and new purchasing intelligence is developed, this combined purchasing and supply chain approach may need to be changed as well. Understanding Vendor, Buyer, and Purchased Parts Performance Ensuring that purchasing management has the capability to evaluate its vendors’ and buyers’ performance is related to a typical company’s reengineering and E-engineering efforts. For example, a typical monthly vendor performance report is an evaluation of outside vendors who have supplied raw materials, supplies, and parts to a typical manufacturing-oriented company. A comparison of the total amount purchased last month and this month gives an indication as to whether buyers have been shifting business to and from certain vendors. In order to make this comparison, it is helpful to employ purchase performance models that center on evaluating price, quality, and delivery, along with a purchase performance index. To measure the price variable, past costs provide the best available standard for the present. Current costs that are considerably above those of the previous period signal that purchasing might be performing poorly. Of course, it is possible that an increase in price is due to a general rise in prices in the economy. Since price is not the sole determinant of a “good buy,” a buyer must also consider the quality of goods it needs before it is possible to evaluate whether it really did get a good buy. No matter what procedure is employed, a measure of the proportion of deliveries that actually are accepted is useful for measuring the effectiveness of purchasing. Delivery, like poor-quality materials and pur-

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chased parts, can negate a seemingly good buy. At their worst, late deliveries may result in closing down a production line or even in the loss of future sales. From this viewpoint, it is easy to visualize why a measure of how well vendors meet their specified delivery dates is essential for evaluation purposes. The aggregate of the price, quality, and delivery indices is a purchase performance index, sometimes called PPI. The purchase performance index (a single value) summarizes the actual performance against expected performance, stated on a quarterly basis or some other time period. It is a composite index, requiring some kind of averaging or weighting process in order to combine these three indices into one. The weights depend on purchasing management’s judgment about the relative importance of each index factor. Common weighting factors assign a weight of 50 to the price index, and 25 each to the quality and delivery indices. In the following example, the purchase performance index, based on the foregoing weighting factors, is 105; its price, quality, and delivery indices are 94, 107, and 125, respectively. Index

Index Value

Weight

Index Value ⫻ Weight

Price

94

50

4,700

Quality

107

25

2,675

Delivery

125

25

3,125

100

10,500

PPI ⫽ 10,500/100 ⫽ 105

The above PPI, which is over 100, represents an improvement over the prior period. A value of less than 100 indicates just the reverse. Normally, it would be expected that vendors with indices below 100 (expected PPI is 100) would be used less often than they had been in the past. Just as vendors can be evaluated, so can the company’s buyers. From a purchasing manager’s viewpoint, monthly buyer-performance reports have great meaning. Purchasing managers generally have little direct control over vendors, but they exercise considerable control over their buyers. By having buyers evaluated on a comparable basis, purchasing managers can pinpoint the weaknesses of their buying staffs. Those buyers who are price-minded at the expense of quality materials and prompt delivery will be highlighted. The monthly buyerperformance report can be refined for more detailed analysis. Specifically, detailed analyses on price, quality, and delivery can be made for buyers (this is also true for vendors). In this example of the evaluation of specific purchasing agents, the weights are 50 percent for price, 25 percent for quality, and 25 percent for delivery, where the index values for monthly performance of five buyers are shown in Figure 9.2(a). Based on these data, the graph that compares the five buyers for this month is shown in Figure 9.2(b).

Figure 9.2 (a) Index Values for Monthly Performance of Five Buyers and (b) Graph That Compares the Five Buyers for This Month

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Going one step further, the ranking of the company’s five buyers in terms of PPI for this month is shown as follows:

A pie chart comparison of the company’s best buyer (J. Cosgrove) and worst buyer (J. Hansen) this month in terms of PPI is shown in Figure 9.3. The relatively low purchase performance index value for J. Hansen stands out when comparison is made to the other buyers. Typically, a relatively high value for the price index and the low value for quality and delivery indexes are indicative of a buyer who is price minded at the expense of quality and delivery of incoming purchased goods. For a typical manufacturing company, a low index value for quality may indicate that the company must spend some time in cleaning up the materials for production and/or spending additional time for machining before the manufacturing process can begin. Just as vendors and buyers can be evaluated, so can purchased materials and parts. For example, a monthly purchased materials and parts performance report can be prepared for purchasing executives that centers on whether or not the corporation is receiving value for parts purchased. If the price index is below 100, this might indicate that prices are rising, and consideration might be given to replacing this purchased raw material or part with another. The quality index might also be important if certain finished goods are critical for the maintenance of the company’s reputation, especially if difficulty has been experienced in the past. In a similar manner, the delivery index might be critical in terms of meeting final customer shipment dates. The monthly purchased materials and parts performance report gives purchasing executives an overview of what the buyers are procuring and how effective they are. Equally important, it provides pertinent information for the company’s buyers. A longer-term view of purchased materials and parts within a BIS operating mode provides a means to better link segments of the supply chain. Manufacturers are coming to realize that their businesses are becoming increasingly demand-driven. They are “pulling” products to consumers rather than “pushing” a set amount of products onto a store shelf or to a dealer and then trying to sell it. However, a complex chain of communications, which includes multiple sets of data from all participants in the supply chain, must be integrated if the “pull” approach is to succeed. This new focus on demand has led manufacturers to reexamine their business process with their suppliers and customers. Manufacturing firms are now looking at the demand side to synchronize their operations

Figure 9.3 A Pie Chart Comparison for a Company’s Best and Worst Buyers for This Month in Terms of Purchase Performance Index (PPI)

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profitably and efficiently. As indicated earlier in the chapter under the subject of E-engineering, there is a need to view customers and suppliers as part of a virtual enterprise. This timely and accurate movement of product based on the synchronized flow of information and knowledge, which has been labeled “synchronized customer response,” is also known as “quick response.” Overall, manufacturers have come to the conclusion that efficiency comes from integrating distributed intelligence sources in the various segments of the supply chain.

Product Improvement Using Value Analysis To assist a company in designing products that are profitable, value analysis or value engineering is needed. This approach requires that the engineer adopt a broader point of view and consider whether the parts contained in the finished product perform their required functions as efficiently and as inexpensively as possible. The appraisal focuses on the function that the part—or the larger assembly containing the part—performs. In an inspection-oriented plant, for example, more than half of all workers are somehow involved in finding and reworking rejects. The total investment in this process can account for 20 to 40 percent of production costs, and in extreme cases, 50 percent. In contrast, the Japanese inspect a product before it is made (i.e., in the design stage) and engineer the manufacturing process to be stable and reliable. In an approach to product improvement using value analysis, the product is dismantled and each part is mounted adjacent to its mating part on a table. The point is to demonstrate visually the functional relationships of the various parts. Each component is studied as it relates to the performance of the complete unit, rather than as an isolated element. A value-analysis checklist contains literally hundreds of questions and key ideas for reducing overall costs as well as maintaining the same level of product performance. Typical questions, based on knowledge of past effective design principles, are set forth as follows: • Can the part be eliminated? • If the part is not standard, can a standard part be used? • If it is a standard part, does it complement the finished product or is it a misfit? • Can the weight be reduced with lower-priced materials? • Are closer tolerances specified than are necessary? • Is unnecessary machining performed on the item? • Are unnecessary finishes required? • Can one produce the part less expensively in the plant or should one buy from the outside? • Is the product properly classified for shipping purposes to obtain lowest transportation costs?

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• Can the cost of packaging be reduced? • Do certain parts, such as precision motors and parts, exceed the expected life of the finished product?

When using value engineering to appraise overall costs, possibilities for making component-part design simplifications are frequently more apparent than is possible under the conventional design conditions. This in no way reflects unfavorably on the work done by the design engineer; the discovery of such potential improvements is the result of an analysis with a substantially broader orientation than that possessed by the original designer. A value analysis study undertaken by a typical company utilizes the background and skills of several people because it is not possible to find the multiplicity of skills and experiences of that group in the person of a single designer. Resulting design changes often permit the substitution of standardized production operations for more expensive operations requiring special setup work. In other cases, an entirely different material or production process turns out to be more efficient than the one originally specified. In the final analysis, value analysis, where the focus is on product improvement, contributes to the profitability of new products for a typical manufacturing-oriented company. PRODUCTION PLANNING AND EXECUTION AND TOTAL QUALITY MANAGEMENT Since employing manufacturing intelligence can be an effective way to assist in managing a factory, it behooves manufacturing management to utilize this resource in production planning and execution on the production floor as well as in effective control over quality (i.e., TQM). A manufacturing knowledge base, which generally consists of various elements, such as forecasts, heuristics (rules of thumb), principles, strategies, standards, facts, and procedures as well as fuzzy knowledge and modeling knowledge, can be used to assist manufacturing managers and their staffs in knowledge discovery, knowledge inference, and knowledge explanation. Equally important is available information from computer integrated manufacturing, enterprise resource planning, and supply chain systems, which can be combined with current manufacturing knowledge to provide a better understanding of manufacturing operations. Such an approach is discussed below for production planning and execution and total quality management. Because information and knowledge are the backbone of manufacturing intelligence, it behooves manufacturing management to recognize this important fact as the key to improved customer service and competitiveness as well as lower costs. The production planning and execution department for a typical manufacturing-oriented company is responsible for all physical movements between manufacturing departments and within their respective work centers at the plant level. This important department coordinates all activities concerning a produc-

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tion order, from its initial recording, through inventory layup and manufacturing, to getting the finished goods ready for shipment to customers (i.e., direct shipments) or to company warehouses for subsequent shipment to customers. Production planning and execution relies heavily on the plant’s database and its communications with all manufacturing work centers. Although daily manufacturing activities are the responsibility of the production planning and execution department, they need to be analyzed periodically by manufacturing managers for efficiency and effectiveness using OLAP analysis. Similarly, there may be the need to discover and track manufacturing problems. In this manner, manufacturing managers and their staffs can be assured that short-range manufacturing plans are being implemented on a current basis as mandated. Before looking at planned and actual manufacturing activities, manufacturing partnerships and their relationship to production planning and execution is set forth, along with a discussion about how to determine the appropriate production planning and execution technique for current operations on a daily and monthly basis. Intelligence furnished to manufacturing management and its staff over time can provide the needed insight to discover and track quality problems. Manufacturing Partnerships and Their Tie-in with Production Planning Not too long ago, many large manufacturers, especially in Japan, formed keiretsu, or company partnerships, by grouping the subcontractors who supply them with parts. The result is a production system, distributed among many companies, that has helped manufacturers strengthen their global competitiveness. More recently, however, the subcontracting system has started changing to reflect the structural sophistication in Japanese industries. Automakers, electric machinery manufacturers, and other large Japanese firms, which are suffering from sagging demand and decreasing earnings, are increasingly pressuring subcontractors to cut production costs. If they do not comply, they will be excluded from the keiretsu. In this new and realistic business environment, many subcontractors are looking for business partners outside their company groups. This presents a good opportunity for subcontractors to become independent. The keiretsu system is evolving from the existing pyramid-type coalition to horizontal or network-type coalitions. In the process, an increasing number of subcontractors are seeking to improve their technology and production to satisfy key manufacturers. Some are pursuing new business partners outside their company groups, while others aim to become independent specialists in processing or manufacturing. In the United States, car manufacturers (as noted previously) are heavily involved in sophisticated supply chain management systems. These systems currently include a focus on various cost-cutting programs, including reduction of work hours and model lines, extension of model-change cycles, and increased use of common parts.

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Determine the Appropriate Production Planning and Execution Technique Once appropriate manufacturing supply chain partnerships have been established, there is a need to determine the appropriate planning and execution technique over manufacturing operations. As noted previously in the chapter, a comprehensive approach centers on enterprise resource planning (ERP), which is real-time manufacturing software that incorporates the entire supply chain— from sales and order processing to production, inventory, and distribution. To help reduce operational errors between forecasts and actual numbers within an ERP operating mode, the best approach is to manage the error in demand planning as discussed below. Related to this ERP approach is the advanced planning and scheduling (APS) system that utilizes software to manage shop-floor activities. Typically, an integral part of an advanced planning and scheduling technique is just-in-time (JIT) inventory which is based on the elimination of wasteful or non-value-added activity within a company. Its primary goal is to reduce inventory as close to zero as possible and to reduce lead time via reduced setup and reduced lot sizes.

Daily and Periodic Production Planning and Execution Typically, a manufacturing company’s products can be produced upon receipt of customers’ orders, in anticipation of demand, or in some combination of the two. If goods are being produced to order, the usual arrangement is to have the production planning and execution department initiate action on the order via its data communications network. The order is then distributed on line to stock control, shipping, and accounting departments. However, for many companies, products are produced in anticipation of demand. Such is the approach taken below where the focus is on planning manufacturing activities at the corporate level on a monthly basis, then controlling on a daily basis at the manufacturing plant(s), using one of the current software packages. The procedure for determining next-period’s sales forecasts (one month hence) after adjusting for finished goods on order and on hand is the starting point for on-line production planning and execution, as shown in Figure 9.4. For manufacturers to be able to meet customers’ ASAP (as soon as possible) requirements, managing the error in demand planning is necessary. Typically, top-down demand forecasting systems exacerbate forecast errors, since they generally rely on aggregate data, which cannot account for variable demand patterns for individual items. The sweeping forecasting rules built into ERP and manufacturing systems are not adequate. Hence, a more rigorous process incorporating all lead-time variables, including demand planning and supply variability, is needed to supply optimized inventory for each items at each location. Such a system is the GAINSystems’ GAINS*OPS, an ICO (inventory chain optimi-

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zation) package, which takes an expert system, statistical analysis approach to balancing inventory to achieve optimal cost savings and service levels. Essentially, an ICO system uses data from existing ERP systems as well as existing or similar product history to create a demand plan for each item that uses multiple forecast models. A series of algorithms analyzes this data to determine optimal inventory level and cost to meet the most profitable service level. Also, the system is regenerative, using a cognitive function to measure its own prediction rate and self-correct as well as to adapt to changes in cost, environment, and demand. The major improvement with ICO is the balance of stock by changing the profile of inventories in buffers, thereby reducing overall inventory levels and costs, while maintaining or improving service.6 In Figure 9.4, because finished-goods production requirements for the next month provide the input for manufacturing, linear programming is used to determine what quantity of each product will be produced in each of the manufacturing plants. After next-period production schedules by plants have been computed, the next phase is “exploding” bills of materials. The materials planning-by-periods program multiplies the quantity needed of each component times the number of final products that must be manufactured. Also, it places the component requirements in the appropriate planning period, because some parts will be needed before others. In this manner, purchased parts and raw materials are placed on a just-in-time basis for production needs. The output for the materials requirements by future planning periods in Figure 9.4 can take two paths. The first is the purchasing of raw materials and parts from outside vendors, and the second is the manufacturing of parts within the plants. The outside raw materials provide the basic inputs for manufacturing specific parts used in the assembly of the finished product. Likewise, outside purchased parts are used in the assembly of the final product. Before materials are to be manufactured or purchased, it is necessary to determine on line if present inventories and materials on order are capable of meeting the company’s needs for future planning periods. At this point, it is important to note that perpetual inventories stored on line have been adjusted to reflect physical counts in order to produce accurate output for the materials availability and EOQ (economic ordering quantity) program. In this manner, the planned requirements for purchased and manufacturing parts can reflect actual conditions. Based upon established monthly production quotas that have been translated into planned daily requirements, the APS software provides the means for scheduling (i.e., executing) production orders on line through the manufacturing work centers on a daily basis. Other operational programs that are available to record and execute daily activities include attendance, payroll, work-in-process, and so forth. The output of these programs provides operations evaluation reports on manufacturing activities. In addition, the output can be analyzed in more detail on a daily basis using OLAP or other analysis. In turn, these types of analyses can be extended over a longer period of time to develop knowledge about what

Figure 9.4 A Monthly Manufacturing Planning System That Manages the Error in Demand Planning for Individual Products

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is actually going on in the company’s manufacturing operations—good, bad, or indifferent. In order to smooth production for each working day, a daily computerized scheduler is employed at each plant. Before the start of each working day (the program is actually run at the end of the prior day shift and reviewed by the plant’s management), the scheduler considers where jobs are backed up or behind schedule and where production bottlenecks are currently occurring. Based on these basic inputs, the scheduler simulates the activities of the plant for the coming day and determines what will happen as the day begins, thereby alerting the plant superintendent and foremen to critical areas that need attention. Because all data affecting manufacturing activities are entered as they occur, the scheduler feeds back information in sufficient time to execute upcoming manufacturing operations. This daily computerized scheduler also allows the production planning and execution department to make adjustments, if deemed necessary, to accommodate last-minute changes, which may not have been entered as yet on the plant database. In summary, periodic (i.e., monthly) programs do not operate individually but are integrated with daily on-line operations, as shown in Figure 9.4. Sales forecasts (as discussed in the previous chapter) serve as input for finished-goods product requirements which, in turn, constitute input for the next month’s production schedules by plant. In a similar manner, this monthly output is input for “exploding” bills of materials, forming the basis for materials requirements by future planning periods. This monthly information is then employed for manufacturing orders within the company’s plants and for placing orders with outside suppliers on a just-in-time basis. This input-output approach using the appropriate software at the corporate level and the plant level provides a basis for day-to-day scheduling and dispatching of various manufacturing operations. The daily advanced planning and scheduling system software, which is assisted by the daily scheduler, provides the means for managing ongoing shop-floor activities of the plants on a continuous basis. Discover, Analyze, and Resolve Quality Problems Although the APS software and the daily scheduler are quite capable of keeping a typical company’s operations under control on a daily basis, it is necessary to go a step further to discover, analyze, and resolve quality problems. For example, one of the major keys to an effective quality partnership between General Motors and its main union, the United Automobile Workers (UAW), is the sharing and resolution of quality problems. This jointly developed GM-UAW Quality Network has been a vital tool in transferring manufacturing knowledge and intelligence throughout the giant automaker’s far-flung operations. Communication has been an essential tool of the network both in terms of transferring the best practices from one plant to another and in building an atmosphere of trust in which effective collaboration can happen. To illustrate, at the Delphi

