Data Storytelling and Translation: Bridging the Gap Between Numbers and Narratives 9781683926511

Table of contents :
Cover
Title
Copyrightpage
Contents
Prologue
Acknowledgments
Chapter 1 The Age of the Data Translator
Curiosity
Empathy
Trustworthiness
Who is This Book For?
How This Book is Laid Out and What to Expect
Chapter 2 All Decisions Start With People
Start Understanding People by Looking Inward First
Understanding Incentives and Biases
Understanding, Engaging, and Communicatin With Your Customer
Sample Survey from Talent Management to Hiring Manager Customer
References
Chapter 3 Start With Good Questions and Great Listening
The Importance of Good Questions
The Definition of a Good Question
How to Ask Good Questions
Asking the Right Customer
Create the Right Setting
Asking in the Right Time and Place
Defuse With Your Questions
Body Language and Tone
Listening: Being Heard by Being a Great Listener
Focus
Understand
Respond
References
Chapter 4 Being Fluent in the Language of Data
Everything Starts With Understanding the Data
Structured versus Unstructured Data
Categorical versus Numerical Data
Clean versus Messy Data
Statistics Is the Language of Understanding Data
Descriptive Statistics
Central Tendency
Variability
Correlation
Inferential Statistics
The Superpower of Analytics and Data Science
Types of Analytics
Foundational Analytics Concepts
Artificial Intelligence
Machine Learning
Specific versus General Artificial Intelligence
Classification versus Regression
Supervised versus Unsupervised Learning
It’s All About the Data
Data and Analytics as Services
The Tension Between Transparency and Performance
Perpetual Model Bias
References
Chapter 5 Identify, Understand, and Frame Problems
Identifying Problems Means Understanding Pain
Problem-Ask-Value Framework
Understand the Problem
Understand the Question
Understand the Value
Reframing Problems
References
Chapter 6 Simplifying Insights Through Metrics and Objectives
What Is the Purpose?
Communicate Priorities
Align People and Processes
Show Progress
Motivate Behavior
Define Expectations
Reduce Uncertainty
Leading and Lagging Metrics
Efficiency, Effectiveness, and Outcome Metrics
Upward and Downward Metrics
Who is the Audience?
Sales Activity Metric Example
Customer Experience Metric Example
How Do You Communicate?
Initial Communication
Ongoing Communication
Who Is the Target?
Accuracy versus Precision
Operationalizing Metrics
References
Chapter 7 Painting Your Data Story
Data Story Canvas Introduction
Data Story Topic
Delivering Your Data Story
The Audience
The Existing Narrative
What They Need to Know
The Hook
Keep: Holding Their Attention
Compel: The Call to Action
The Data Source
The Tradeoffs
Your Confidence
Data Story Canvas Example
Chapter 8 everaging Visuals to Share
Insights and Compel Action
The Purpose of Data Visualization
Exploratory Data Visualization
Data Visualization as Storytelling
Principles of Good Data Visualization
Picking the Right Chart
Tables Are Not Evil
Harnessing the Power of Size, Angle, and Position
Size
Angle
Position
The Power of Color
Color Usage
Context Correct Color
Color Consistency
Number of Different Colors
Intensity of Color
Categorical versus Continuous Color
Colorblind-Friendly
Text in a Data Visualization
Summaries
Titles
Legends
Axis Labels
Data Labels
Annotations
Consistency
Format
Source
Trends and References
Don’t Overdo It
Gestalt Principles
Moving Beyond Design and Communicating Data Visualizations
Prioritize the Meaning
Ask Questions to Engage
Get Second and Third Opinions
Avoid Check-the-Box Visualizations
Layer Your Visualization
Show Your Work and Get Detailed
Build Trust Through Data Visualization
References
Chapter 9 Leveraging Dashboards
in Your Communication
Dashboard Best Practices
Provide the What, Why, and Now What
Be Consistent
Follow the Z-Pattern
Balance Interactivity
Don’t Shy Away From Text
Make Sure the Data Source is Obvious
Defaults Matter
Dashboard Lifecycle
Beginning
Middle
End
Dashboards and Storytelling
References
Chapter 10 Communicating Your Data Story
An Introduction to the Data Story Checklist
Be Authentically You
Test and Verify
Be Vulnerable
Eliminate Roadblocks in Advance
Engage Often and Early
Be Transparent and Ethical
Be Confident and Humble
Be Prepared to Improvise
Lead With a Story Backed by Data and Visuals
Consider the Right Person
Data Story Checklist
Developing Your Communication Skills
Meetup Groups / Professional Association
Contributing Author
Improvisational Theater
Toastmasters International
Conclusion
References
Epilogue
Top 20 Podcasts for Data Translators
Top 20 Books for Data Translators
Index