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Energy and Engine Management Systems (a division of GM’s Delphi Automotive Systems unit), the plant manager spends every Friday discussing quality concerns with members of the plant’s 80-plus self-empowered work teams. The outcome of these meetings is that there is an invaluable avenue for learning and a better understanding of manufacturing operations. A typical situation faced by manufacturing managers within a BIS environment centers around quality problems. For these managers, the goal of this analysis is to look through a database of assembly-line products in order to discover and track quality problems. Basically, quality problems are isolated and can be classified on the basis of two critical factors. The classification tree is then grown to a depth of three levels in order to illustrate that while a factor may not be relevant in predicting quality problems at the “general” (first level) of the tree, it becomes important when considered in the context of other factors. To illustrate an analysis of engineering quality control data, reference can be made to a knowledge discovery tool—namely, KnowledgeSEEKER. In this analysis, the “exception code” field was examined. All database records were examined and edited, producing a “clean” data set of 2001 records. Installation date and removal date were recorded and duration of service was computed. Basically, the specific goal of the analysis was to explain an “overheating” exception code. Thus, the question was: What characteristics in the data set tend to predict that the unit will be removed because of overheating? If the factors that affect overheating can be identified, then potentially the production process can be improved to lower the incidence of overheating problems. Alternatively, if the circumstances that lead to overheating can be isolated, then increased quality control can be applied to the areas that characterize these circumstances. Because time is of the essence in industrial processes, the sooner a problem can be fixed the less expensive the remedy. And since it is expensive to apply high levels of vigilance across all processes, any information that points to problem areas sooner rather than later can lead to major cost savings (for both vigilance and repair). As shown in Figure 9.5(a), the data set exception code was mapped into two values: “Overheating” and “Other.” This will allow concentrating on overheating as a particular problem (separate from all the particular exception codes that are recorded by the manufacturing process). The first level of the decision tree shows that, overall, there were 1,037 “Overheat” exception codes in the data set. Although a number of factors turned out to be significant predictors of overheating, the field “Install duration” produced the most dramatic effect. The likelihood of an “overheating” failure increases dramatically when the length of service is less than two days, then only 35.6% of the units produce an “overheating” exception condition. For units that have been in service longer than 12 days, the “overheating” exceptions constitute 80.9% of the cases. When the second level of the classification tree is grown as shown in Figure 9.5(b), “Series number” is introduced as an important classifier. This is true for both the extreme left (short install duration) node and the extreme right (long

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Figure 9.5 Analysis of Overheating Exception Code at the (a) First, (b) Second, and (c) Third Levels Using KnowledgeSEEKER

Source: Angoss Software International Limited.

duration) node. Although different series numbers are susceptible to an overheating exception in these two short and long duration extremes, it is useful to look at the “2A” and “2B” series. In the long duration installations, these two series numbers have the highest incidence of overheating exceptions: 92.3%. It should be noted that these two series numbers are also members of the node that characterizes high numbers of overheating exceptions in the short duration node as well. Here codes “2A” and “2B” are grouped in with codes that produce an overheating exception rate of 53.6%. The analysis in Figure 9.5(c) can continue to a third-level depth. As can be seen in the lower left-hand side of the classification tree, other factors in addition to service duration and series can be used to explain the presence of overheating exceptions in the data—sometimes (as shown here) in unusual ways. “Installation date” affects the presence of overheating exceptions for the lowest service duration group (less than two days) as follows. If the unit was installed in the first 19 days of the month, then the chances of it being an overheating exception

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increase from 35.6% to 58.7% (first-level node results compared to the thirdlevel node results). This applies to units with a series number of 1A, 1B, 2A, or 2B. This represents an increase of almost 25% and places these units in the same group as units that have been in service for two to five days (the second node at the first level of the tree shown above). It raises the question of why overheating exceptions should be higher in the first 19 days of the month (the exception rate for the installation dates 20th to 31st was less: 44.6%. A further examination of the procedures involved in short-duration series 1B, 2A, 2B, and 1A units would generally shed light on this question. Thus, the company has the capability to discover important causes of overheading exceptions which, in turn, can be used to suggest appropriate remedial action.7

EFFECTIVE MANUFACTURING BIS APPLICATION— HALLMARK CARDS One example of an effective business intelligence solution can be found at Hallmark Cards (Kansas City, Missouri) in the inventory area. To become more efficient at managing inventory and controlling costs (for both itself and its retailers), Hallmark found that making better use of the data and information that resides in the point-of-sale (POS) terminals of its retail outlets was necessary. The solution involved putting nearly 3 billion rows of data into a new NCR Teradata data mart. Hallmark chose this solution because it was the only database that could handle such large volumes when the project began. When the company reevaluated the BI initiative several years ago, it found that Teradata was still the best database for the job. Its parsing engine could handle the complex queries of a mature decision support environment such as Hallmark’s, and its massively parallel processing configuration splits the workload of these large queries evenly across all processors, reducing run time and allowing for near linear scalability. Furthermore, by upgrading to an NCR 5100 UNIX server from a proprietary NCR solution, Hallmark was able to reduce system costs, while moving to a more open solution. In terms of day-to-day operations, the POS data is uploaded to the data mart each night from more than 15,000 retailers, including nearly 3,000 Hallmark card shops. About 300 Hallmark managers, analysts, and general employees can access the data mart and apply MicroStrategy’s DSS Agent tool to the data for ad hoc reporting. In turn, they can put customer purchase patterns under a magnifying glass because they have access to transaction-level and item-level data for more than 40,000 products. Whatever Hallmark’s business needs are—deciding on product development cycles, preparing pricing strategies, or analyzing the effectiveness of promotional programs—the business intelligence solution makes them somewhat routine. The system can take high-level business questions, such as, What were the sales of Everyday cards in December for all card shops in the Chicago metropolitan area? and translates them into SQL statements

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that can be queried against the database. Then the system presents the answers to the user in a very familiar, usable format, such as a spreadsheet or chart.8 FUTURE OPERATIONAL INTELLIGENCE CAN BE EXTENDED TO RUN THE VIRTUAL FACTORY The utilization of operational intelligence to run real manufacturing operations can be extended to include the virtual factory. The virtual factory can be defined as a number of factories linked together electronically, each focusing on what it does best. Since the factories are linked by an electronic network of some type (such as an E-commerce network currently), they can operate as one, regardless of their location. This network makes it possible for companies with dissimilar information systems to exchange information, knowledge, and the resulting intelligence for improving manufacturing operations as well as increasing productivity. Just as it allows companies with different CAD (computeraided design) systems to collaborate electronically on designs, it also permits potential suppliers to gain entry to the system in order to bid on jobs with minimal hassle. Finally, a virtual factory allows a small manufacturer to have the same access as a large partner. Due to its complexity, there is the need for open-computing standards, more abundant bandwidth, improved computer security, and expertise with virtual operations. For example, AeroTech (a spin-off of McDonnell Douglas Aerospace) acts as an information broker for the community by signing up new partners, tracking network memberships, overseeing security, and serving as a converter that permits partners with different formats to communicate. As such, it permits members to carry out a wide variety of collaborative tasks and is extremely secure. To accommodate a broad range of tasks and users, and to make the system as simple as possible to use, AeroTech employs protocols developed for the Internet, which itself is just an extra-large network that connects millions of dissimilar computers around the world. Also, AeroTech permits its members to choose from a very wide assortment of telecommunication methods and speeds. Those members with minimal or sporadic needs access the system with modems, while more permanent participants, such as customers within other large aerospace companies or the U.S. government, use dedicated high-bandwidth links. All in all, AeroTech’s approach is a flexible, low-cost network that is a model for a new era in manufacturing.9 To assist in developing business intelligence to run the virtual factory, languages such as Java allow users to connect to a remote site and use small pieces of software, called applets, one at a time. Any computer that can run a Web browser can run this kind of software. Essentially, Internet-based Java-like languages allow customers to obtain modules for performing particular functions as they need them by downloading them across an increasingly fast and reliable Internet. Also, Java was developed with security safeguards in place to foil viruses and other threats. From this view, a manufacturer posts worksheet ap-

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plets on its Web pages in order to allow potential customers to simulate the function of its products before buying them. A valve manufacturer, for example, might supply an applet that allows engineers not only to see pictures and CAD drawings of its products but also to use a working spreadsheet that would permit them to make key design calculations and show the results on screen. But equally important, these applets allow companies to deliver appropriate bid requests over the Web. These requests include a combination of moving diagrams of the products that they would like potential suppliers to bid on. A Java-based interface for a three-dimensional CAD system not only allows companies to put a catalog on the Web, but also lets potential customers examine a threedimensional representation of its products from any angle. As network-based software becomes more and more sophisticated, it will present new opportunities to extract the power of the Web to help run the virtual factory. And, at the same time, the operational intelligence that is derived within this environment can be shared among all participants. SUMMARY The manufacturing function has and continues to witness many exciting improvements. Among these are automated factories, industrial robots, vision systems, and supply chain systems that are linked to E-commerce. In addition to the employment of advanced manufacturing machines and methods, newer production planning and execution systems, such as enterprise resource planning and advanced planning and scheduling systems, plus mathematical and statistical models have helped to bring an entirely new approach to manufacturing. For these advances to be effective in understanding manufacturing operations better, it is necessary to perform them using operational intelligence on a daily basis. Essentially, after the products are engineered properly, purchasing must buy on an optimum basis and, at the same time, utilize value analysis to ensure product profitability. In turn, there is a tie-in of premanufacturing activities with manufacturing operations so that there is a timely flow of raw materials and parts from outside vendors on a just-in-time basis through the manufacturing work centers. The end results are manufactured products for direct shipment to customers or to finished-goods stockkeeping. Throughout the entire process, effective management of manufacturing operations must be employed to get a complete overview and a better understanding of detailed day-to-day operations. If problems are discovered, such as those related to quality, manufacturing intelligence can be used to get a better grasp on them as a prelude to their solution. The bottom line is not only a more efficient approach to manufacturing, but also a more intelligent approach to a company’s manufacturing operations. NOTES 1. Robert J. Thierauf, Group Decision Support Systems for Effective Decision Making: A Guide for MIS Practitioners and End Users (Westport, CT: Quorum Books, 1989).

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2. Janet Gould, “The Internet Turns the Manufacturing Paradigm Upside Down,” IDS, June 1999, pp. 20–29. 3. Jennifer Reingold and Marcia Stepanek, with Diane Brady, “Why the Productivity Revolution Will Spread,” Business Week, February 14, 2000, pp. 112–118. 4. Anil Khurana, “Managing Complex Production Processes,” Sloan Management Review, Winter 1999, pp. 85–97. 5. Angoss Software, KnowledgeSEEKER in Action: Case Studies (Toronto, Canada: Angoss Software International, 1996), pp. 3–4. 6. Deb Navas, “New Tools for Old Inventory Problems,” IDS, October 1999, pp. 50– 58. 7. Angoss Software, “Study 2: Product Assurance, Quality Control Data,” KnowledgeSEEKER in Action (Toronto, Canada: Angoss Software International, 1996). 8. Samuel Greengard, “How to Profit from Business Intelligence,” Beyond Computing, January–February 1999, p. 28. 9. David M. Upton and Andrew McAfee, “The Real Virtual Factory,” Harvard Business Review, July–August 1996, pp. 123–133.

10 Financial Intelligence in Accounting FURTHER A COMPANY’S FINANCIAL PLANS BY UTILIZING A BIS OPERATING MODE In the past, an organization’s ability to manage and leverage its financial plans went a long way toward ensuring its survival and growth. However, due to today’s fast-changing times, this is only partly true. Currently, financial markets are particularly sensitive to the financial performance of a company in the short to long run. A successful company in the 21st century will be one that does the best job of leveraging its financial capital as well as intellectual capital. It must be able to accommodate the massive changes in the business world, which include technological innovations, unexpected commercial alliances, industry transformations, and a host of powerful social forces. Many of these changes are linked directly or indirectly to the Internet and the World Wide Web—both of which have been discussed at length throughout the text. Related to the best utilization of financial capital and intellectual capital is a company’s vision of the future. For a typical company, top-level managers must want the company to be the leader in its industry, in, say, five years or the company must do this to better service its customers. Vision is not merely longrange financial planning intended to realize more sales but a fundamental change in how the company must operate to become more competitive or achieve a leadership role. Examples include Fred Smith’s vision when he launched Federal Express and Max Hopper’s vision when he and several others created American Airlines’ Sabre flight reservation system. In addition, visioning is linked directly to a company’s critical success factors (CSFs), key performance indicators (KPIs), and financial ratios. Essentially, visioning over time and its measurement

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become the raw materials for business intelligence systems to further a company’s goals in accounting and finance. This vision of the future ties in with the materials presented in the previous chapters. Basically, the emphasis in Chapter 7 was on the utilization of shortto long-range strategic planning intelligence by managers in order to choose objectives and goals, select strategies, develop programs, and set policies. In turn, Chapters 8 and 9 were directed toward using information and knowledge and its resulting intelligence to assure effectiveness in the acquisition and use of the organization’s resources by marketing and manufacturing managers, respectively. This chapter builds upon these prior chapters by centering on financial intelligence and its tie-in with short- and long-range strategic plans. The focus will be on discovering and utilizing appropriate financial intelligence from accounting transactions to carry out a company’s strategic financial plans in a BIS operating mode. Tie-in of Financial Intelligence in Accounting with Strategic Intelligence The environment in which accounting and financial decisions are made is highly dynamic, complicated, and intelligence intense. For example, investment banks, commercial institutions, regulatory institutions, and brokerage houses find their staffs increasingly strained in both good and bad financial times. To help streamline operations, it is necessary for a company to provide the most intelligent, timely, and focused approach to its financial operations by employing a BIS operating mode. Such a mode allows for a tie-in of financial intelligence with strategic intelligence. As noted before in the text, strategic intelligence is the focus of corporate planning at the highest level for top management and its corporate planning staff. It is this intelligence that directs a company’s total operations today and tomorrow. By placing a company’s total operations in dollars and cents, financial intelligence provides a guiding hand to a company’s present and future profitability or lack thereof. Today, financial intelligence for a typical company recognizes that fastchanging times are linked to reconciling conflicting financial objectives and goals. More often than not, there are multiple goals that are not easily reconcilable into a single objective function. Finding or recommending the optimal decision is not easy, but fortunately traditional computing methods lend themselves to this type of problem. Thus, there is a vast business opportunity in using integrated financial intelligence tools, techniques, and methodologies to develop a flexible business intelligence system for various activities, such as trading, portfolio analysis, and arbitrage; currency and financial instruments analysis; and adherence to regulatory guidelines. An effective intelligent financial system provides a transparent environment, which allows a user to receive timely advice consistent with the financial goals of the organization and the marketplace at large. An intelligent financial support

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application should be developed using the latest computing technology as well as current accounting and financial modeling approaches. It should be built using a flexible financial acquisition interface that requires no technical knowledge to install, paramaterize, or customize. In addition, the financial intelligence database should be kept current with existing financial and production databases. Overall, the financial intelligence system should be fully integrated into the mainstream transaction processing environment so that there is appropriate feedback to the interworkings of a company’s strategic intelligence system. Relationship of Executive Information Systems to Business Intelligence Systems in Accounting Business intelligence systems in the areas of accounting and finance, as in other functional areas, have their origin in other type systems. One such system is the executive information system (EIS), which locates the data desired, places it in a common format, massages it into useful form, and presents it as useful information for the executive. An EIS allows the executive to combine key pieces of information that support the decision-making process. It is the technology-based alternative to people spending hours or even days going through mounds of computer output, querying databases, making numerous telephone calls, and going through several iterations of manually pulling together material from multiple sources. An EIS is not, however, a static decision mechanism or an extension of existing computer mainframe systems, although mainframe linkage is generally necessary. It demands no tight controls or typing skills from the executive and is not a means of mechanizing the individual’s job. In light of these comments, an executive information system can be defined in its broadest sense as one that deals with all of the information that helps an executive make important competitive and financial decisions, keeps track of the overall business and its functional units, and cuts down on the time spent on routine tasks performed by an executive. As such, an EIS is capable of providing an executive with the right information in the right format, fast enough to enable the individual to make the right decisions.1 Currently, executive information systems make use of data marts and data warehouses that are widely used in business intelligence systems. For example, data marts can drive an EIS that supplies a wealth of critical daily operations information. The EIS aggregates and summarizes that information for optimal use so that an executive logging on to the system is able to see instantly how his or her operating unit is doing in terms of the most important business criteria, like daily revenues, raw material costs, and operating profits. In other cases, data marts are used for a detail-oriented field support EIS that can drill down so far into daily operations as to reveal, say, the number of times the “void” or “cancel” keys were pressed on point-of-sale computers. Based on information derived from the EIS, managers can visit the locations under their jurisdiction

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and work out problems. It should be noted that these access capabilities should be used to allow managers to act in a positive manner (i.e., offer congratulations) rather than in a negative manner (i.e., complain). Overall, executive information systems are capable of helping managers at all levels to gain a better understanding of their operations. FORWARD AND BACKWARD INTEGRATION OF FINANCIAL ACTIVITIES To integrate a company’s financial activities effectively with other organization activities, top management and its corporate planning staff need to take an enlarged view of a company by incorporating forward and backward integration of financial activities. Similarly, company treasurers, controllers, and their staffs must enlarge the scope of their thinking and integrate their financial activities from a broader perspective. This includes using the Internet and the World Wide Web to speed up financial operations, tying in a company’s critical success factors (CSFs) with key performance indicators (KPIs) and financial ratios, benchmarking to detect the best business practices, and employing appropriate accounting and financial principles. Basically, this backward and forward integration approach allows managers to “get the big picture” when reviewing the company’s total activities so that there is optimization of resources for the entire company instead of one or just a few parts of it. From a forward perspective, the success that the typical company has in attaining its general objectives and measurable short- to long-range strategic planning goals depends on the degree of integration of its operations with all of the company’s functional areas. In terms of a backward perspective, a company’s critical success factors form the basis for measurement using KPIs and financial ratios that tell where the company has been. Overall, enlargement of the scope of accounting and finance activities using forward and backward integration is found within a successful BIS environment. Integration of Electronic Commerce and the Marketplace for Financial Success Currently, an important means to conduct business centers around electronic commerce (E-commerce) that uses the Internet and the World Wide Web. As discussed at various times in this text, E-commerce is the present and future of a typical company’s operations. By way of review, an effective electronic commerce platform centers on delivering five critical elements: the operating system, the Web server, the commerce application server, the tools/middleware (and appropriate standards support), and the underlying architecture. The operating system provides the basic services plus reliability, availability, scalability, and security. The Web server must provide high-volume Internet connections. The commerce application server adds extensible commerce functionality, such as

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transaction and order processing support. The tools, middleware, and standards support enable a company to build and maintain a quality site. But the most important element is the underlying architecture. A strong architecture makes it easy to build, connect, and deploy commerce solutions with flexibility over time. The breadth of E-commerce means that most solutions require many different types of functionality put together with multiple skill sets. At this time, no one vendor can be best-of-breed in all areas. Hence, it is necessary for a typical company to partner with computer vendors that are best-of-breed in their areas of expertise. The most successful electronic marketplaces will be those that focus on specific niches, vertical markets, or industries. For example, business-to-business sites, such as MetalSite and ChemConnect, appeal to very well-defined buyers and sellers of metals and chemicals. Suppliers interested in working with an Emarketplace must understand the business model the site follows, for that will determine the customers it attracts and the way it can be profitable. Options include a seller-controlled model, a buyer-controlled model, or a dynamic supply-and-demand model. In seller-controlled sites, such as MetalSite, success depends on offering the right products or services. Buyers look for products in general categories and will browse through numerous catalogs before choosing. On the other hand, buyer-controlled marketplaces attract buyers who know what they want, how much they want to pay, or both. These sites may eventually allow customers to specify a product that does not yet exist. In the dynamic supply-and-demand model, prices for goods and services are determined by supply and demand. This is the model used by ChemConnect, whose strategy is to become the Web’s largest global chemical exchange. When a company changes the business model of an E-marketplace, it changes the kinds of customer it will attract. For example, a buyer-controlled travel site that shifts to a seller-controlled model would look more like a traditional travel agency—perhaps serving business travelers with less flexibility and less price sensitivity. It is expected that future E-marketplaces will embrace multiple models. For example, Expedia.com’s primary market today is seller-focused, offering travel services from multiple vendors. It could easily implement a buyer-focused model by creating a secondary market for travel suppliers in which customers make specific requests and allow the providers to determine whether to meet the pricing and conditions. Many industries could make good use of Emarketplaces. The obvious candidates are hardware manufacturing and commodities (i.e., industries with inventory and limited shelf life for products). But there are other candidates too. Insurance brokers could set up E-marketplaces for multiple insurers. The automobile industry could set up a marketplace for loans and leases from competing financial services firms. Overall, E-marketplace hosts and suppliers need to understand what the marketplace’s business model means in terms of business opportunities.2

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Tie-in of CSFs with Key Performance Indicators and Financial Ratios A logical starting point for financial planning intelligence is the determination of a company’s critical success factors. CSFs can be best facilitated by a company’s culture when organizational personnel believe that what they are doing is worth doing. As noted in Chapter 7, a company’s critical success factors are the limited number of areas in which results, if they are satisfactory, will help ensure successful competitive performance. Basically, they are the important areas where things must go right if the company is to flourish. If results in these key areas are not adequate, the company’s efforts for the period will be less than desired. Typically, CSFs are areas of activity that should receive constant and careful attention from management. Performance in these areas needs to be measured on a continuing basis and the results should be available to higher management levels. Due to the fast-changing business times facing a typical company, CSFs need to be reviewed often for relevance. That is, the appropriate CSFs for today may not be appropriate for tomorrow. There is a need for higher levels of management to employ problem finding in order to relate what is known and what is not known to what needs to be known for a company to have the capability to be one step ahead of its competition. The knowledge gained from this approach, which includes the discovery of new emerging patterns and trends, provides the basis for proposing new strategies that center on improving a company’s competitiveness and its return on investment. Such an approach provides new insights for further analysis. The bottom line is that a company’s critical success factors need to be changed over time to reflect a changing world. An effective way to make the appropriate changes is to employ knowledge management resources at the proper time and place for effective results. For a typical company, there are a number of key performance indicators that are useful in determining its viability. Typical ones in marketing include the following: • ability or inability to make changes to a company’s marketing strategies • increase or decrease in company sales • increase or decrease in market share • increase or decrease in customer satisfaction • market research on continuing the status quo or bringing new products to market • acceptance or nonacceptance of new products and services by customers • increase or decrease in quality of products and services • improved or unacceptable delivery times • increase or decrease in sales and operating expenses • increase or decrease in gross profit and cash flow for a company’s products and services

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In a similar manner, a number of KPIs that tend to focus on financial ratios can be developed not only for a company’s other functional areas but also for the company as a whole. Overall, KPIs reflect those performance areas over which a company has control. The company is able to manage the performance of these areas and make changes when necessary. Chief among a number of useful financial ratios for financial analysts is return on investment. ROI analysis can be used to judge present performance and evaluate future investment opportunities. Analysts can also use ROI to rate managerial effectiveness and to compare potential profitability of divisions and departments. Although the technique of ROI analysis is uncomplicated, the potential applications are varied and valuable. Basically, the ROI equation is earnings divided by total assets, then multiplied by 100 to give a percentage. A problem in calculating ROI arises in identifying what is meant by earnings or total assets. Although there is no single, correct way to figure ROI, it is customary to use earnings from operations before taxes, sales after returns and allowances for bad debts, and net, year-end book value of assets. It is important that there be consistency and the same measure of earnings, sales, and assets be used when figuring ROIs for different periods. Also, managers should be prepared for apparent surprises when comparing the company’s ROI to another company’s—that is, the other company may have used different measures in calculating its ROI. An alternative approach to ROI is economic-value-added (EVA). For a company to be truly profitable, it must have money remaining after it deducts the cost of all the capital it employs—that is, both equity and debt. Until a business returns a profit that is greater than its cost of capital, it operates at a loss. Hence, what is called profits, the money left to service equity and debt, is usually not profit at all. Going beyond ROI and EVA, there are a number of financial ratios that can be applied to a company’s operations. Typical ones (which measure some aspect of a company) are: • return on sales (operation efficiency) • capital turnover (management efficiency) • current ratio (liquidity status) • investment status (measure of solvency) • return on assets used (asset profitability) • average collection period (receivable investment) • inventory turnover (inventory utilization) • undelivered commitments (days of sales in backlog) • net worth debt ratio (credit strength) • acid test ratio (immediate liquidation) • stockholders’ earnings status (percent earnings available)

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Some of these financial ratios are explored in more detail later in the chapter. Generally, they focus on the overall aspects that assist decision makers in their financial strategic thinking. Financial ratios can be calculated as needed and thereby serve as the basis for management by exception, where appropriate ranges are assigned to each ratio. Typically, to calculate these financial ratios, it is necessary to interact with a corporate database or data warehouse.

Benchmarking to Detect the Best Business Practices An important way to judge the performance of a company is not only to compare it with other units within the company—using KPIs and financial ratios (as noted above)—but also with outsiders that represent the best industry practices. Commonly, this technique is called benchmarking, in which a company can take a look at its industry in order to get an idea of the product or service gap in meeting customer needs. When the Xerox Corporation started using benchmarking not too long ago, management’s aim was to analyze unit production costs in manufacturing operations. Uncomfortably aware of the extremely low prices of Japanese plain paper copiers, the manufacturing staff at Xerox wanted to determine whether their Japanese counterparts’ relative costs were as low as their relative prices. The staff compared the operating capabilities and features of the Japanese machines, including those made by Fuji-Xerox, and tore down their mechanical components for examination. As somewhat expected, the investigation revealed that production costs in the United States were much higher. Discarding their standard budgeting processes, U.S. manufacturing operations thereby adopted the lower Japanese costs as targets for driving their own business plans. Top management, gratified with the results, directed that all units and cost centers in the corporation use benchmarking. In contrast, distribution, administration, service, and other support functions of Xerox found it difficult to arrive at a convenient analogue to a product. These non-manufacturing units began to make internal comparisons, including worker productivity at different regional distribution centers and per pound transportation costs between regions. Next, they looked at competitors’ processes. Logistically that meant comparing the transportation, warehousing, and inventory management of Xerox’s distribution function with those of the competition. The initial step in the process was to identify what will be benchmarked (i.e., measured)—expense-to-revenue ratios, inventory turns, service calls, customer satisfaction, or whatever the “product” of the particular function is. Then it was necessary to pinpoint the areas that needed improvement. In Xerox’s experience, managers tended to concentrate first on comparative costs. But as they became more knowledgeable about benchmarking, managers discovered that understanding practices, processes, and methods was more important because these defined the changes necessary to reach the benchmark costs. Moreover, as managers became more confident about benchmarking, they could readily extend it beyond

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cost reduction to profit-producing factors, like service levels and customer satisfaction.3 Annual reports and other easily available publications are important sources for the purpose of comparison. They can uncover gross indicators of efficient operation. Universally recognized measures like return on assets, revenue per employee, inventory turns, and general and administrative expenses will help identify the well-managed companies. To identify superior performance in specific functions, a company can utilize trade journals, consultants, annual reports, other company publications in which “statements of pride” appear, and presentations at professional and other forums. The same well-run organizations keep turning up. Currently, there is another approach to traditional benchmarking that is less elaborate and tends to be more tactical. Basically, companies identify specific operational problems or opportunities. They seek out a wide range of other organizations from whose operations they think that they may be able to learn. Rather than trying to copy the practices of other companies directly, they may be looking for what Professor David Garvin of the Harvard Business School calls useful analogies, hoping thereby to generate creative ideas for their own operations. When the Mobil Corporation wanted to remake its 8,000 service stations, for example, it studied the Home Depot to determine why the retail chain’s customers were so loyal, and it studied the pit-stop crew for Team Penske race cars to learn about quick turnaround. While service stations obviously could not replicate the tactics of the Penske crew, Mobil used the experience to generate creative thinking on the concept of minimal time in the pit. It was that experience that led to the introduction of the Speed Pass, Mobil’s new wave-it-at-the-gas pump credit system. Similarly, other companies have tapped dissimilar businesses for quick insights and learning.4 Underlying Accounting and Financial Principles Principles that underlie efficient and effective accounting and financial activities are based on essential knowledge of a company’s total operations. Although these principles have evolved, over the years they are subject to the fastchanging times of a global economy. For example, reengineering in the past focused on cutting costs. Today, the center of attention is more and more on increasing revenues. Too often in the past, companies feared that they would lose good customers, so they forgot about the revenue side. But when a company reengineers for revenue, it can provide better customer service and only lose customers that cost the company money. Also, by focusing on the revenue side, a company can use its resources, especially computer technology, to help customers reduce their costs over time. Related to these accounting and financial principles is the people factor. According to a survey sponsored by Arthur Andersen and the American Productivity & Quality Center, there is a vast difference between where participants are and

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Figure 10.1 Accounting and Financial Principles Based on a Company’s Total Operations

where they think they should be. Of the companies surveyed, 79 percent said that managing organizational information and knowledge was important or essential to their strategy, while only 15 percent thought that they were doing a good or excellent job of it. In addition, 91 percent said that they had no way to link information and knowledge management to financial results or that they did so poorly. For a typical company, the essential message is that business intelligence systems need to be implemented to allow a good tie-in of information and knowledge with financial results.5 Principles that underlie the areas of accounting and finance are found in Figure 10.1. As times change, there will be a need to update these principles. This takes the direction of helping a company’s customers do a better job which, in turn, will be noticed and taken into account by their customers. Hence, the net result is a “win-win” situation for all parties involved.

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ACCOUNTING FACTORS THAT ARE RELATED TO EFFECTIVE FINANCIAL INTELLIGENCE To discover knowledge and gain a better understanding of a typical company’s financial operations, treasurers, financial executives, controllers, and their staffs must enlarge the scope of their thinking. Essentially, this means allowing them to “get the big picture” when analyzing the company’s total activities so that there is optimization of resources for the entire company rather than for just one or a few parts of it. The success the typical company has in attaining its mission as well as its short- to long-range objectives and goals depends on the degree of integration of its operations. Enlarging the scope of organizational activities and rethinking traditional accounting and financial activities from a newer view are set forth in this chapter. Important factors that lend themselves to financial intelligence can focus on a wide range of topics. Among them are: (1) using Web-driven reporting to improve the budgeting process, (2) making use of content analysis of financial statements, (3) making more effective use of cost-accounting approaches, (4) applying the balanced scorecard approach, and (5) using accounting and financial software to enhance financial intelligence. As a starting point, problem finding is able to diagnose more types of financially oriented problems facing accounting and finance managers as well as toplevel executives than traditional problem-solving approaches. To illustrate the use of problem finding within a BIS environment, consider the following example. A measurable goal of the corporation is to increase the value of its investments by 15 percent in five years. Top-level executives and the corporateplanning staff, working in conjunction with the corporation’s vice president of finance, the treasurer, and the controller, can analyze and solve the problem using different approaches. Within an OLAP operating mode, the “bottom line” for each investment approach can be evaluated thoroughly by examining ways to increase investment values. If the investment’s total return does not attain the corporation’s goal, the important variables can be examined to determine what individual profit contributor(s) is (are) likely to fail to produce its share of gains. The net results are that these analyses are useful to identify future potential investment problems before they happen. In turn, the financial results are quickly calculated based on these problems, and the effect on the aggregrate returns is seen immediately. From a BIS perspective, after giving consideration to a company’s future return on investment, decision makers can evaluate various methods to increase ROI (return on investment) today and tomorrow by employing knowledge learned from past and current financial performance. One method would focus on the company earning more profit without more capital in terms of cost-cutting methods in its manufacturing plants and warehouses by employing the KISS (keep it simple, stupid) principle. Another method would center on using less capital. For example, Coca-Cola uses plastic containers for concentrate instead

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of costlier metal ones. Still another method would focus on the company investing capital in higher-return projects. There is a need, however, for decision makers to make sure that these projects earn more than the total cost of the capital they require. Thus, several methods that come from financial intelligence gained from the company’s operations can be employed to increase a company’s return on investment. Some of this intelligence can be tied back to the principles set forth above for a company’s overall operations. Using Web-Driven Reporting to Improve the Budgeting Process The budgeting process with Web-driven reporting is quite different from that of just a few years ago. Essentially, prior budgeting systems were very labor intensive. Results from spreadsheet calculations were collected into a desktop database, which was then used for reporting. Reports were generally run in a batch mode, printed in a hard copy form, and distributed to management. But data entry and maintenance costs became serious issues, and getting reports based on new data involved significant processing delays. The paper reports served their primary purpose of revealing the overall budget, but it was difficult to determine how particular budget amounts were divided. Generally, a special report request could take several hours to several days to be filled. Today, the typical budgeting process can make use of specialized software, such as Comshare’s BudgetPLUS, which is a client/server and Web-enabled enterprise budget application. The client portions, including both an Excel interface and a custom management/reporting interface, run on Windows 95 and Windows NT. The server portion runs on Windows NT. BudgetPLUS has an easy-to-use spreadsheet integration. This software allows individual managers to have ownership in the budgeting process. Typically, BudgetPLUS has the capability to be organization-wide and include all of the company’s managers. Company managers, from engineering to human resources, can use BudgetPLUS to create and monitor their own budgets. With BudgetPLUS’s spreadsheet integration, users become more productive very quickly, creating and perusing reports on their own. Each budgeter’s unique requirements are met via a small number of pre-built templates that handle a range of budgeting needs, including such things as head count, facilities, and engineering. Using BudgetPLUS, the company creates spreadsheet templates to reflect the unique spending needs of each department. Using historical data on which to base their budgets, users simply open the application, see the template, fill in what they think they will spend, hit SUBMIT, and the budgeting process begins. Once the budgets are established, BudgetPLUS can create financial reports and track actual spending against budget projections. Managers can pull information from the database, format reports, and post them to a folder on a shared network, where managers can review the reports. Comparison reports can also be developed looking at the data from any angle.

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Another Web-driven reporting approach to the budgeting process is available from Information Builders. Using this approach, managers and their support personnel at all levels can obtain budget and cost information much more quickly and easily than ever before. For many companies, this means breaking down the budget into two primary components: the base layer and the capital layer. The base layer includes budgets for existing operations that are currently producing revenue. The capital layer includes discretionary investment for projects. The capital budget helps determine which of many potential projects to pursue each year. Typically, company engineers identify all of these opportunities and submit development proposals for management review. In order to have an effective budgeting process, it may be necessary to integrate several hardware and software vendor products currently on the market. For example, Gulf Canada Resources employs the main software components of its ODIN (Operations and Development Information Network) application that includes WebFOCUS, Cactus, EDA, Netscape Enterprise Server, and the Oracle database. The hardware platform is an Enterprise 3000 server from Sun Microsystems, running the Sun Solaris operating system. Technical support for the Netscape Enterprise Server software administers the Oracle database and establishes connectivity between the server and the rest of the network. Consultants from Information Builders have helped refine the ODIN system requirements, install the Information Builders software, and develop the ODIN application. Software developers have created the ODIN Web pages and reports with WebFOCUS, a server-based reporting and analysis engine that provides comprehensive EIS capabilities to Web users. Users can run standard reports, parameterize queries, and even create ad hoc reports. HTML and Java is used to create some of the client-side portions of the ODIN system, while Cactus is used to dynamically generate HTML entry forms used for entering budget data into the ODIN database. Because Cactus applications are based on a three-tiered architecture, they are easy to adapt to the Web’s distributed makeup. Finally, EDA is used to populate the ODIN database with information from Gulf’s key financial and operations systems into the Oracle database underlying ODIN.6 Making Use of Content Analysis of Financial Statements The purpose of reviewing a company’s current financial performance via financial ratios is not only to assist in giving direction to the company’s historical financial knowledge base, but also to assist in giving direction to the company’s top management and its corporate planning staff for furthering financial strategic thinking. They can relate external environmental factors to internal ones and compare their growth and profitability with other firms in the industry. This financial analysis gives an indication of whether or not the company is increasing its market share and giving a fair return to its stockholders, when considering the industry’s current state. This overview of financial ratios needs to be sup-

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plemented by a more detailed analysis of competitors’ financial statements, frequently referred to as content analysis of periodic financial statements and annual reports. Basically, content analysis provides valuable clues to competitors’ corporate strategic thinking. It is a source of both financial information and new directions by the competition. Content analysis of competing companies can be very useful for getting a handle on specific issues of corporate strategy and can serve as a primary or supplementary source of information. It can be used to analyze current changes and past correlates of performance as well as for more general investigations of questions of interest to top-level executives and their corporate planning staffs. In terms of a thorough analysis of overall performance and variances that are tied in with financial ratios, graphics can be extremely helpful. Although financial ratios can be compared on a day-by-day, a week-by-week, a month-bymonth, or some other time-period basis, the purpose of this analysis by financial executives is to determine whether or not the company is improving its financial stature. Of equal importance is the fact that financial ratio analysis that uses graphics discloses whether or not financial managers are really managing the company effectively over the short term to the long term. Generally, “at a glance” graphic presentations of financial performance and knowledge allow managers to start their thinking processes quickly. A “picture” may tell managers immediately what they want to know about their own operations—information that might otherwise be buried in stacks of computer-generated reports. In a similar manner, information and knowledge in graphic form can be brought to bear on important comparisons to a company’s competitors. Overall, computerprepared graphics of the important content of a company’s financial statements and of its competitors are very effective for getting managers to think about new ways of running their operations and supporting decision-making efforts. Making More Effective Use of Cost-Accounting Approaches Because companies in the United States are competing in global markets, they must get control of their production costs. The good news is that, for the most part, U.S. manufacturers have been meeting that challenge with integrated manufacturing, just-in-time inventories, factory automation, robotics, and the like (refer to the prior chapter). The bad news is that even with these manufacturing advances, costing systems have not kept pace. Generally, today’s productcosting data are wrong, often by extremely large margins. Without more accurate costing methods, the bottom line is a continued competitive crisis. Hence, it is necessary to rethink the current cost-accounting methods in this 21st century. More specifically, there is a need to take a hard look at activity-based costing, technology accounting, life cycle accounting, and target costing. Activity-based costing (ABC) directly relates costs to the resources used to produce a product. A starting point is analyzing a company in order to determine