Citation preview

Data Storytelling and

Translation

LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY By purchasing or using this book and companion files (the “Work”), you agree that this license grants permission to use the contents contained herein, including the disc, but does not give you the right of ownership to any of the textual content in the book / disc or ownership to any of the information or products contained in it. This license does not permit uploading of the Work onto the Internet or on a network (of any kind) without the written consent of the Publisher. Duplication or dissemination of any text, code, simulations, images, etc. contained herein is limited to and subject to licensing terms for the respective products, and permission must be obtained from the Publisher or the owner of the content, etc., in order to reproduce or network any portion of the textual material (in any media) that is contained in the Work. Mercury Learning and Information (“MLI” or “the Publisher”) and anyone involved in the creation, writing, or production of the companion disc, accompanying algorithms, code, or computer programs (“the software”), and any accompanying Web site or software of the Work, cannot and do not warrant the performance or results that might be obtained by using the contents of the Work. The author, developers, and the Publisher have used their best efforts to ensure the accuracy and functionality of the textual material and/or programs contained in this package; we, however, make no warranty of any kind, express or implied, regarding the performance of these contents or programs. The Work is sold “as is” without warranty (except for defective materials used in manufacturing the book or due to faulty workmanship). The author, developers, and the publisher of any accompanying content, and anyone involved in the composition, production, and manufacturing of this work will not be liable for damages of any kind arising out of the use of (or the inability to use) the algorithms, source code, computer programs, or textual material contained in this publication. This includes, but is not limited to, loss of revenue or profit, or other incidental, physical, or consequential damages arising out of the use of this Work. The sole remedy in the event of a claim of any kind is expressly limited to replacement of the book and/or disc, and only at the discretion of the Publisher. The use of “implied warranty” and certain “exclusions” vary from state to state, and might not apply to the purchaser of this product. Companion files for this title are available by writing to the publisher at [email protected].

Data Storytelling and

Translation Bridging the Gap Between Numbers and Narratives

David Mathias

Mercury Learning and Information Boston, Massachusetts

Copyright ©2024 by Mercury Learning and Information. An Imprint of DeGruyter Inc. All rights reserved. This publication, portions of it, or any accompanying software may not be reproduced in any way, stored in a retrieval system of any type, or transmitted by any means, media, electronic display or mechanical display, including, but not limited to, photocopy, recording, Internet postings, or scanning, without prior permission in writing from the publisher. Publisher: David Pallai Mercury Learning and Information 121 High Street, 3rd Floor Boston, MA 02110 [email protected] www.merclearning.com 800-232-0223 D. Mathias. Data Storytelling and Translation. ISBN: 978-1-68392-651-1 The publisher recognizes and respects all marks used by companies, manufacturers, and developers as a means to distinguish their products. All brand names and product names mentioned in this book are trademarks or service marks of their respective companies. Any omission or misuse (of any kind) of service marks or trademarks, etc. is not an attempt to infringe on the property of others. Library of Congress Control Number: 2023944209 232425321 This book is printed on acid-free paper in the United States of America. Our titles are available for adoption, license, or bulk purchase by institutions, corporations, etc. For additional information, please contact the Customer Service Dept. at 800-232-0223(toll free). All of our titles are available in digital format at academiccourseware.com and other digital vendors. Companion files for this title are available by contacting [email protected]. The sole obligation of Mercury Learning and Information to the purchaser is to replace the product, based on defective materials or faulty workmanship, but not based on the operation or functionality of the product.