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all its production and support activities. All costs are then assigned to activities. Next, activities are measured and linked with the products that consume the activities. The total cost of the finished product is an accumulation of the activities required to make the product. In addition to assigning the costs to the products that actually absorb the activities, ABC identifies cost drivers and isolates non-value-added activities. With this information, a company can establish priorities that focus on eliminating or reducing non-value-added activities. Basically, the ABC method allows a company to identify specifically the activities that are generating costs so that management has a better idea of why budgeted numbers are being exceeded. In essence, the ABC method is a restatement of costs. If a financial analyst looked at the production department section of a profit-and-loss statement in both traditional cost accounting and ABC accounting reports, the numbers would be identical on the bottom line. However, they would be categorized differently. Going a step further, ABC has spawned an intellectual descendant known as activity-based management (ABM). Where ABC attempts to measure a product’s true cost, ABM uses cost information to evaluate an entire operation. The goal is to distinguish between value-added costs (necessary) and non-valueadded costs (unnecessary), and minimize the latter. For example, take a factory that has trouble delivering on time. Many such plants have a group of employees called expediters. The expediters’ job is to speed certain products through the production process so that customers get them sooner than they would otherwise. ABC can capture all the costs associated with expediting—the people, the cost of obtaining missing parts, the freight to get goods to the customer quickly, and so forth. Managers using ABM, however, would observe that expediting in general is a non-value-added activity. Rather than just counting up all of the costs, a manager would be better to make the production process faster and more predictable so that expediting is unnecessary. Technology accounting is based on the concept that technology costs, such as plant, equipment, and information systems, should be treated as direct costs, equivalent to direct labor and materials. Today’s technology costs, for the most part, are accounted for by amortization (or depreciation) and included in overhead. The problem with this method of accounting is that conventional amortization methods are time based, not production based. A time-based method equates time with cost and often causes amortization of idle machinery to increase overhead costs when there is little or no production. This encourages constant and ineffective production to maintain a desired cost per unit. Product costs are further affected by the inclusion of the time-based amortization in overhead, which must then be allocated to production. By adopting a direct production-based amortization method, such as units of production, costs are matched more accurately with products manufactured. When determining the number of units over which to amortize an asset, only the planned production of the asset should be considered. Simply using the asset’s total lifetime production capacity does not solve the problems associated with a time-based

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method. Total units used to amortize an asset should be limited by planned production, product demand, and obsolescence of the asset’s technology or manufacturing process. As with activity-based costing, the choice of an overhead allocation method can significantly alter product costing. As technology costs increase as a percentage of total product costs, any misallocation will improperly influence management decisions and possibly the financial results of the company. Life cycle accounting accumulates the costs of activities that occur over the entire life cycle of a product, from inception to abandonment by the manufacturer and the consumer. A primary objective of life cycle accounting is a better match between revenues and expenses. All costs are capitalized as incurred. These costs are charged to earnings as units are sold, based on the total planned number of units to be brought to market. The shortcomings of the traditional cost-accounting model, then, are largely due to the changing manufacturing environment. To remain competitive in today’s global marketplace, the time has come for U.S. manufacturers to adopt new cost-accounting methodologies so that they know the true cost of their products and can make informed costmanagement and pricing decisions.7 Although the foregoing cost-accounting methodologies are quite useful to assist a company in telling the true story about costs, a more pragmatic way to get a handle on costs is to follow the Japanese. That is, it is necessary to take a look at costs before the fact rather than afterwards. More specifically, a Japanese cost-management system guides and motivates planners to design products at the lowest possible cost and gives them considerable freedom in introducing new products as well as getting them to market quickly. Like its famed quality philosophy, Japan’s cost-management system is ahead of its global counterparts. American companies developing a new product, for example, typically design it first and then calculate the cost. If it is too high, the product goes back to the drawing board, or the company settles for a smaller profit. On the other hand, the Japanese start with a target cost based on the price the market is most likely to accept. Then they direct designers and engineers to meet this target. The system also encourages managers to worry less about a product’s cost than about the role it could play in gaining market share. This strategic thinking approach is a big reason why the Japanese so often come out with winning products. The critical feature of the Japanese cost-management system is its focus on getting costs out of the product during the planning and design stage. That is the point at which virtually all subsequent costs are determined, from manufacturing to what customers will have to spend on maintenance. This target-cost technique, which is used by such companies as NEC, Sharp, Nissan, and Toyota, comes in countless variations. The stripped-down version has several important features. The team in charge of bringing a new product idea to market determines the price at which the product is most likely to appeal to potential buyers. From this crucial judgment all else follows. After deducting the desired profit margin from the forecasted sales price, the planners develop estimates for each

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of the elements that make up a product’s costs: design and engineering, manufacturing, and sales and marketing. Each of these is further subdivided to identify and estimate the cost of each component that goes into the finished product. Overall, U.S. companies tend to build a model of the product, determine what it is going to cost, and then ask whether it can be sold at a certain price based on costs. In contrast, the Japanese turn it around. That is, they say, “It’s got to sell for X dollars. Let’s work backwards to make sure we can achieve it.” This is not currently being done by U.S. companies with the same intensity. Westernstyle cost management, by basing costs on given standards, tends to maintain the status quo. The Japanese approach is dynamic and constantly pushes for improvement.8 Applying the Balanced Scorecard Approach The balanced scorecard approach was introduced in the software section of Chapter 7. By way of review, corporate scorecard software acts like a control panel of an airplane by keeping track of a company’s financial progress as well as its softer measurements. The balanced scorecard is proof of the benefits of investing in the long term—in customers, in employees, and in systems—rather than managing the bottom line to spruce up short-term earnings. For years, top management has struggled to use financial-driven performance management systems to achieve operational and strategic goals. Today, the balanced scorecard is a proven approach to strategic management that imbeds long-term strategy into a company’s business intelligence system through the mechanism of measurement. Essentially, a balanced scorecard approach supplements traditional financial measures by measuring the important items that matter to a company and its customers. Important criteria are measured from the perspectives of customers, internal business processes, and learning and growth. The scorecard enables a company to track financial results while monitoring progress in building the capabilities they need for growth. The balanced scorecard was first developed and used by the Nolan Norton Institute, the research arm of KPMG. It uses several evaluation factors that are tightly aligned to the corporate mission. Although this method does use financial comparisons for scoring competing projects, it also includes customer satisfaction, retention, and market and account share information. It considers internal quality issues, like response times, costs, and new product introductions. Also, the model calls for an examination of the learning and growth factors associated with the proposed project, including employee satisfaction and information system availability. Since traditional management systems rely on financial measures, which bear little relation to progress in achieving long-term strategic objectives, the scorecard introduces four processes that help companies connect their long-term objectives to their short-term actions. First, translating the vision helps managers build a consensus around the company’s strategy and express it in terms that

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guide action at the local level. Second, communicating and linking lets managers communicate their strategy up and down the organization and link it to unit and individual goals. Third, business planning enables companies to integrate their business and financial plans. Fourth, feedback and learning gives companies the capacity for strategic learning, which consists of gathering feedback, testing the hypotheses on which strategy was based, and making the necessary adjustments.9 For some information systems professionals, a balanced scorecard report for decision makers sounds like the executive information system cockpit much discussed years ago. The essential items that enable such systems today are more available. In fact, the accession of the Web client enables more people in organizations to access analytical information, which is one of the precepts of the balanced scorecard. A balanced scorecard is kind of the ultimate business intelligence system, since it incorporates the different technologies that have been around for years—data warehousing, OLAP, distributed networking, real-time operations, and middleware—to actually pull all the scores from all the individuals and then consolidate them. Today, large software vendors market this software, but there are some smaller players like Gentia Software that are pushing an independent and lower-cost version. Although there are a number of companies that have the capability of undertaking a balanced scorecard approach, there can be a number of obstacles to overcome for a successful installation. There is an organizational challenge in many respects that is necessary for preparing a company to assess itself using these kinds of measurements and tools. In addition, a company must be committed to it. If these obstacles and others can be overcome, a balanced scorecard approach allows a company to describe and explain metrics that need to be measured to assess the effectiveness of its corporate strategies. In effect, it represents a comprehensive and integrated set of metrics to gauge the health and competitiveness of a company. In turn, these measures provide a standard of comparison between a company and other companies in the same industry or otherwise. Using Accounting and Financial Software to Enhance Financial Intelligence The latest accounting and financial software is designed to accommodate the Internet and the World Wide Web. Recognizing this trend, many software vendors have added features designed to accommodate the Internet. The new functions include the ability to publish Web catalogs directly from, and make links to, the software’s inventory module in real time; retrieve orders directly from the Web site and import them automatically into the sales order module; print all reports to a Web page format; allow users to access reports and accounting data across the Internet; and let remote users securely enter accounting data and transactions via the Web. The accessing of reports via the Web includes linkage to the budgeting process mentioned earlier in the chapter.

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In personal finance and accounting programs, Web links tap rapidly changing information. Block Financial’s Kiplinger TaxCut and similar programs check for and download information about tax changes. Intuit’s Quicken Deluxe 99 Web-Entry and WebConnect features are designed to simplify data entry. Some programs offer collaborative features. BestWare’s M.Y.O.B. Premier Accounting, for example, enables several users to work simultaneously on a single data file. The path between the Internet and accounting and financial software has become a busy two-way street. Some programs make use of data available on the Web; others have been designed to assist users who want to move their own applications on line. There has been a shift in the high-end accounting software offered by software vendors. That is, current accounting systems offer broader functionality and target market niches and vertical industries. In the past, many accounting software packages were developed on an ad hoc basis to fulfill the needs of a particular company. These ad hoc packages were then sold to other similar businesses. In time, more standard core accounting functions were added. Today, the new vertical software is different in that the core accounting functions were there first. When client/server systems became available, application developers designed generic packages that could fit any business. There were some distinctions—the biggest one being between manufacturing and non-manufacturing industries—but generally the intent was for a single product to satisfy the needs of any potential customer. As a result, core accounting applications, such as general ledger, accounts receivable, and accounts payable, are now so similar that many analysts and vendors refer to them as “commodities.” A typical high-end accounting software package is marketed by the Oracle Corporation, called Oracle Financials, Version 11. It uses a Web-based network computing architecture (NCA) user interface. NCA is a “thin client” interface that requires only a Web browser on the networked client. This means that Version 11 runs not only on Windows but also on lower cost, less-powerful dedicated computers, thereby solving the performance problem with thousands of concurrent users. It should be noted that Oracle makes a browser interface the standard for all of its applications. Oracle Financials, Version 11, includes an emphasis on analytical applications and corporate performance management, such as activity-based management (ABM) and the balanced scorecard. Oracle added the former by acquiring PriceWaterhouseCoopers’ ABM software, Activa. A newer feature of accounting and financial software packages is providing analytics. Essentially, analytics enable managers to gain meaningful financial intelligence from the millions or billions of individual transactions stored in a typical accounting software database. Analytics applications typically begin by consolidating the transaction data into a separate database. On-line analytical processing (OLAP) tools may be provided to help users analyze the data, such as comparing profitability across different divisions or product lines. Currently, Oracle is an aggressive analytics provider with its Strategic Enterprise Manage-

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ment, a suite of applications for strategic planning, budgeting and forecasting, and performance management. Going one step further, analytics software includes a balanced scorecard approach. As indicated previously in the chapter, such an approach revolves around monitoring multiple performance measures in a company, instead of looking at the traditional financial measurements. Typical software vendors include CorVu, Gentia Software, Oracle Corporation, PeopleSoft, Pilot Software, SAP AG, and SAS Institute. For example, Pilot Software’s Balanced ScoreCard is a part of Pilot’s Decision Support Suite. It is PC-based and runs under Windows 95, 98, or NT. When users open Balanced ScoreCard, the first thing they see is the overview window. This main screen presents a snapshot view of all measures, using gauges resembling those on a dashboard. Each gauge shows the performance of the current period versus an established target based on exception criteria that the administrator defines. Just below the dial in the gauge is a small circular or triangular icon. This icon indicates whether the performance trend is up, down, or steady. The gauges may also contain a small red arrow pointing to another measure to show causal relationships. The default gauge grouping uses the four categories the Kaplan and Norton methodology specifies, but the user can customize these groupings. When decision makers are under pressure to make critical financial decisions, software such as CFO Vision from SAS Institute is useful for interactive analysis and reporting. It is relatively easy to see product and customer profitability, the potential impact of a reorganization, or key performance indicators. Decision makers can view a business from many angles because CFO Vision is a financial consolidation and reporting software that integrates flexible multidimensional analysis (OLAP). Building upon the concept of multidimensional analysis, there are several vendors whose OLAP offerings feature the drill-down technique in their client/ server applications. Their OLAP software enables users to view multidimensional data and burrow into a data warehouse to extract answers to business questions. The technology goes a long way toward helping users realize the empowerment promised by client/server computing. Vendors such as Dun & Bradstreet Software, Hyperion Software Corporation, Oracle Corporation, SAP America, SAS Institute, and SQL Financials are all marketing OLAP financial software systems. Essentially, OLAP offered today by financial software vendors is built upon the foundation of integrated financial software. That is, the basic data of accounting and finance that is accumulated by a typical integrated financial software package provides the input for on-line analytical processing. Where the accounting and finance data may reside (i.e., in a corporate database or a data warehouse) is not important. The important item is that the integrated financial software has data that is available for massaging using OLAP multidimensional analysis, which can then be reviewed by appropriate accounting and finance managers for action, if deemed necessary. Even though OLAP has improved the way that managers visualize their fi-

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nancial operations, there is still the need for a new focus on discovering financial knowledge about a company’s total operations using some type of computer software. Data mining software is quite capable of fulfilling this task. Banks, for example, may use data mining to identify their most profitable credit card customers or their highest-risk loan applicants. They also may seek to prevent fraud by using a data mining technique called “deviation detection.” Rather than finding relationships between different groups of data records, it finds events that are outside the norm that could be a sign of fraudulent activities. A typical data mining software package that is useful in obtaining financial knowledge is Script Software International’s KnowledgeMiner 2.0. Being a shareware tool for modeling and predicting the behavior of complex systems, this software helps users extract previously undetected patterns of knowledge from sets of data. Its uses include the modeling of economic systems, financial planning, market forecasting, medical diagnosis, or the identification and classification of visual information. In the financial area, KnowledgeMiner 2.0 can be used to find a series of formulas that describe the relationship between a company’s stock price and a variety of other data, including sales figures, product pricing, the gross national product, and the Dow Jones industrial average. From this view, financial managers for a typical company can combine their own knowledge with that obtained from data mining software and leverage this combined knowledge when buying or selling a company’s stock as well as other stocks. From an enlarged view, a company can employ knowledge discovery software not only to undertake various aspects of financial planning (such as investment planning, capital planning, tax planning, and estate planning), but also to identify those customers that will give a high rate of return on the company’s investment. For example, the Capital One Financial Corporation, one of the top 10 credit card issuers in the United States, uses a massive data mining application that works with proprietary algorithms to let the company identify opportunities that promise the highest return at the lowest risk. This is what Capital One refers to as its Information-Based Strategy, and its payoffs are large. The company has created an infrastructure that most notably mines information on 200 million customers. The system churns terabytes of data—for example, buying behavior, payment records and other key statistics—through proprietary algorithms to tailor products down to the individual customer level. The net result of this mass customization is that Capital One can offer 3,000 credit card variations, whereas the industry norm is Gold or Regular.10 No matter the path taken using accounting or financial software, it is helpful for decision makers to deploy financial intelligence that is an integral part of the software. For example, AlphaBlox makes use of a palette that contains objects (blox) that represent a variety of functions associated with business intelligence analytical applications. These objects can be graphs, tables, charts, Java applets, or whatever. AlphaBlox provides a large number of blox objects out of the box, but the user can make up his or her own and have them reside on the

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palette as well. On the right side of the screen, there is a Web-page-in-progress, which can be displayed by any Web page design program (e.g., Front Page, PageMill, etc.). One simply drags and drops one or more of these blox onto the Web-page-in-progress. The net result is that the business intelligence solution becomes a part of the page. In short, as a front-end tool for the rapid development of business intelligence applications or otherwise, AlphaBlox is very easy to use. As the knowledge age unfolds, the World Wide Web will increasingly become the decision maker’s window to the financial intelligence needed to conduct business in these fast-changing times.11 ACCOUNTING FUNCTIONS THAT LEND THEMSELVES TO FINANCIAL INTELLIGENCE Accounting and financial areas that lend themselves to business intelligence are numerous, since there is a need for integrating financial activities with other functional areas for a typical company. The integration starts with strategic planning, followed by marketing, manufacturing, and human resources. The level of integration starts with data, information, or knowledge. No matter the level of integration, the end point is a better understanding of a company’s financial performance in order to improve it. Due to space limitations, only the areas of cost accounting and financial analysis are covered below for a business intelligence system. Not only is a BIS environment at home in the area of forecasting cash flow for the short term to the longer term but it is also helpful in the management of accounts receivables, accounts payables, and payroll (which, today, is linked to a human resource management system). In addition, the source and application of funds, along with financial statements (i.e., balance sheets and income statements), lend themselves to a BIS operating mode. The proper management of accounting functions that can be related to financial intelligence can lead to delivering desired bottom-line results. COST ACCOUNTING Traditionally, cost-accounting systems have been criticized on the grounds that they do not provide useful information and knowledge for internal decision making. An important reason given is that the systems were designed to provide cost data for inventory valuation in financial statements rather than for cost management. Other reasons were given in the prior discussion on the rethinking of cost-accounting approaches. More recently, executive information systems, OLAP systems, and knowledge management systems provide better costs analyses that are complementary to newer cost-accounting approaches. Although these systems are helpful in getting a handle on a company’s true costs, they lack the capacity to provide a true understanding of a company’s costs. A BIS operating mode goes a step further by assisting financial managers in a better

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understanding and evaluating cost-cutting changes that could possibly result in a company going broke to save money. Newer View of Cost-Accounting Approaches To provide intelligence for cost management, there must be an enlarged view of cost-accounting approaches so that they can be flexible enough to analyze costs, including inventory valuation, product costing, life cycle costing, overhead analysis, quality cost evaluation, direct and indirect labor analysis, and productivity analysis. A comprehensive accounting system for supporting cost management must include various types of data, most of which is generated internally. At the most basic level, the data consists of the activities causing the costs (i.e., the cost drivers) and the costs incurred. For example, receiving department costs may be caused by the number and size of the deliveries, along with inspection and handling times of these deliveries. If the receiving department’s costs are to be apportioned to the products causing the costs, the cost drivers must be identified and used to trace costs to specific products. When this is done, the cost-accounting system becomes activity based (as discussed previously in the chapter) rather than volume based (like most current cost systems). One means of developing an activity-based system is to use a database approach, which means recording events, activities, and costs in a computer database in enough detail so that they can be retrieved, analyzed, and summarized in multiple ways as the need arises. Such an approach in reality involves recording some information not required for financial reporting, such as the numbers of deliveries and purchase orders and the number and times of inspections and handling. However, the database of costed activities not only supports the cost-attachment process for product costing but it also provides a useful tool to support planning, coordination, and control across multiple dimensions. In addition, activity-based costing provides for an in-depth analysis of changing costs. An ABC approach not only assists cost management in understanding costs, but also helps to visualize via multidimensional analysis how costs affect a company’s financial operations. Activity-based costing gives cost management the look and feel of an alternative world. More to the point, costs can be analyzed by cost accountants for a number of products to determine what effect the elimination of certain costs, such as costly training of machine operators or assembly operators, would have on the company. Perhaps senior personnel could act as mentors to eliminate or reduce training costs. Similarly, inspection of assembly operations may be better left to assembly personnel where production-line personnel are permitted to shut down production if there are quality problems. Changes in production activities and their resulting costs, then, are related to the company’s bottom line. From an enlarged perspective, an activity-based cost-accounting approach can employ the knowledge gained from data mining over time. This knowledge can

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help management and cost accountants determine whether costs incurred in production or non-production activities really benefit the company over the long haul. From this view, a true cost-benefit analysis can be undertaken not only for manufactured products, but also for costs related to the marketing and distribution of products and product lines. Thorough analysis gives cost accountants the capability to uncover new relationships that were previously buried in cost figures and to understand them thoroughly. A business intelligence system that employs data mining tools, then, can be used for uncovering costs for different purposes, thereby highlighting problems and exceptions that were ignored or neglected in the past. Accent on Target Costing to Lower Costs Related to the above discussion about cost-accounting approaches is target costing. It represents a powerful force that shifts from cost-led pricing to target costing. Traditionally, companies have started with costs, added a desired profit margin, and arrived at a selling price (i.e., cost-led pricing). Wal-Mart, Sears, and Marks & Spencer, as examples, have switched to target costing, in which the price the customer is willing to pay determines allowable costs that start at the design stage. Until recently, those companies were the exceptions. Now target costing is an accepted practice. The Japanese first adopted it for their exports. A recent study of seven Japanese companies reveals that the cardinal rule these companies use is that target costs should not be exceeded. They enforce this rule in three ways: (1) by offsetting design improvements that result in increased costs with savings elsewhere in the design, (2) by not launching products that exceed the target cost, and (3) by carefully managing the transition to manufacturing in order to achieve the target cost.12 Because target costing is a cost-management technique that lets a company determine how much its customers are willing to pay for a product and then design the product within certain cost limits that allow for a predetermined profit, multidimensional analysis is quite helpful to company managers throughout a company to get a handle on pricing and costing the product. For example, a marketing manager, working with a senior cost manager, can explore the relationship of pricing the product at various levels based upon making the product internally at various costs throughout the marketing and manufacturing processes. In addition, this analysis can be extended to include the outsourcing of the product. Hence, company managers are able to undertake the appropriate pricing-costing analysis for a definitive answer about the product being produced internally or externally. Although target costing has made its mark on industries in which products require a significant amount of production time, it is equally applicable to services for which the focus is the service delivery system. As in process-intensive manufacturing, process is inextricably linked to product. Think of the issues that are important to the delivery of health care and fast-food functions. Where serv-

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ices and process-intensive manufacturing divergence is in their flexibility. That is, it is very costly to convert a paper machine so that it can produce a grade or weight that was not considered in its initial design. On the other hand, service delivery systems are a different matter. In customer-responsive service delivery systems, it is easy to add new services. For example, menus are easy to extend and room services can easily be added. Overall, target costing is equally at home in service-oriented or manufacturing-oriented industries.

Determine the Appropriate Costing Method Once the costs of a company’s future products have been properly determined by a cost-management technique, such as target costing, the next step is accumulating costs once the products are manufactured. One approach to cost control or containment is direct costing, which segregates variable costs from fixed costs (as opposed to absorption costing, which combines them). As such, selling price less variable costs equals contribution to fixed costs and profits before federal income taxes. This value of contribution is then subtracted from fixed costs, thereby resulting in net profits before federal income taxes. Direct costing is concerned primarily with the elimination of arbitrary allocations of common or joint costs. It emphasizes the benefits of tracing costs to individual cost centers and then measuring them. To realize the benefits of direct costing, a cost-review group is typically assigned the responsibility of evaluating product costs and their contributions. To better understand direct costing from a financial intelligence viewpoint, reference can be made to a typical company’s five principal products, which are set forth in Figure 10.2(a). In turn, these values are graphed in Figure 10.2(b). Their new profit amounts and percentages of net profit are ranked for the company’s five principal products as follows:

Finally, pie charts, as shown in Figure 10.3, portray variable costs, fixed costs, and net profit before federal income taxes for the company’s best product and worst product in terms of net profit percentages. With a segregation of variable and fixed costs to determine contribution, the company’s managers are better able to view important information regarding which products really contribute to overall net profits before federal income taxes. In addition, the total contribution of each of the company’s products can be calculated. It may well be that this cost information, which is reviewed by lower-level managers, should be

Figure 10.2 (a) Direct Costing Amounts for a Company’s Five Principal Products and (b) Graph That Compares a Company’s Five Principal Products

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Figure 10.3 A Pie Chart Comparison for a Company’s Best and Worst Products in Terms of Percent of Sales

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forwarded to top management for review. There may be a changing pattern among the products in terms of total contribution. This capability of the costreview group, which works with experienced cost accountants, allows the company to detect changing cost trends for a better understanding of its total operations. Another approach to cost control is the ABC method set forth previously, where the focus is on separating value-added costs from non-value-added costs. With this segregated cost information, company managers can determine what non-value-added activities can be reduced or possibly eliminated. As an example of effective activity-based costing, consider a contractor that changed its costing/ quoting process to more closely reflect the actual cost structure of the company. This has enabled the company to improve the management of its contracts. Better costing and quoting were not the only features of the ABC method that improved the company’s profitability. ABC highlighted many areas for cost control that lay hidden under the traditional costing system, like isolating and measuring the cost of material movement. Since this information was developed, the company has utilized it to justify many operational changes to realize further efficiencies. On an important but less tangible level, management’s knowledge of and attitude toward cost justification have undergone a positive change. Where once managers had their own way of measuring the cost impact of management actions, they now measure those costs in a formal, uniform way. When managers contemplate changes, they have a mental model that directs them toward changes that truly benefit the organization.13 Although direct costing and the ABC method are ways of getting at the current and future company costs, there is a need to go a step further in these fastchanging times and take a comprehensive approach. American companies need to rethink how they manage costs over their operations. That is, they need to assure the cost department that what they are doing is worth doing. The end result is that company management will get better ideas on effective costing methods that are best suited to the company’s needs. Sometimes, this means making radical changes and setting dramatic targets, whether in costs or otherwise, based on knowledge of global trends and patterns. There may be a need to reengineer products not processes based on ongoing knowledge of world markets, since product design can account for as much as 80 percent of total production costs. When it comes to product design, it is not what is built into the product but what can be taken out of it. The Japanese, for example, have been currently doing this to their cars in a big way. Basically, simplicity cuts costs and saves time. In addition, there should be a focus on giving customers less, not more, variety, which can result in lower prices. Thus, financial intelligence in the area of costing is useful in assisting decision makers to lower a company’s costs as well as improve organizational productivity.

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FINANCIAL ANALYSIS From an enlarged view, a company needs to have the capability to focus on a thorough analysis and understanding of key performance indicators and financial ratios that tie back to its critical success factors. A thorough analysis is even more important today in these fast-changing times. A broadened view within a BIS environment integrates planning or budgeting with performance reporting and is based on knowledge of cost behavior (as discussed in the prior section) and recognizes the significance of flexible budgeting and costing methods to improve the quality of financial intelligence provided for all managerial levels. It is from this perspective that financial analysis is explored below. Basically, for a typical company financial performance and its resulting evaluation in the form of critical success factors, key performance indicators, and financial ratios is related to short-range strategic planning, which forms the basis for monthly flexible-department financial budgets (as noted in Chapter 7). Actual results are compared monthly with departmental financial figures. If actual operations are within 5 percent (plus or minus) by individual expense categories, there is no need for action. However, if the amount exceeds the 5 percent limit, appropriate action is undertaken. In certain cases, it may be helpful for management to bring the knowledge of past experience to bear on the evaluation. It should be recognized that a financial planning and performance system provides for individual managers to be assigned responsibility and, in turn, to be held accountable for meeting stated budgetary finance objectives. The purpose of the discussion below is to give a better understanding of a company’s financial statements that center on balance sheet and income statement analysis. The various analyses include the use of the Internet and the World Wide Web as well as data warehousing as a means of bringing more parties (whether they are inside or outside the company) into the financial decision process. Where appropriate, knowledge discovery tools to assist accounting and financial managers in overseeing investments are explored. Providing a Better Understanding of Present and Future Financial Operations To better understand present and future financial trends within a BIS operating mode, a logical starting point could be cash flow forecasting. For example, Cash Plan Pro (designed for small businesses), which offers a relatively simple, stepby-step approach to gathering and analyzing financial data, is available from Palo Alto Software. This software package looks at a company’s major account balances, primary costs and expenses, inventory, accounts payable, and accounts receivable and corresponding collection days. Cash Plan Pro presents a series of screens containing mini-spreadsheet tables. A text box provides an overview, and the user can click on tabs to see either a prebuilt example or instructions for each row. Several charts are generated automatically to provide financial

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feedback based on the data entered in the tables. These include Cash versus Profits, Cash Forecast, and Profitability Forecast. Some of the data that must be entered can be imported directly from Palo Alto’s Business Plan Pro and Intuit’s QuickBooks. The Cash Pilot lets the user alter specific variables and compare the results to the actual situation, analyzing these hypothetical possibilities without actually changing the original plan. Cash flow forecasting is vital to any size company, and it is necessary to tie in cash forecasts with a company’s short-term financial plans (i.e., budgets for the coming year). Today, this combined approach comes under the heading of rolling forecasts. For example, a company can perform a rolling four-quarter review twice a year, instead of a calendar-year exercise. The rationale is that a company does not cease operations at the end of the calendar year, so why should the planning exercise? In a similar manner, forecasts can be completed before a quarter is over, taking pressure off finance, which is typically tied up closing its books. Typically, such continuous forecasts revolve around a number of performance measures (i.e., periodic budgets as well as KPIs and financial ratios). Related to financial forecasts is an understanding of past and current operations in terms of sales, costs, and profits. More specifically, not only can total sales and related costs and contributions be illustrated over five years but the net profit before federal income taxes can also be shown for the same time periods. To illustrate, reference can be made to Figure 10.4(a) for a company’s current year and past years. A graphical comparison of these sales data to net profit before federal income taxes is found in Figure 10.4(b). In turn, these data can be ranked in terms of dollar amounts and percents as follows:

Appropriate pie charts for the company’s best and worst years in terms of amounts and percent of sales are found in Figure 10.5. Since year 4 is best, this indicates that there is room for improvement in the current year. This analysis needs to be further refined over five years for rising and falling KPIs and financial ratios that tell more of the total story about the company’s future—good, bad, or indifferent. In addition, analysis of the current year can be undertaken by increasing prices and decreasing variable costs as well as by decreasing prices and increasing variable costs to see the effect on contribution. In a similar manner, fixed costs can be reduced as well as increased to see the impact on net profits before federal income taxes. The profit trend line can be favorably influenced by in-

Figure 10.4 (a) Amounts for a Company’s Current Year and Past Years and (b) Graph That Compares a Company’s Current Year to Past Years

Figure 10.5 A Pie Chart Comparison for a Company’s Best and Worst Years in Percent of Sales

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creasing the sales price of the product, decreasing the variable costs, or improving a product mix. Change in the product mix can be helpful when several products are involved if increased concentration is obtained on products with high contribution. Changes of these factors in the opposite direction will naturally have an unfavorable influence on net profit before federal income taxes. In a similar manner, the same type of logic can be applied to fixed costs and net profit before federal income taxes. Going a step further, comparable financial information and knowledge and, in turn, appropriate KPIs and financial ratios can be developed. The intended purpose of these analyses by company financial managers is to determine whether or not the company is improving its financial stature. Of equal importance is the fact that financial information and knowledge discloses whether or not company managers are really managing effectively over the shorter term. Generally, this type of “at a glance” multidimensional analysis allows managers to start their thinking processes quickly. KPIs and financial ratios over a longertime frame may indicate changing patterns and emerging trends that need to be explored by top management and the corporate planning staff. The analysis may indicate the need for problem finding to get the company back on track. In addition, the analysis may signal the urgency for the company managers to change their financial understanding as now constituted to a more global perspective that includes the emerging nations of the world. From this view of changing world intelligence, it may be necessary for a company’s top management and its staff to go back and ask questions about the company’s critical success factors. Although current CSFs are the guiding force for top management, this may not be the case for tomorrow. Hence, financial results that are produced by a business intelligence system may indicate a change is necessary in the overall direction, including changing its CSFs. It should be noted that the dissemination of financial results can be extended to the far reaches of a typical company. With the employment of World Wide Web technologies and the acceptance of the Internet as a primary vehicle for distributing business intelligence, there is an opportunity to maximize the return on investment in corporate information and knowledge. It is now possible to deliver reporting capabilities cost effectively to every user, from senior financial executives and production line workers to sales forces and branch offices across an entire organization and beyond to customers, suppliers, and other business partners. In addition, distributing information and knowledge on the status of a company’s operations to all levels via the Internet and the World Wide Web delivers the means to improve operations by adding value that goes beyond simple status reviews. For example, decision makers can access financial reports through the World Wide Web using the Actuate Reporting System (from Actuate Software Corporation). This approach eliminates the need for traditional dial-in lines to retrieve reports. The Actuate Reporting System not only utilizes the Web component but also provides for connectivity to on-line multiple servers as well as off-line type systems. This type of solution allows users to measure

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and better understand business performance rather than simply review the status of business measures. In addition, this reporting approach offers report formats that support business measurement activities by presenting information and knowledge in a manner that matches what decision makers are trying to accomplish: uncovering problems plus discovering patterns and trends. Making Sense Out of Financial Statements to Improve Operations To better understand and improve financial performance, accounting and financial managers need to look behind the figures found in a typical balance sheet and income statement. The balance sheet is a snapshot of the company’s financial structure. This includes how funds (assets) are being used and where the funds come from (liabilities and equity) at a given moment coinciding with the income statement’s end date. What the balance sheet measures is the financial efficiency of the company using the same industry data and standards applied to measure and compare performance. One of the most critical ratios to monitor is the current ratio—current assets divided by current liabilities. This ratio measures the adequacy of the company’s working capital and its ability to meet its short-term obligations. If the current ratio is less than one, then the company is clearly in trouble. Here again, industry standards are used to benchmark the most appropriate current ratio. In some industries, it may be 1.5, in others 2.0. A more stringent test of liquidity is the acid test ratio. Instead of dividing total current assets by total current liabilities, subtract inventory from current assets and then divide by current liabilities. For most industries, the standard is approximately 1.0. Another key measure is the debt-to-equity ratio. This measures how the company’s finances are structured. A ratio of 3 usually indicates that a company is using an appropriate mix of debt and equity to finance its operations. For most industries, a ratio of 4 indicates that it has reached its borrowing capacity without an additional equity injection. If the company has no equity, or even worse, negative equity, then its survival is highly unlikely. In contrast, the income statement measures performance for some period of time—a month, a quarter, or a year. When measuring income statement performance, it is crucial to remember the value of 1 percent. For example, one percent of $1 million is $10,000. In a $1 million business, if costs increase a total of 5 percent without a comparable price increase, $50,000 just disappeared from the bottom line. The first key indicator in the income statement is COGS (cost of goods sold). If COGS is less than the industry average, then gross profit margin (the difference between sales and cost of goods sold) will be higher, an indication that the company has either priced its products and services better or controlled costs better than others in the industry. If the reverse is true, then there is a need to work on pricing, cost control, or both. The second indicator is labor cost. It is necessary to match or be less than the industry standard. In

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today’s tight labor market, though, many small companies are having difficulty controlling their labor costs. One solution is to design more efficient ways of serving customers to avoid adding more employees and to help offset the rising cost of existing staff. Other key indicators on the income statement include marketing costs, insurance, utilities, transportation, and communications. Other indicators of financial success can be found earlier in the chapter under financial ratios. These include calculations that make use of the balance sheet and the income statement. For example, to measure a company’s performance and efficiency, the sales to total assets ratio is calculated by dividing annual net sales by year-end total assets. This gives a measure of how efficiently the company is employing its assets. Overall, financial ratios provide an insightful look for measuring actual results against the corporate strategic plans (short range to long range). Management effectiveness can be measured by the capital turnover ratio, asset profitability, ROI, and EVA. In addition, the company’s performance can be gauged through the return on sales, days of sales in backlog, inventory utilization, and receivable collections. For some companies, getting at the data needed to develop appropriate financial ratios is difficult. The process of gathering data, cleansing it, validating it via programmatic means, or even by the very nature of multiple business areas, and accessing it as a source of information is often a disconnected series of events. On the other hand, data warehousing strives to put a framework around the process that can be managed at the highest level. For example, Pride International (a large drilling contractor in Houston, Texas) decided that a data mart solution would meet the company’s short- and long-term business requirements for global information consolidations and sharing. The company knew that data warehousing would require careful architectural planning and extensive development work to be successful. Building such systems from scratch is a massive undertaking, which is why so many companies are turning to packaged data mart solutions. So, instead of creating a new system, the company concentrated on finding a packaged data mart solution that could be quickly linked to the J. D. Edwards OneWorld financial system already in place at Pride. Information Builders’ WorldMART was selected, since it best met Pride’s business requirements. Basically, WorldMART is an integrated set of tools for data access, data transformation, migration, and reporting that Information Builders created expressly for the J. D. Edwards application environment. In fact, the WorldMART analytical application was designed in partnership with J. D. Edwards and uses a financial data model provided by J. D. Edwards to populate the data mart. At Pride, the next step was working with executives to build into WorldMART the ability to track operations globally. This step helped them create a scorecard for their business that also let them track key information on corporate finances, human resources, equipment, and projects over time.14 The balanced scorecard approach for a company like Pride International is an effective means to measure and monitor organizational performance. It enables

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the assignment of KPIs and provides the ability to track and optimize performance based on those idicators. KPIs are measured based on a set of metrics that consider multiple interdependent perspectives, and they help organizations balance their focus on more than just the bottom line. This approach ensures that customer service, employee satisfaction, and sales and marketing are weighted appropriately, resulting in well-rounded and successful companies. Until recently, however, a balanced scorecard was limited in the level of intelligence it provided and the degree to which it enabled drill down of presented intelligence for the purpose of a very detailed analysis. The data warehouse has virtually eliminated this limitation. The combination of the balanced scorecard and data warehousing is capable of achieving an efficient and accurate performance analysis and management. The combined technologies enable companies to balance their resources and manage their business functions according to process and key performance indicators. The data warehouse is the effective infrastructure that supports the performance management process, and it provides a means for collecting and storing the data. Marrying balanced scorecard and data warehousing technology provides the decision maker with the ability to drill down on data delivered to his or her desktop by the balanced scorecard. The data warehouse provides companies with the ability to compare and analyze identified KPIs against actual data, thereby allowing for benchmarking and performance improvement tracking. Essentially, a data warehouse–supported balanced scorecard system enables organizations to monitor their progress continuously. Using Knowledge of Competitors to Enhance a Company’s Performance A most important reason for reviewing a company’s financial performance via key performance indicators and financial ratios, as just noted, is to give direction to the company’s top management and its corporate planning staff. By evaluating external environmental factors as related to internal ones, a typical company can compare itself in terms of growth and profitability to comparable firms in the industry using multidimensional analysis as a starting point and then further refine the process using knowledge of competition. For example, one competitor’s current financial position can be compared to the company’s own financial picture plus those of other competitors. This financial analysis gives an indication of whether or not this one competitor is giving a fair return to its stockholders when considering the industry’s current status. This overview of financial ratios needs to be supplemented by a more detailed analysis of competitors’ financial statements, frequently referred to as content analysis of periodic financial statements and annual reports. Also, knowledge of competing products and services, including their marketing philosophy, is very important in the analysis.