CONTENTS Prologuexi Acknowledgmentsxiii Chapter 1 The Age of the Data Translator

1

Chapter 2 All Decisions Start With People

11

Chapter 3 Start With Good Questions and Great Listening

29

Chapter 4 Being Fluent in the Language of Data

49

Curiosity5 Empathy6 Trustworthiness7 Who Is This Book For? 8 How This Book is Laid Out and What to Expect 8 Start Understanding People by Looking Inward First 12 Understanding Incentives and Biases 18 Understanding, Engaging, and Communicating With Your Customer 20 Sample Survey from Talent Management to Hiring Manager Customer 22 References27

The Importance of Good Questions 29 The Definition of a Good Question 30 How to Ask Good Questions 33 Asking the Right Customer 34 Create the Right Setting 34 Asking in the Right Time and Place 35 Defuse With Your Questions 35 Body Language and Tone  36 Listening: Being Heard by Being a Great Listener 40 Focus41 Understand43 Respond45 References48 Everything Starts With Understanding the Data Structured versus Unstructured Data  Categorical versus Numerical Data Clean versus Messy Data 

50 50 51 52

vi • Contents

Statistics Is the Language of Understanding Data 53 Descriptive Statistics 55 Central Tendency 55 Variability58 Correlation59 Inferential Statistics 62 The Superpower of Analytics and Data Science 64 Types of Analytics 64 Foundational Analytics Concepts 66 Artificial Intelligence  66 Machine Learning  66 Specific versus General Artificial Intelligence 66 Classification versus Regression 67 Supervised versus Unsupervised Learning  67 It’s All About the Data 67 Data and Analytics as Services  68 The Tension Between Transparency and Performance  68 Perpetual Model Bias  70 References71

Chapter 5 Identify, Understand, and Frame Problems

73

Chapter 6 Simplifying Insights Through Metrics and Objectives

89

Identifying Problems Means Understanding Pain  75 Problem-Ask-Value Framework 75 Understand the Problem 76 Understand the Question 78 Understand the Value 79 Reframing Problems 81 References86

What Is the Purpose? Communicate Priorities Align People and Processes Show Progress Motivate Behavior Define Expectations Reduce Uncertainty Leading and Lagging Metrics Efficiency, Effectiveness, and Outcome Metrics Upward and Downward Metrics

91 92 92 92 92 93 93 93 94 95

Contents • vii

Who is the Audience?  95 Sales Activity Metric Example 95 Customer Experience Metric Example 96 How Do You Communicate?  96 Initial Communication 97 Ongoing Communication 98 Who Is the Target?  99 Accuracy versus Precision 100 Operationalizing Metrics 100 References103

Chapter 7 Painting Your Data Story

105

Chapter 8 Leveraging Visuals to Share Insights and Compel Action

121

Data Story Canvas Introduction Data Story Topic Delivering Your Data Story The Audience The Existing Narrative What They Need to Know The Hook  Keep: Holding Their Attention  Compel: The Call to Action  The Data Source The Tradeoffs  Your Confidence  Data Story Canvas Example

106 106 107 107 107 109 110 110 114 114 114 115 116

The Purpose of Data Visualization 122 Exploratory Data Visualization 122 Data Visualization as Storytelling 125 Principles of Good Data Visualization 126 Picking the Right Chart  127 Tables Are Not Evil  128 Harnessing the Power of Size, Angle, and Position 129 Size  129 Angle129 Position130 The Power of Color  131 Color Usage 132 Context Correct Color  133

viii • Contents

Color Consistency 135 Number of Different Colors  135 Intensity of Color 135 Categorical versus Continuous Color 135 Colorblind-Friendly136 Text in a Data Visualization 136 Summaries137 Titles137 Legends138 Axis Labels 139 Data Labels  139 Annotations  139 Consistency140 Format140 Source140 Trends and References  141 Don’t Overdo It 142 Gestalt Principles 143 Moving Beyond Design and Communicating Data Visualizations 144 Prioritize the Meaning 145 Ask Questions to Engage 145 Get Second and Third Opinions  146 Avoid Check-the-Box Visualizations 146 Layer Your Visualization 148 Show Your Work and Get Detailed 149 Build Trust Through Data Visualization 149 References152

Chapter 9 Leveraging Dashboards in Your Communication Dashboard Best Practices  Provide the What, Why, and Now What Be Consistent  Follow the Z-Pattern Balance Interactivity  Don’t Shy Away From Text Make Sure the Data Source is Obvious Defaults Matter Dashboard Lifecycle  Beginning  Middle 