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Basically, content analysis provides valuable clues to competitors’ corporate strategies. Content analysis of competing companies, along with their marketing policies and their future marketing directions, can be very useful for understanding specific issues of corporate strategy and can serve as a primary or supplementary source of information and knowledge. It can be used to analyze current changes and past correlates of performance and for more general investigations of potential future directions that are of interest to top-level executives and their corporate planning staffs. For example, content analysis may disclose that one of the company’s competitors is showing improved cost performance—that is, its costs are declining. The appropriate corporate response for a typical company is to get involved in a cost-analysis technique called benchmarking, which focuses on what the competitor does and how much it costs to do it. In the company’s lab, analysts tear apart the competitor’s products and estimate the cost of designing and producing each part. The analysis extends beyond product costs. To pin down distribution and handling costs, company executives need to order some of its competitor’s products, then trace where they were shipped from and examine how they were packed. Typically, cost savings on a particular product start at the earliest stage, with engineering determining the product design. The challenge is to find ways to make engineering more cost effective without stifling creative efforts. From another perspective on benchmarking, using the activity-based costing method, a decision maker can transform dollar amounts and priorities assigned to projects into colorful, interactive virtual worlds that can be explored. As the individual works with one budgeted item or changes an assignment’s priority, a ripple takes place in real time. That is, the decision maker can explore how money is spent at all levels of the company and can make better decisions on how funds are spent. In such cases, virtual reality (VR) software is useful to create a walk-through scenario of how a company spent its money. Particular attention is given to flagging low-priority activities that were overfunded. Activities are assigned tall or short bars by their spending levels, and color-coded from green to red, to show priority. Exploring this 3-D landscape, managers are able to determine which activities to view, touch, and manipulate. For example, they can focus on tall red bars, indicating low-priority activities with high costs. Touching these bars activates detailed information about the resources consumed. At any point, they can step back to see how resources were consumed for each high-level business process and for the entire company. Hence, VR can be a very effective means to assist in benchmarking because it uses size, shape, and color in a 3-D landscape to convey useful knowledge that has been highlighted from extensive analysis. Similarly, VR has the potential to turn a company’s knowledge about its customers into a giant simulated structure of itself and its competitors that is much easier for managers and their staffs to understand.

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Using the Internet to Discover Appropriate Financial Intelligence Whatever service is chosen for getting on the Internet, an individual or a company can discover a vast array of financial information and knowledge and, in turn, financial intelligence. For example, an investor or a financial manager for a company can keep tabs on various stocks by way of a service called Portfolio Accounting World-Wide (Pawws). The individual can click the mouse to get the latest quotes, or, by clicking a second time, see a company’s most recent financial statements or take a look at how its stock price has moved over the past several years. Currently, brokerage houses, investment banks, and discount brokers are offering a wide range of on-line financial services. Companies like Fidelity, Merrill Lynch, J. P. Morgan, and Charles Schwab have opened up Web sites for their investors. In addition, there are a number of on-line sources, including the Microsoft Network and specialists such as the Reuters Money Network and Dow Jones News/Retrieval. A starting place for an individual investor or company includes financial bulletin boards and investment forums. These popular gathering places are where users talk shop, exchange advice, and interact on line with a variety of Wall Street gurus. However, there is a word of warning. Investor forums are fertile ground for operators who talk up cheap, thinly traded stocks, thereby hoping to create enough buy orders to boost the price for a few days. Then they sell their holdings and leave everyone else holding issues that have little or no market. Nevertheless, if individuals have the ability to overcome this caveat, they can discover new knowledge about financial markets and services offered to best meet their needs whether operating independently or acting on behalf of a company. Going one step further and tying in with the earlier discussion in the chapter on CSFs, KPIs, financial ratios, and content analysis, important comparisons and evaluations of the information found on the Internet coupled with upcoming economic events can allow a person to discover important financial knowledge that was not possible in past years. From a broadened perspective, a more enlightened investment approach using financial intelligence can prove to be highly rewarding for the individual investor or company. EFFECTIVE FINANCE BIS APPLICATION—WELLS FARGO BANKS An overload of data led San Francisco–based Wells Fargo Bank to install a BI solution. A record of every customer transaction goes into the bank’s fiveterabyte Oracle data warehouse, which runs on a Sun E10000 server. To use this information effectively, Wells Fargo is assembling a portfolio of BI tools, ranging from ones that offer the user a fixed set of algorithms and modeling routines to tools that require more training and sophistication (but allow a greater range of user intervention). All have a common purpose: discovering connec-

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tions and relationships in the data. The bank has selected the Insight relational OLAP tool from Brio Technology as one data modeling tool. For its most sophisticated users who prefer a customizable tool, the bank has chosen NeoVista Software’s Decision Series, and it is currently evaluating SAS Institute’s Enterprise Miner to make many of those same routines available to users more interested in a simple interface and less concerned with customizing the algorithms. These BI tools give the bank’s business analysts instant access to clustering algorithms, decision trees, neural networks, and other advanced mathematical tools that perform complex analyses of customer behavior. Analysts can examine which products customers are likely to use and when they are likely to need them.15 EMPLOYING CPAS AS A FINANCIAL INTELLIGENCE RESOURCE In the prior exposition on applying financial intelligence to business, an underlying assumption was that accounting and finance personnel at all levels can benefit from it. However, before this can happen, there is a need for a change in mindset of these professionals. For example, certified public accountants (CPAs) should think of themselves not so much as accountants—those who balance the books and prepare financial statements—but as financial intelligence professionals. They need to learn who needs the financial information and in what form, and build a computer infrastructure so that it is readily available throughout the organization via the company’s intranet and, if appropriate, connected to the Internet and the World Wide Web. They should recognize that while financial information has always been power, in today’s fast-paced and highly competitive business world, speedily delivered financial intelligence in the hands of decision makers often can mean keeping one step ahead of the competition, at best. CPAs need to determine the departments in the company that need what can be supplied, thereby thinking of themselves as partners to these departments. Their job is to knit together the basic elements of decision making: strategic planning, forecasting, budgeting, benchmarking, and so on. In other words, by integrating these functions, CPAs become principal drivers of the financial decision process at the highest level of a company. Based on their job as change agents to better utilize financial intelligence, CPAs are in an excellent position to develop the appropriate metrics for a company’s knowledge assets. New approaches will be needed to measure the “knowledge flow” (i.e., return on knowledge, or ROK). These approaches will be somewhat comparable to the way traditional accounting methods determine a company’s cash flow and return on investment. Several firms have made attempts at knowledge by the numbers but there are no set formulas at this time. These new approaches go beyond the old adage: “If it ain’t broke, don’t fix it.” A more contemporary focus is: “If it’s too old and not keeping up with the current times, fix it whether it’s broken or not.” Thus, certified public account-

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ants have a great opportunity at their doorsteps. If they understand a company’s financial performance and convey this intelligence to the appropriate parties, a typical company can reap enormous benefits from their efforts. SUMMARY This chapter has focused on the application of financial intelligence to accounting activities. The initial focus was on the forward and backward integration of financial operations, followed by accounting factors that lend themselves to financial intelligence. These newer directions are necessitated by the current environment, which can be characterized as highly dynamic, complicated, and knowledge intense. To illustrate how financial operations can be streamlined for the times, the areas of cost accounting and financial analyses were presented within a BIS operating mode. Decision makers in these areas were given new insights into their operations from a short-term to a long-term perspective. Hence, continuing changes based on financial intelligence in these areas as well as other accounting and financial areas can be made on a more sound basis. NOTES 1. Robert J. Thierauf, Executive Information Systems: A Guide for Senior Management and MIS Professionals (Westport, CT: Quorum Books, 1991). 2. Jeetu Patel, “E-Market Models Matter,” Information Week, August 16, 1999, p. 102. 3. Francis Gaither Tucker, Seymour M. Zivan, and Robert C. Camp, “How to Measure Yourself Against the Best,” Harvard Business Review, January–February 1987, pp. 8– 9. 4. “Fast-Cycle Benchmarking,” Harvard Management Update, a newsletter from Harvard Business School Publishing, April 1999, pp. 1–4. 5. Steve Alexander, “Is It Knowledge Yet?” Computerworld, January 8, 1996, p. 73. 6. David Baum, “Gulf Canada Improves Budgeting Process with Web-Driven Reporting,” Information Builders News, Fall 1998, pp. 19–22. 7. Dennis Peavey and Jim DePalma, “Do You Know the Cost of Your Products?” (New York: Coopers & Lybrand Executive Briefing, May 1990), pp. 7–9. 8. Ford S. Worthy, “Japan Smart Secret Weapon,” Fortune, August 12, 1991, pp. 72– 75. 9. Robert S. Kaplan and David P. Norton, “Using the Balanced Scorecard as a Strategic Management System,” Harvard Business Review, January–February 1996, pp. 75– 85. 10. Jane Morrisey, “Mining for Gold,” PC Week, May 19, 1997, p. 137. 11. Michael Burwen, “AlphaBlox: BI in LEGOLAND,” DM Review, September 1999, p. 76. 12. Robin Cooper and Regine Slagmulder, “Develop Profitable New Products with Target Costing,” Sloan Management Review, Summer 1999, pp. 23–33. 13. Douglas T. Hicks, “Yes, ABC Is for Small Business, Too,” Journal of Accountancy, April 1999, pp. 41–43.

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14. “WorldMART Data Mart Enriches Pride International’s Financial Analysis,” Information Builders News, Winter 1999, pp. 22–25. 15. Samuel Greengard, “How to Profit from Business Intelligence,” Beyond Computing, January–February 1999, p. 29.

Selected Bibliography DECISION SUPPORT SYSTEMS Bennett, J. L., ed. Building Decision Support Systems. Reading, MA: Addison-Wesley, 1983. Bodily, S. E. Modern Decision Making: A Guide to Modeling with Decision Support Systems. New York: McGraw-Hill, 1985. Bonczek, R. H., C. W. Holsapple, and A. B. Whinston. Foundation of Decision Support Systems. New York: Academic Press, 1981. Davis, M. W. Applied Decision Support. Englewood Cliffs, NJ: Prentice-Hall, 1988. Gray, P., and H. Watson. Decision Support in the Data Warehouse. Upper Saddle River, NJ: Prentice-Hall, 1998. Hawgood, J., and P. Humphreys, eds. Effective Decision Support Systems. Aldershot, Hants, England: Technical Press, 1987. Heymann, H. G., and R. Bloom. Decision Support Systems in Finance and Accounting. New York: Quorum Books, 1988. Holsapple, C. W., and A. B. Whinston, eds. Decision Support Systems: Theory and Application. New York: Springer-Verlag, 1987. Keen, P.G.W., and M. S. Scott Morton. Decision Support Systems: An Organizational Perspective. Reading, MA: Addison-Wesley, 1978. Leigh, W. E., and M. E. Doherty. Decision Support and Expert Systems. Cincinnati, OH: South-Western, 1986. Lotfi, V., and C. C. Pegels. Decision Support Systems for Production and Operations Management. Homewood, IL: Richard D. Irwin, 1986. McCosh, A. M., and M. S. Scott Morton. Management Decision Support Systems. New York: John Wiley & Sons, Halsted Press, 1978. McLeod, R., Jr. Decision Support Software for the IBM Personal Computer. Chicago: SRA, 1985.

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Selected Bibliography

Scott, Morton, M. S. Managerial Decision Systems: Computer Support for Decision Making. Boston: Harvard University Press, 1971. Sprague, R. H., Jr., and H. J. Watson. Decision Support Systems: Putting Theory into Practice. Englewood Cliffs, NJ: Prentice-Hall, 1986. Thierauf, R. J. Decision Support Systems for Effective Planning and Control: A Case Study Approach. Englewood Cliffs, NJ: Prentice-Hall, 1982. ———. Group Decision Support Systems for Effective Decision Making: A Guide for MIS Professionals and End Users. Westport, CT: Quorum Books, 1989. ———. User-Oriented Decision Support Systems: Accent on Problem Finding. Englewood Cliffs, NJ: Prentice-Hall, 1988. Turban, E. Decision Support and Expert Systems, 2nd ed. New York: Macmillan, 1990.

EXECUTIVE INFORMATION SYSTEMS Below, P. J., C. L. Morrisey, and B. L. Acomb. The Executive Guide to Strategic Planning. San Francisco: Jossey-Bass, 1987. Hickson, D. J. et al. Top Decisions: Strategic Decision Making in Organizations. San Francisco: Jossey-Bass, 1986. Rockart, J. F., and D. W. DeLong. Executive Support Systems: The Emergence of Top Management Computer Use. Homewood, IL: Dow-Jones Irwin, 1988. Thierauf, R. J. Executive Information Systems: A Guide for Senior Management and MIS Professionals. Westport, CT: Quorum Books, 1991.

EXPERT SYSTEMS Bowerman, R. G., and D. E. Glover. Putting Expert Systems into Practice. New York: Van Nostrand Reinhold, 1989. Chorafas, D. N. Applying Expert Systems in Business. New York: McGraw-Hill, 1986. Gallagher, J. P. Knowledge Systems for Business: Integrating Expert Systems and MIS. Englewood Cliffs, NJ: Prentice-Hall, 1988. Harmon, P., and D. King. Expert Systems: Artificial Intelligence in Business. New York: John Wiley & Sons, 1985. Harmon, P., R. Mares, and W. Morrisey. Expert System Tools and Applications. New York: John Wiley & Sons, 1988. Hertz, D. B. The Executive Using Artificial Intelligence for Financial Management, Marketing, Production, and Strategy. New York: John Wiley & Sons, 1987. Holsapple, C. W., and A. B. Whinston. Business Expert Systems. Homewood, IL: DowJones Irwin, 1987. Klahr, P., and D. A. Waterman. Expert Systems: Techniques, Tools, and Applications. Reading, MA: Addison-Wesley, 1986. Leigh, W. E., and M. E. Doherty. Decision Support and Expert Systems. Cincinnati, OH: South-Western, 1986. Thierauf, R. J. Expert Systems in Finance and Accounting. Westport, CT: Quorum Books, 1990.

Selected Bibliography

353

IDEA PROCESSING SYSTEMS Couger, J. D. Creativity & Innovation in Information Systems Organization. Danvers, MA: Boyd & Fraser, 1996. Davenport, T. H. Process Innovation. Boston: Harvard Business School Press, 1993. Evans, J. R. Creative Thinking in the Decision and Management Sciences. Cincinnati, OH: South-Western, 1991. Henry, J., ed. Creative Management. Newbury Park, CA: Sage Publications, 1991. Kao, J. Jamming: The Art and Discipline of Business Creativity. New York: HarperBusiness, 1996. Martin, A. P. Think Proactive: New Insights into Decision Making. New York: Professional Development Institute, 1984. Miller, W. The Creative Edge. Reading, MA: Addison-Wesley, 1987. Nierenberg, G. I. The Art of Creative Thinking. New York: Simon & Schuster, 1982. Parnes, S. J., R. B. Noller, and A. M. Biondi, eds. Guide to Creative Action. New York: Scribner, 1977. Rubinstein, M. F. Tools for Thinking and Problem Solving. Englewood Cliffs, NJ: Prentice-Hall, 1986. Sandler, B. Z. Computer-Aided Creativity: A Guide for Engineers, Managers, and Inventors. New York: Van Nostrand Reinhold, 1994. Schoennauer, A.W.W. Problem Finding and Problem Solving. Chicago: Nelson-Hall, 1981. Thierauf, R. J. Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals. Westport, CT: Quorum Books, 1993. ———. A Problem-Finding Approach to Effective Corporate Planning. Westport, CT: Quorum Books, 1987. Van Gundy, A. Creative Problem Solving: A Guide for Trainers and Management. Westport, CT: Quorum Books, 1987. Young, L. F. Decision Support and Idea Processing Systems. Dubuque, IA: William C. Brown, 1989.

KNOWLEDGE MANAGEMENT SYSTEMS Angoss Software. KnowledgeSEEKER in Action: Case Studies. Toronto: Angoss Software International, 1996. Applehans, W., A. Globe, and G. Laugero. Managing Knowledge: A Practical WebBased Approach. Reading, MA: Addison-Wesley, 1999. Aubrey, A., and P. M. Cohen. Working Wisdom: Timeless Skills and Vanguard Strategies for Learning Organizations. San Francisco: Jossey-Bass, 1996. Badaracco, J. I., Jr. The Knowledge Link: How Firms Compete Through Strategic Alliances. Boston: Harvard Business School Press, 1991. Collins, J. C., and J. I. Porras. Built to Last: Successful Habits of Visionary Companies. New York: HarperBusiness, 1994. Davenport, T. H., and L. Prusak. Information Ecology: Mastering the Information and Knowledge Environment. New York: Oxford University Press, 1997.

354

Selected Bibliography

———. Working Knowledge: How Organizations Manage What They Know. Boston: Harvard Business School Press, 1998. Edvinson, L., and M. Malone. Intellectual Capital. New York: HarperBusiness, 1997. Koulopoulos, T. M. Creating Corporate Instinct: Building a Knowledge Enterprise for the 21st Century. New York: Van Nostrand, 1997. Leonard-Barton, D. Wellsprings of Knowledge: Building and Sustaining the Sources of Information. Boston: Harvard Business School Press, 1998. Nonaka, I., and H. Takeuchi. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press, 1995. Richardson, J. W., Jr. Knowledge-Based Systems for General Reference Work: Applications, Problems, and Progress. San Diego: Academic Press, 1995. Seivert, S. Working from Your Core: Personal and Corporate Wisdom in a World of Change. New York: Butterworth-Heinemann, 1997. Stewart, T. A. Intellectual Capital. New York: Doubleday, 1997. Thierauf, R. J. Knowledge Management Systems for Business. Westport, CT: Quorum Books, 1999. Torsun, I. S. Foundations of Intelligent Knowledge-Based Systems. San Diego: Academic Press, 1995.

ON-LINE ANALYTICAL PROCESSING SYSTEMS Thierauf, R. J. On-Line Analytical Processing Systems for Business. Westport, CT: Quorum Books, 1997. Thomsen, E. OLAP Solutions: Building Multidimensional Information Systems. New York: John Wiley & Sons, 1997.

VIRTUAL REALITY SYSTEMS Davidow, W. H., and M. S. Malone. The Virtual Corporation: Structuring and Revitalizing the Corporation of the 21st Century. New York: HarperBusiness, 1992. Durlach, N. F., and A. S. Mavor, eds. Virtual Reality Scientific and Technological Challenges. Washington, DC: National Academy Press, 1995. Gigante, M., R. A. Earnshaw, and H. Jones, eds. Virtual Reality Systems. London: Academic Press, 1993. Goldman, S. L., R. N. Nagel, and K. Preiss. Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer. New York: Van Nostrand Reinhold, 1995. Hamit, F. Virtual Reality and the Exploration of Cyberspace. Carmel, IN: Sams Publishing, 1993. Hamit, F., and W. Thomas. Virtual Reality: Adventures in Cyberspace. San Francisco: Miller-Freeman, 1991. Heim, M. The Metaphysics of Virtual Reality. New York: Oxford University Press, 1993. Helsel, S. K., and J. P. Roth, eds. Virtual Reality: Theory, Practice, and Promise. Westport, CT: Meckler Publishing, 1991. Krueger, M. W. Artificial Reality II. Reading, MA: Addison-Wesley, 1991.

Selected Bibliography

355

Pimentel, K., and K. Teixeira. Virtual Reality: Through the New Looking Glass. New York: McGraw-Hill, 1993. Rheingold, H. The Virtual Community: Finding Connections in a Computerized World. Reading, MA: Addison-Wesley, 1993. ———. Virtual Reality. New York: Touchstone, 1991. Thierauf, R. J. Virtual Reality Systems for Business. Westport, CT: Quorum Books, 1995. Vince, J. Virtual Reality Systems. Reading, MA: Addison-Wesley, 1995. Watkins, C. D., and S. R. Marenka. Virtual Reality Excursions with Programs in C. Boston: Academic Press, 1994. Wexelblat, A., ed. Virtual Reality: Applications and Exploration. Boston: Academic Press, 1993.

Index Accounting: accent on target costing to lower costs, 332–333; applying the balanced scorecard approach, 325– 326; benchmarking to detect the best business practices, 316–317; cost accounting, 330–336; determine the appropriate costing method, 333–336; effective BIS application, 346–347; employing CPAs as a financial intelligence resource, 347–348; factors that are related to effective financial intelligence, 319–330; financial analysis, 337– 346; forward and backward integration of financial activities, 312–318; functions that lend themselves to financial intelligence, 330; further a company’s financial plans, 309–310; integration of electronic commerce and the marketplace for financial success, 312–313; making more effective use of costaccounting approaches, 322–325; making sense out of financial statements to improve operations, 342–344; making use of content analysis of financial statements, 321–322; newer view of cost-accounting approaches, 331–332; principles underlying financial intelli-

gence, 317–318; providing a better understanding of present and future financial operations, 337–342; relationship of executive information systems to business intelligence systems, 311–312; tie-in of CSFs with key performance indicators and financial ratios, 314–316; tie-in of financial intelligence with strategic intelligence, 310–311; using accounting and financial software to enhance financial intelligence, 326–230; using knowledge of competitors to enhance a company’s performance, 344– 345; using the Internet to discover appropriate financial intelligence, 346; using Web-driven reporting to improve the budgeting process, 320–321 Accurate problem definition, 42–44 Acid test ratio, 342 Ackoff, R. L., 41 Acta Technology, 168 Activity-based costing (ABC), 210, 322– 323, 331–332, 336 Activity-based management (ABM), 210, 323, 327 Actuate Reporting System, 341 Actuate Software Corporation, 341, 262

358

Index

Advanced planning and scheduling (APS), 270, 275–276, 280–281, 284, 298–302 Advertising Web-based, 262–263 AeroTech, 306 AESOP’S visualization product line, 287 Agile manufacturing, 274 AIG, 196 AIM, 175 AIX, 165 Ajinomoto, 205 AlphaBlox, 329 Amazon.com, 185, 203 American Airlines, 309 American Express, 16–17, 198 America Online, 143 American Productivity & Quality Center, 317 Andeman, George, 43 Andersen Consulting, 263 Angoss Software International, 165, 285 Antichrist, 12, 28, 32 AOL Instant Message client, 175 Applets, 306 Applied Decision Analysis, 67 Arbor Software’s Essbase, 113 Aristotelian logic, 9 ARPANET, 143 Arthur Andersen, 317 ASCII, 144 As soon as possible (ASAP), 298 AT&T, 15, 37 Attachmate Corporation, 142 Attachmate’s Emissary, 140 Automatic storage and retrieval system (ASRS), 227–228 AutoMod, 287 Autosimulations Bountiful, 287 AutoXchange, 282 Avantos ManagePro, 211 Baan, 67, 273, 284 Back Web Technologies, 252 Bacon, Francis, 9, 76 Balanced Scorecard approach, 210, 325– 328, 343–344 Balderdash, 46 Bank of America, 135–147

Battelle Memorial Institute, 108 Battelle’s Automated Search Information System (BASIS), 108 BBD&O, 43 Bell, Alexander Graham, 37, 48 Benchmarking, 226–228, 316–317, 345 BestWare’s M.Y.O.B. Premier Accounting, 327 Bills of materials, 299–302 Biondi, A. M., 41 Block Financial’s Kiplinger TaxCut, 327 Blox, 329 Bots, 29 Bounty, 262 Brady, Doug, 185–186 Brainstorming, 42–43, 82–83, 85–86 Brio Enterprise, 102, 285 Brio Technology, 102, 162, 166, 285, 347 Britain’s Cable & Wireless, 196 Broadbase Information Systems, 168 Broadcast Agent, 247 Budgeting process, 320–321 BudgetPLUS, 320 Business Computer Connection, 284 Business intelligence: defined, 66; different levels, 66; financial, 66; introduction to, 3–6; operational, 66; organized delivery, 91–92; strategic, 66; tactical, 66 Business Intelligence Marketplace, 100 Business intelligence software: data mining or knowledge discovery software, 108–112; emerging architecture, 101; GUI/4GL software, 114–115; knowledge extraction tools, 104–105; knowledge management intranet search engines, 107–108; knowledge management software, 105–107; OLAP software, 112–113; statistical analysis software, 114; types, 101–104, 166; utilization of, 100 Business intelligence system (BIS): convergence with knowledge management systems, 23; defined, 3–4, 19–24; essential elements, 20–21, 95–96; means to the end, 92; prior information systems, 24–28; real-world examples, 185–

Index 187; relationship to, 193–195; typical example, 24–25; underlying structure, 92–94; upgrading to, 96–100 Business Objects, 102, 113, 134–135, 162, 165–166, 247, 285 Business planning software, 211 Business processes, 281–283 Business-to-business (B2B) technologies, 138, 150 Buzzell, Robert, 263 Cactus, 321 Campaign management, 248 Capital One Financial Corporation, 329 Cargill, 196 Carizon, Jan, 198 Carleton Corporation, 132 Cascade, 262 Cash Plan Pro, 337 Cause-and-effect diagram, 44 Certified public accountants (CPAs), 347– 348 CFO Vision, 328 Change agents, 347 Chaotic systems, 27 Chaotic times, 196–197 Charles Schwab, 5, 346 Chase Manhattan, 30 Cheer, 262 ChemConnect, 313 Cherndex, 204 Chesapeake Decision Sciences, 284 Chevron, 176 Chief business information officer, 179 Chief financial officer (CFO), 186 Christensen, Clayton, 39–40 Chrysler, 197 CimVision, 284 Cincom Systems, 260 Cipher’s IntelAssist, 206 Circles of Creativity, 45 Cirntechnologies Factory CAD planning and management software, 287 Cisco Systems, 39 Classification and regression tree (CART), 165 Client, 93, 137 Client browser, 145

359

Client/server architecture, 93–94 Coca-Cola, 12, 319 Cognos, 102, 162, 165–166, 186 Collaborative Business Intelligence (CBI), 103, 194 Collaborative portal, 69 Collaborative processing, 69–70 Collaborative processing enterprise portal, 138 Collaboratory computing, 76, 172 Commerce One, 204 Common Gateway Interface (CGI), 128 Compaq Proliant Windows NT webserver, 186 Compaq Prosignia Window NT Server, 264 Competitive advantage, 15–17, 233–234 Competitive intelligence, 206 CompuServe, 114, 143 Computer-aided design (CAD), 306–307 Computer-aided software engineering (CASE), 115 Computer Associates International, 28, 166 Computer integrated manufacturing (CIM), 270, 273, 275 Computer networking: business-to-business E-commerce, 150–151, 171–178; client/server architecture, 137; employment of groupware by business teams, 171–172; enterprise portals, 138– 139; extranets, 172–180; Internet, 143– 152, 172–180; Internet II, 147–148; intranet, 139–143, 172–180; introduction to, 136; merger of traditional E-commerce with the Internet, intranets, and extranets, 148–150; three-tiered architecture, 137; wireless Internet access, 148–150; World Wide Web, 136–152, 172–180 Computer simulation, 216–217 Comshare, 320 Comshare’s Commander, 113, 115 Cornte Hilaire de Chardonnet, 43 Concept of bounded rationality, 79 Connectix Corporation, 140 Content analysis, 321–323, 344–345 Contingency planning, 216

360

Index

Core competence, 204–205 Corporate performance, 13–14 Corporate planning: factors that are related to effective strategic intelligence, 202–212; functions that lend themselves to strategic intelligence, 212– 213; future will move toward greater use of strategic intelligence, 229; longrange strategic planning, 213–221; opportunities of knowledge-based or ‘‘smart’’ products and services, 199– 200; principles underlying strategic intelligence, 200–201; relationship of knowledge management systems with business intelligence systems, 193–195; short-range and medium-range strategic planning, 221–288; short- to long-range strategic intelligence, 195–201; software for strategic intelligence, 210–212; strategic competitiveness BIS application, 228–229; strategic intelligence, 191–193; strategic planning, 191–193; survival strategies for local companies competing in global markets, 198–199; tie-in of strategic intelligence to tactical, operational, and financial intelligence, 192–193; using strategic intelligence to make sense out of chaotic times and disruptive changes, 196–197; utilization of strategic intelligence in executive visioning, 197–198 Corporate portal (CP), 138 CorVue, 328 Cost accounting approaches: activitybased costing (ABC), 210, 322–323, 331–332, 336; activity-based management (ABM), 210, 323, 327; direct costing, 333–336; life-cycle accounting, 324; newer view, 331–332; target costing, 324–325, 332–333; technology accounting, 323–324 Cost-benefit analysis, 83, 86, 332 Cost of goods sold (COGS), 342 CPA/MIS, 186 Creative computer software, 45–47 Creative thinking, defined, 41 Creative thinking techniques: accurate problem definition, 42–44; assumption

reversals, 44; brainstorming, 42–43; component detailing, 44; creative computer software, 45–47; creative process, 42–43; creativity games, 45; effective techniques, 41–48; electronic collaborative creativity, 47–48; electronic meeting room, 47; the five W’s and the H, 44; force fit, 44; metaphors, 44; other creativity techniques, 42, 44; Plus, Minus, and Interesting (PMI) points, 44; rapid prototyping, 42, 44; relational algorithms, 44; Six Thinking Hats, 44; stimulus analysis, 44; symbolic representation, 44; synectics, 42–43; systematized directed induction, 44; traditional, 43–44 Creative Whack Pack, 45 Creativity, applied, 37; checklist of traits, 50–52; common barriers, 48–50; natural, 37; need for, 37; part of a company’s corporate philosophy, 36; reflections of a creative manager, 56– 57; theoretical, 37; ways of viewing, 37 Creativity games, 45 Creativity quiz, 52–56 Crisco, 263 Critical success factors (CSFs), 17–18, 183, 206–209, 213, 309, 312, 314, 341 Cross-Worlds Software, 158 Curie, Marie, 37 Current Analysis, 206 Current ratio, 342 Customer-centric approach, 246 Customer-centricity, 280 Customer relationship management (CRM), 239, 249, 252–253 Customer Synchronized Resource Planning (CSRP), 280 CVS, 135 Cyberspace, 87–88 Daily computerized scheduler, 302 Daimler-Chrysler, 6, 28, 204, 282, 289 Dassault Systems, 287 Data, 7–9 Data architecture, 92, 94 Database marketing, 245–246

Index Data federation system, 123–124, 135– 136, 170 Data fusion, 30–31 Data infrastructure: data federation system, 123–124; data marts, 122–123; data warehouses, 122–123; development of, 120–121; enhancing data quality, 125–126; knowledge bases, 122; total quality management (TQM), 126; very large databases (VLDBs), 121–122 Data management (DM) on the World Wide Web, 119–120 Data marts, 122–123, 127–129 DataMind, 109, 165 Data mining software, 108–112, 134–135, 162–166, 248, 257 Data warehousing: aged data, 119; building, 166–170; choosing a proper server platform, 167–168; data management, 118; data mining tools, 134–135; introduction, 117–120, 122–123, 129, 176– 178; justifying the data warehouse, 129; populating a data warehouse, 168–169; real-time computing systems, 166–170; software, 132–133, 168–169; strategic weapon, 135–136; turnkey data marts, 168; World Wide Web, 131, 169–170 Dataware NetAnswer, 107–108 Dataware Technologies, 107–108 Data visualization software, 132, 164–165 Dawn, 262 Dayton Hudson, 93 DB2 data warehouse, 134–135 DeBono, Edward, 40, 44 Decision-centered approach, 79–81 Decision Edge, 103, 162 Decisioneering, 67 Decision making: capitalizing on business intelligence, 65–66; newer directions, 67–70 Decision processing, 67–69 Decision processing portals, 69 Decision Sciences Corporation, 114 Decision Suite, 30, 107 Decision support system (DSS), 4, 25, 96, 98–99, 271 Decision trees, 257

361

Defense Department, 143 Delphi Energy and Engine Management System, 302–303 Deming, W. Edwards, 276 Deneb Robotics, 287 D. E. Shaw & Company, 159–160 Development and implementation of business intelligence systems: appoint a chief intelligence officer, 179; building effective data warehouses and real-time computing systems, 166–170; continuing support, 184–185; cost justification, 160; determine a proper organization to acquire, understand, and disseminate business intelligence, 181–182; develop BIS applications, 183; develop the system to produce the desired results, 180– 181; enterprise application integration (EAI), 157–158; focus on transforming decisions into action, 183–184; four essential elements, 160–178; get support by starting at the very top of the company, 178–179; making the greatest use of computer networking with accent on E-commerce, 171–178; managing business intelligence over time, 185; new direction in application development, 158; real-world examples, 185–187; select an experienced team to develop and implement the system, 179–180; select appropriate software to meet decision makers’ needs, 181; steps necessary to develop and implement successfully, 178–184; trend toward real-time computing, 159– 160; upgrading current information systems, 161–162; utilizing data mining or knowledge discovery and business intelligence software, 162–166; zero latency, 159–160 Digital Equipment, 15, 39 Digital manufacturing process system (DMAPS), 287 Dimensional Insight, 136 Direct costing, 333–336 Discriminant analysis, 165 Distributed database management system (DBMS), 124

362

Index

Dow Jones, 24, 329 Dow Jones Interactive, 160 Dow Jones News/Retrieval, 346 Drucker, Peter, 4, 117, 184, 221 Dryel, 262 Dun & Bradstreet Software, 328 DuPont, 37, 196–197

Expert system, 25–26 EXtensible Markup Language (XML), 40, 144–145, 204 External environmental factors, 202, 218 Extraction, transformation, and load (ETL), 67 Extranet, 136

eBay, 203 E-business, 5–7 E-business portals, 70 E-commerce, 5–7, 148–151, 174–178, 203–204, 312–313 Economic ordering quantity (EOQ), 299 Economic-value added (EVA), 315 EDA, 321 Edison, Thomas, 37 EDS, 15 E-engineering, 281–283 Einstein, Albert, 37, 49 Electronic collaborative creativity, 47–48 Electronic data interchange (EDI), 139, 147, 149 Electronic meeting room, 47 E-mail, 251 Encryption, 149 Engineering Animation (EAI), 287 Engineering Automation, 287 Enterprise application integration (EAI), 95–96, 151–152, 163 Enterprise information portal (EIP), 138 Enterprise portal (EP), 138 Enterprise resource planning (ERP), 152–163, 210, 270, 273–275, 284, 298– 299 Enterprise 3000 server, 321 Enterworks, 168 Entraprise Group, 176 Ethernet, 125 Excalibur Technologies Corporation, 108 Excel, 211 Exchange Platinum, 175 Excite, 105 Executive information system, 4, 25, 96, 99–100, 132, 311–312 Executive visioning, 197–198, 213 Expediters, 323 Experience In Software, 46

Factory CAD, 287 Fat client, 137 Febreze, 263 Federal Express, 202, 309 Feedback, 78, 80, 84, 87, 213 Fibre Channel, 124–125 Fidelity Investments, 147, 346 The Fifth Discipline, 14 Financial analysis, 337–346 Financial forecasts, 226 Financial intelligence, 10, 192–193, 310– 311, 330 Financial intelligence resource, 347 Financial ratios, 213–215, 222, 309, 312– 315, 341–344 Firewall, 140 Fisher Idea Systems, 96, 330 Flexible budgets, 223 Folgers, 262 Ford, Henry, 37–38 Ford Motor Company, 6, 204, 282 Ford’s AutoXchange, 204 Forecast Pro, 114, 226, 249 Forte Software’s Forte, 115 Fortune 500 companies, 135 Fourth-generation language (4GL), 114– 115, 284 Frequently Asked Question (FAQ) list, 174 Fuji-Xerox, 316 Fulcrum Technologies, 108 Fuller, Thomas, 73 Fuzzy systems, 27 GAIN Systems, 298 Gale, Bradley, 263 Game Gang, 46 Gartner Group, 177, 234 Garvin, David, 317 Gateway, 237

Index General Automation’s UNIX-based Sequoia mainframe, 264 General Electric, 38–39, 282 General Electric Power Systems, 282 General Motors, 6, 177, 204, 282–286 Geographic information system, 27 Gentia Software, 328 Gerstner, Louis, 198 Gigabyte, 121–122, 127 Global paradigm, 37–41 GM’s Delphi Automotive Systems unit, 303 GM’s TradeXchange, 204 GM-UAW Quarterly Network, 302–303 GrapeVine, 105–106 GrapeVine Technologies, 106 Graphical user interface (GUI), 180 Group decision support system (GDSS), 99 Groupware, 75–76, 171–172 GUI software, 114–115 Gulf Canada Resources, 321 Hallmark Cards, 305–306 Harvard Business School, 317 Harvard Graphics, 101 Hass, Robert, 38 Helios, 16 Hepworth, 240 Herman, Miller, 15 Hershey Foods, 45 Heuristic methods, 71 Hewlett Packard, 111, 285 High availability, 92 High-definition television (HDTV), 205 Home Box Office (HBO), 142 Home Depot, 317 Honeywell International, 282 Hopper, Max, 309 HP 9000 Enterprise Parallel Server, 111 HP’s OpenWarehouse Alliance Program, 111 Hughes Electronics, 135 Hummingbird, 166, 168 Hybrid OLAP (HOLAP), 247 Hyperion Essbase OLAP Engine, 102 Hyperion Software Corporation, 102, 162, 328

363

Hypertext Markup Language (HTML), 40, 128, 140, 143–145, 169, 253, 321 Hypertext Transport Protocol (HTTP), 249 Iacocca, Lee, 197 IBM, 5, 12, 39, 128, 132, 151, 161, 165– 166 IBM AS/400 DB2 database, 186–187 IBM DataJoiner, 124 IBM DB2 OLAP Server, 102–103, 162 IBM DB2 Universal Database, 102, 151, 162, 166 IBM Firewall, 151 IBM Global Business Intelligence Solution (GBIS), 103 IBM Intelligent Miner, 228–229 IBM Net.data, 169–170, 187 IBM RS/6000 server, 24 IBM ViaVoice and Dragon System Naturally-Speaking, 29–30 IBM ViaVoice Gold, 30 IBM Visual Warehouse, 102, 162 ICL, 16 Idea, 96 IdeaBank, 46 IdeaFisher 4.0, 46 Idea Generator Plus, 46 Idea-processing system (IPS), 25, 96–97 IDS Financial Services, 17 IDX-change, 178 Imparto Software, 253 Imparto Web Marketing Suite, 253 Impromptu, 102 Industry Data Exchange Association, 178 Influence Software, 168 Informatica Corporation, 128 Information, 7–8 Information Advantage, 106, 136 Information Builders, 115, 128, 168, 321, 343 Information Builders WorIdMART, 343 Information Dimensions (IDI), 108 Information Discovery, 107, 111, 165 Information fusion, 30–31 Information leverage points, 209 Information Week Research, 175 Informix, 103, 132, 166, 168