153 155 156 158 159 159 160 160 161 161 161 162

Contents • ix

End162 Dashboards and Storytelling  162 References164

Chapter 10 Communicating Your Data Story

165

An Introduction to the Data Story Checklist 165 Be Authentically You 166 Test and Verify  167 Be Vulnerable 168 Eliminate Roadblocks in Advance 169 Engage Often and Early  170 Be Transparent and Ethical 170 Be Confident and Humble 171 Be Prepared to Improvise 173 Lead With a Story Backed by Data and Visuals 174 Consider the Right Person 174 Data Story Checklist 175 Developing Your Communication Skills 176 Meetup Groups / Professional Association 176 Contributing Author  176 Improvisational Theater  176 Toastmasters International 176 Conclusion177 References177

Epilogue179 Top 20 Podcasts for Data Translators Top 20 Books for Data Translators

179 182

Index187

PROLOGUE If you are asked to picture a translator what picture comes to mind? Maybe you are picturing the United Nations and all the people that are translating different conversations to different leaders. Maybe you are picturing the person at an event signing the event to those without hearing. Maybe you might picture a courtroom where someone has been charged with a crime but doesn’t speak English, so the person next to him is whispering in his ear everything that is going on. In each of these examples the person leveraging the translators is lost without them. They get nearly all their understanding of what is going on in that environment from that trusted person. Equally true is that the person who is communicating to the person with the translator has an equal dependency on that translator in communicating exactly what they said. Being a translator is a tough job. In the moment, they are processing information in one language and with a certain context that must then be applied in another language, in words that fits your audience’s cultural and educational background, but also cannot deviate in a way that has lost its meaning. The role of a translator is really important. The role of a data translator is arguably even more difficult than a language translator. Not only do you have to translate the language of data, but you also have to do this translation generally to many different audiences with vastly different understandings of data. Add to this they have different desires to learn about data. This book is all about helping people become successful data translators. Data translators are the real heroes in many organizations today. Maybe you are in a nonprofit that is trying to demonstrate value to get a funding grant awarded, and you are the person asked to translate this value to others. Maybe you are a software product manager that needs to constantly translate data and how it impacts decisions to customers, executives, engineers, designers, and others. Maybe you are a business analyst in government that is responsible for analyzing trends and their potential impacts on governmental objectives. The reality is almost everyone working with data and information either needs to be fluent themselves or needs a translator. While ideally everyone would be data fluent and maybe someday that will be the case. However, for the near future there is a constant need for strong data

xii • Prologue

translators in a variety of roles. In fact, in many roles your strength as a data translator will be a key difference in your success. With this backdrop you are ready to enter this book’s journey into the role of a data translator and its importance. Most of this book though is geared at helping you become a successful data translator. One that is comfortable with the language of data and communicating data with others. Also, a quick reminder to download the interview videos and transcripts that accompany this book. Learn from experts being interviewed on their experience and advice around being successful data translators. Additionally, for using this as a textbook, please see the sample syllabus included in the files. All these items can be downloaded by writing to the publisher at [email protected].

D. Mathias September 2023

ACKNOWLEDGMENTS We are influenced by so many so it is guaranteed that I will only cover a fraction of the many that have impacted me in making this book a success. After all I like you are influenced by the people in our lives, the world and culture generally around us, and the genes that we are born with. Thank you to all the greats that I have learned from. Some of these are referenced directly in this book. But this is only a small fraction of those that have influenced me and bring me where I am with a work like this, whether through conversations I have had, classes I have taken, or information I have consumed. Everything builds upon the shoulders of many others and this book is certainly a reflection of the many shoulders of others that I was fortunate to benefit from. Thank you to my loving wife Jenn Mathias in putting up with this extra complication of writing a book on top of an already busy load. More yet, Jenn graciously would read and give open input on what worked and what didn’t as a first pass to make my first pass to others look more impressive than it should have. Thank you to Jeff Richardson, Serena Roberts, Bonnie Holub, and Allen Hillery for taking the time to review and give advice on making this book better. Thank you to Jim Walsh and others at Mercury Learning in making this book stronger and a success and for reaching out to have me write something in the first place.