364

Index

Infoseek, 105 Insight relational OLAP tool, 347 Integrated Software Systems Corporation, 114 Intel, 15 Intellectual assets, 185 Intellectual capital, 16–17 Intellidex Systems, 128 Intelligence, 7–11 Intelligence Assistant, 206 Intelligent agents, 29, 91 Intelligent Knowledge Exchange, 210 Intelligent marketing assistance, 248 Intelligent Miner, 103, 135, 162, 165–166 Internal environmental factors, 202, 218 Internet, 131, 143–152, 203–204, 280– 281 Internet II, 147–148, 237–238 Internet Marketing Manager, 248 Internet service provider (ISP), 151 Intranet, 136, 139–143 Intuit Quicken, 327 Inventory chain optimization (ICO), 298– 299 Inxight’s LinquistX, 105 Java, 306–307, 321 Java applets, 329 Java Dynamic Management software kit, 148 JBA International, 284 J. D. Edwards, 273, 284, 343 J. D. Edwards OneWorld financial system, 343 Jobs, Steve, 198 J. P. Morgan, 346 Just-in-time (JIT), 163, 298 Kaplan and Norton methodology, 328 Keiretsu, 297 Key performance indicators (KPIs), 207– 209, 213–215, 222, 309, 312, 314, 341 Klekamp, Robert C., 50 Kmart, 93 K-nearest neighbor, 165 Knosys’ Knowledge Point, 113 Knowledge, 7–10 Knowledge Access Suite, 107

Knowledge base, 26 Knowledge discovery software, 108–112, 134–135, 162–166, 210–211 Knowledge extraction tools, 104–105 Knowledge management intranet search engines, 107–108 Knowledge management software, 105– 107 Knowledge management systems, 4, 22– 23, 25, 96–98, 193–195 Knowledge Map, 108 KnowledgeMiner 2.0, 329 Knowledge Network, 108 KnowledgeSEEKER, 20, 109, 285, 303– 305 KnowledgeShare, 107 KnowledgeX, 106–107, 210–211 KPMG, 325 Kurzweil, Ray, 28–29 Labor Department study, 214–215 Lanner Group, 287 Law, William, 47 Law & Associates, 47 Lead management, 248 Learning organization, 14–15, 283 Levi Strauss, 38, 93, 274 Lexicon, 30 Life cycle accounting, 324 L. L. Bean, 227–228 Local area network (LAN), 136, 139– 140, 148 Logical Decisions, 67 Logistic regression, 165 Long-range strategic planning, 213–221 Lotus, 172 Lotus 1-2-3, 101, 211 Lotus Notes, 70, 75, 91, 101, 105–106, 172, 206 Lotus Notes/Domino, 91, 105–106 Loyalty Consulting, 228–229 Loyalty Consulting’s data mining services, 165 Lumina Decision Systems, 67 MacDonald, Hugh, 16 Machiavelli, Niccolo`, 37 Magnify, 165

Index Manufacturing: daily and periodic production planning and execution, 298– 302; determine the appropriate production planning and execution technique, 298; development of an overall purchasing and supply chain management, 289–290; discover, analyze, and resolve quality problems, 302–305; effective BIS application, 305–306; expanded view of enterprise resource planning ties in with supply-chain management, 273–275; expanded view of manufacturing-execution system centers on advanced planning and scheduling, 275–276; factors that are related to effective operational intelligence, 278–287; focus on total quality management, 276; from reengineering to E-engineering to improve productivity, 281–283; functions that lend themselves to operational intelligence, 287– 288; future operational intelligence code extended to the virtual factory, 306–307; Internet provides for customer creativity that redefines manufacturing, 280–281; making manufacturing more efficient and effective, 269–271; manufacturing partnerships and their tiein with production planning, 297; principles, 277–278; product improvement using value analysis, 295–296; production planning and execution and total quality management, 296–305; purchasing and supply chain management, 288–296; relationship of decision support systems to business intelligence systems, 271; software for operational intelligence, 284–287; taking an enlarged view of manufacturing operations, 272–278; tie-in of operational intelligence with strategic intelligence, 270; understanding vendor, buyer, and purchased parts performance, 290–295; utilization of a learning organization in a manufacturing environment, 283 Manufacturing execution system (MES), 275, 284 Manufacturing partnerships, 207

365

Manufacturing resource planning (MRP II) system, 275, 284 Manugistics, 284 Marketing: customer analysis to select a specific marketing strategy, 253–254; customer relationship management where the customer is primary, 239; effective BIS application, 264–265; enlarged view of market research and analysis, 244–245; factors that are related to effective tactical intelligence, 242–250; functions that lend themselves to tactical intelligence, 249–250; gaining a competitive advantage, 233– 234; listening to customers and observing their behavior over time, 243–244; marketing strategy, 250–257; pricing products over their life cycle, 258–261; principles based on knowledge of customers, 240–242; product pricing, 257– 264; Profit Impact of Marketing Strategies (PIMS), 240–242, 253, 263–264; relating product quality to pricing, 262– 264; relating Web-based advertising to pricing, 262–263; relationship of OLAP systems to business intelligence systems, 235–236; relationship to competitive wisdom, 265–266; rethinking the marketing process, 236–242; sales analysis to identify market changes, 254–257; software for tactical intelligence, 246–249; tie-in of tactical intelligence with strategic intelligence, 234– 235; using customer satisfaction to build customer loyalty, 240; using database marketing to discover more about a company’s customers, 245–246; using the Internet and the World Wide Web to expand marketing opportunities, 237–238 Marketing automation, 247–248 Marketing Builder Interactive, 248 Marketing intelligence, 205 Marketing Plus, 248 Marketing strategy, 250–257 Market research and analysis, 244–245 Market Site software, 204 Marks & Spencer, 332

366

Index

Massive parallel processing (MPP), 132 MasterCard, 135–136 Material requirements planning (MRP), 284 Mattimore, Bryan, 46 Mattimore Communications, 46 McDonnell Douglas Aerospace, 306 MCI, 135 MCI WorldCom, 178 McKinsey, 198 Medium-range strategic planning, 221– 228 Mellon Bank Corporation, 134 Merck & Company, 15 Merrill Lynch, 147, 196, 346 MES Factory Manager, 284 MES 9000, 284 Meta Group, 130 MetalSite, 313 Metaphor Computer Systems, 45 Metropolitan area network (MAN), 136 Microsoft, 39–40 Microsoft Access, 206 Microsoft Corporation’s SQL Server OLAP Services, 113 Microsoft Exchange, 70, 91 Microsoft Internet Explorer, 140 Microsoft Network, 346 Microsoft Strategy, 136, 305 Microsoft SQL Server, 103, 113, 166 Microsoft Windows, 147, 175, 249 Mill, John Stuart, 76 MindJet’s MindManager, 35, 47–48 MindLink, 46 MIT, 17 MIT’s Center for Organizational Learning (COL), 15 MIT’s Sloan School of Management, 207 Mize, Hauser & Company, 186 Mobil Corporation, 317 Monsanto, 12 Mosaic, 147 Motorola, 148, 205, 244, 274 Mozart, 31 Mr. Clean, 262–263 Multidimensional analysis, 112–113, 254 Multidimensional OLAP (MOLAP), 98, 247

Multimedia systems, 27 Multipetabyte, 121 National Semiconductor, 15 Natural language processing, 29 NBC White Paper, 276 NCR Corporation, 128, 168, 305 NCR 5100 UNIX, 305 NCR Teradata, 94, 305 NCSA Mosaic, 143 NEC, 244, 324 NeoVista Solutions, 111, 165, 347 NeoVista Solutions Decision Suite Series, 111 Net.Commerce, 151 Netscape, 136 Netscape Enterprise Server, 321 Netscape Navigator, 140, 143 Network attached storage (NAS), 124– 125 Network computing architecture (NCA), 327 Neural networks, 26–27, 164–165, 285– 286 New Era of Networks (NEON), 158 New Product Development, 45 Newton, Sir Isaac, 37 Newtonian view, 38 Next Generation Software, 187 NGS-IQ, 187 Nippon Telephone and Telegraph, 44 Nissan, 324 Nolan Norton Institute, 325 Noller, R. B., 41 Northern Trust, 24 Novell’s DigitaIMe, 175 Novum Organum, 9 NT Web Server, 135 Object-oriented programming (OOP) paradigm, 115 Objects, 329 Office 2000, 40 OLAP software, 112–113 On-line analytical processing (OLAP) system, 4, 25, 96, 98, 132, 235–236, 328

Index On-line knowledge management (OLKM), 105 On-line transactional processing (OLTP), 94, 121 On Track MES, 284 OpenMind, 142 Open-system architecture, 92–93 Operational intelligence, 10, 192–193, 270, 287–288 Operational plans, 195–201 Operations and Development Information Network (ODIN), 321 Opportunity-centered approach, 85–87 OptionFinder, 47 Oracle BI tools, 210 Oracle Business Intelligence Systems, 151–152, 210, 257 Oracle Corporation, 67, 103, 105, 111, 132, 166, 178, 204, 273, 284, 327–328 Oracle Developer 2000, 115 Oracle Express, 113 Oracle Financials Version II, 327 Oracle SEM, 210 Oracle Web-enabled tools, 136 O’Reilly & Associates, 140 Orion Corporation, 196 Osborn, Alex F., 43 OS/390, 134–135 Palm Pilots, 175 Palo Alto Business Plan Pro, 338 Palo Alto Software, 337–338 Pampers, 262 Panorama Software Systems, 113 Parnes, S. J., 41 Pattern Query Language, (PQL), 107 PC DOCS/Fulcrum, 166 Penske, 317 Pentium-based PC, 167 PeopleSoft, 67, 166, 273, 284, 328 Pepto-Bismol, 262 Perdue, Frank, 263–264 Personify, 262 Petabyte, 122 PICK database management system, 264 Pictionary, 46 Pie charts, 223–225 Pilot Software, 111, 165, 328

367

Pilot Software Balanced ScoreCard, 326 Pilot Software Decision Support Suite, 111 Pilot Software LightShip Suite, 113 Pioneer Hi-Bred International, 16–17 Plante & Moran, 186 Platinum Technology, 132, 166 Plato, 37, 113 PlexCenter Planning and Decision Support Laboratory, 47 Plurntree Software Corporate Portal, 69 Point-of-sale (POS) terminal, 305 Polaroid Corporation, 15–17 Pope John Paul II, 16 Portal technology, 138 Portfolio Accounting World-Wide (Pawws), 346 PowerBuilder, 115 PowerPlay, 102, 186 Powersoft Corporation PowerBuilder, 115 Premous Technology Corporation Templar, 149 Price index, 290–295 PriceWaterhouseCoopers ABM software, Activa, 327 Pride International, 343 Proactive approach, 4, 15, 73–74 Problem-centered approach, 81–85 Problem finding, 11, 17, 19, 72–76, 319 Problem-finding process: introduction, 81; logical-analytical thinking, 81; opportunity-centered approach, 81, 85–87; problem-centered approach, 81–84; typical applications, 84–85, 87 Problem solving, 11, 72–76 Problem-solving process: decision-centered approach, 79–81; introduction, 76; quantitative-centered approach, 76–79; typical applications, 78–81 Problem types: heuristic methods, 71; introduction, 70–72; rules of thumb, 71; semi-structured, 70–71; unstructured, 70–71; well-structured, 70–71 Procter & Gamble, 37, 251, 262–263 Prodigy, 143 Production planning and execution, 296– 302 Product pricing, 257–264

368

Index

Profit Impact of Marketing Strategies (PIMS), 240–242, 253, 263–264 Profit plans, detailed, 223 PS-Engine, 287 Pull approach, 180 Purchase performance index (PPI), 291– 295 Purchasing, 288–296 Push approach, 180, 251–252 QBank, 46 QDB/Analyze, 126 QDB Solutions, 126 Quality index, 291–295 Quantitative-centered approach, 76–79 QUEST, 287 R & V Insurance, 135 Rapid application development (RAD), 115, 124 Rapid prototyping, 42, 44 Raytheon, 242 Reactive approach, 4, 72–74 Real-time computing, 159–160, 170 Red Brick relational database, 111 Reengineering, 281–283 Relational database management system (RDBMS), 121, 131, 284 Relational OLAP (ROLAP), 98, 247 Reliable, available, and scalable (RAS), 118 Republic Indemnity, 186–187 Research and development (R&D), 39–40 Return on intellectual capital, 185 Return on investment (ROI), 205, 242, 315, 319 Return on knowledge (ROK), 347 Reuters Money Network, 346 Richardson-Vicks, 251 Risc/6000 UNIX machines, 166 RobCAD software, 287 Rockart, John F., 17, 207 Rolls-Royce, 126 Royal Dutch/Shell Group, 282 RS/6000 SP, 165 Rubik’s Cube of data, 98 Rubric, 248 Rubric EMA, 248

Rule induction, 165 Rules of thumb, 71 RWT Corporation, 284 Sagent Technology, 128 Sales analysis, 254–257 SAM (Strategic Analysis Mode), 114 Sametime 1.0, 172,175 SAP, 67, 166, 273, 284, 328 SAS Institute, 103, 111–112, 114, 128, 132, 165–166, 194–195, 198, 328, 347 SAS (Statistical Analysis System)/lnsight, 112 SAS Institute Collaborative Business Intelligence, 166 SAS Institute Enterprise Miner, 347 SAS Institute’s Executive Information System, 264–265 Satisficing, 79 Scalability, 92 Scenario planning, 216–217 Script Software International’s KnowledgeMiner 2.0, 329 Sears, 16, 93, 332 Seagate, 166 Senge, Peter, 14 Sensitivity analysis, 78, 80, 216, 223 Server, 93, 137 Set Analyzer, 247 Sharp, 324 Shaw, Gordon, 31 Short-range strategic plans, 195–201, 221– 228 Showtime, 142 Silvon Software, 168 Simon, Herbert, 79–80 Simulation software, 286–287 Smart DB Corporation, 168 Smart Ideas, 48 Smart manufacturing, 269 Smart products and services, 199–200 Smart Technologies’ Smart Idea 2.0 brainstorming tools, 47–48 Smart technology systems, 30–31 Smith, Adam, 281–282 Smith, Fred, 309 Sovereign Hill Software, 106 Speech recognition system, 29–30

Index SPSS, 114, 132 SQL Financials, 328 Standard & Poor’s 500, 24 Statistical analysis software, 114 STATS.II, 114 Storage area networks (SANs), 124–125 Strategic Decisions Group, 67 Strategic Enterprise Management (SEM), 210, 327 Strategic intelligence, 10, 191–193, 231– 235, 270, 310–311 Strategic planning, 191–193, 195–201 Strategy Pyramid, 248 Structured query language (SQL), 107, 123, 142 Sun E10000 server, 346 Sun Microsystems, 148, 178, 321 Sun Microsystems’ HotJava, 140 Sun Microsystems’ JAVA language, 136 Supply Chain Data Warehouse, 177–178 Supply chain management (SCM), 270, 273–274, 288–296 Survival strategies, 198–199 Sybase Omniconnect, 124 Sybase System II, 24, 103, 111, 132, 166 Symix Systems, 280 Symmetric multiprocessing (SMP), 132, 167 Synectics, 42–43 Synergistic decision making, 99 System/390 mainframes, 166 T-1 line, 149 Tactical intelligence, 10, 192–193, 234– 235, 250–252, 265 Tactical plans, 195 Target costing, 324, 332–333 Technology accounting, 323–324 Tecnomatix Technologies, 287 Telebrands, 264–265 Tell-A-Graf, 114 Terabyte, 122 Testwise, 105 Thin client, 137 Thinking Machines, 165 Think Systems, 284 3-D environments, 144 3-D simulation, 287

369

3-Name3D, 28 Time-Warner Entertainment, 142 Toffler, Alvin, 117 Toskoku, 196 Total quality management (TQM), 126, 270, 276, 296, 302–305 Toyota, 324 Traditional creativity techniques, 42–44 Transmission control protocol/Internet protocol (TCP/IP), 140, 147 Travelers, 196 Truth, 11–12 Unicode, 144 Uniface Corporation’s Uniface, 115 United Automobile Workers (UAW), 302 University of Arizona, 47 University of California, Irvine, 31 Unix workstations, 167 UPS, 202 U.S. Air Force, 143 Vality Technology, 162 Value added networks (VANs), 147, 149 Value analysis, 295–296 Value engineering, 295–296 Value of 1 percent, 342 Venture analysis, 258–261 Vergara, E., 41 Verity, 105, 108 Very large databases (VLDBs), 94, 121– 122 Videophone, 140, 142 Viewpoint DataLabs International, 28 Virtual factory, 306–307 Virtual reality (VR), 27–28, 345 Virtual Reality Modeling Language (VRML), 143–144 Virtual worlds, 274–275 Visible Decisions, 24 Visioning, 242–243 Visual information retrieval systems, 27 Visual RetrievalWare software, 108 Vitria Technology, 158 VIT’s SeeChain, 69 Von Oech, Roger, 45 Wallas, Graham, 42 Wal-Mart, 16, 93, 135, 236, 332

370

Index

Wang, 39 Warehouses, 94 Web-based bulletin boards, 140 WebBoard, 140, 142 Web browsers, 137–138, 143, 150 Web cellphones, 175 Web FOCUS, 321 Web Intelligence, 20, 285 Web servers, 137–138 Web TV, 175 Welch, Jack, 38–39 Wells Fargo Bank, 346–347 ‘‘What if’’ approach, 214, 223 Wide area network (WAN), 125, 136, 139–140 Wincite systems, 106, 206 Windows NT, 165, 320, 328 Windows 98, 328 Windows 95, 198, 248, 320, 328

Wireless Internet Access, 148 Wisdom, 11, 265–266 Wisdom management system (WMS), 31– 32 WITNESS, 287 WizRule, 126 WizSoft, 126 WordPerfect, 101 World Wide Web, 131, 136–152, 169–178, 203–204, 237–238, 284–287, 326–330 X.25 standard, 149 Xerox Corporation, 227–228, 316 Xerox Logistics and Distribution (L&D), 227–228 Yahoo!, 40, 69–70, 262–263 Zero-latency concept, 159–160, 234–235

About the Author ROBERT J. THIERAUF is Professor Emeritus of Information Systems at Xavier University, Cincinnati, Ohio. Previously a staff accountant and consultant at the then Coopers & Lybrand, he has written extensively on all facets of information systems, with particular attention to knowledge management, online analytical processing, decision support, virtual reality, and other systems—most of which are explored in Dr. Thierauf’s 13 previous Quorum books.