CHAPTER

1

The Age of the Data Translator

H

ow many times have you felt that you and your colleagues are talking past each other when discussing data insights and analytics? Maybe you are a data and technology professional that is tired of the misunderstandings and the challenging meetings and the angry emails that lead to you being accused of not delivering “what the organization needs.” Or maybe you are a professional who is frustrated by a lack of results and you are uncertain that data analysis holds any real value or potential for real-world application. You may even be a team leader or part of a team that struggles in making the impact that is deserved because of a struggle to communicate data-informed decisions. Understanding that teams benefit from developing data communication skills together there are exercises aimed at teams in addition to individual exercises. Another important audience is students and their instructors looking to develop those lifelong data communicator skills. There is even a sample course syllabus aligned with this book that might be helpful. You may even be a student that has not yet faced these challenges in the workplace. If so, allow me to assure you that it is only a matter of time. You will most definitely be confronted with these challenges. Unfortunately, you will almost certainly face frustration, miscommunication, and unwarranted blame at some point in your career. This book will give you the tools to minimize these occurrences and to deal with them as they come. Data analysis is becoming an increasingly powerful tool. Leveraging data in order to make agile, customer-focused decisions

2 • Data Storytelling and Translation

is almost a necessity in order to remain competitive in today’s market, whether you are a nonprofit, government, startup, or forprofit corporation. As a result, corporations have begun trying everything to get an edge on the competition: buying the newest technology, hiring countless data analysts, and so on. It can be incredibly disheartening when these efforts do not achieve desired results. Soon enough, this disappointment can lead to blame, and people might even begin to suspect that the technology is faulty, or that their data analysts are incompetent, or maybe even that the data itself is entirely useless in the first place! In truth, most of the time this pain is not a product of incompetence or a lack of proper tools or technology. Rather, this frustration is often the result of bad communication and misunderstandings between people trying to jointly tackle problems. This communication gap can often be traced back to poor data translation and a lack of compelling data storytelling. With all the importance put on data, how can this lack of data communication exist? The unfortunate truth is that most people are not formally taught the skills required for data storytelling and translation. This is true even if they possess relevant degrees like an MBA or master’s in data science from impressive universities. This learning gap often leads us to taking some type of online course or grabbing a book with some combination of the words “data,” “analytics,” and “storytelling” in the title. Unfortunately, most of these options tend to focus more on data visualization and learning tools and neglect true data communication skills. So how have most people become good data communicators? When you ask people that are good at data storytelling and translation how they became so good at it, most will give you one of these two answers: First, they might say something like “my parents or my siblings or my mentor or my first boss coached me to become a good communicator.” Second, they might say something like “I learned from repeated missteps and errors and worked on getting better.” The purpose of this book is to be that mentor or coach to help you along your way to being a good data communicator more quickly and to minimize those missteps, errors, and learning pains. Towards that

The Age of the Data Translator • 3

goal, this book leverages both a systematic and an applied approach for two reasons: (1) this is more accessible for more people; and (2) this is more memorable and thus easier to grasp. This book is for nontechnical and technical people and teams looking to level up. The skills associated with data storytelling, translation, and communication are just as important for nontechnical professionals as technical professionals. In fact, as data becomes more prevalent, data communication skills are becoming important even in professions that are not strictly technical. The good news is that data communication skills are something that we can all get better at and become good at no matter where we are in our career. As this book unfolds, we will get more into the specifics of different data communication skills. My hope is that you will take this discussion and the applied activities throughout the book and use them to enhance both your personal and professional life. The good news is that if you put the work in, you will get results. This book will give you the leg up you need to get more value and meaning from data. Let’s get started on this journey by making sure we are on the same page. After all, an important skill for a data communicator is not being presumptive and asking for questions and clarifying thoughts. Throughout this book, we reference data communication as the use of data storytelling and data translating, so let’s breakdown data storytelling and data translating individually. So, what is storytelling? Storytelling is the act of relaying information cohesively to another through words, pictures, sounds, and other information—transferring actions in order to serve a greater purpose. Yes, you are storytelling when you are presenting to a group, but just as importantly you are storytelling when you are writing an email or updating a report. Storytelling will also help you to make meaningful and even emotional connections with others in personal and professional situations. Good storytelling can even make you seem smarter, and we could all use a little of this. Improving your storytelling skills will help you to elevate yourself both professionally and personally. After all, one of the most meaningful things for humans is connection, and storytelling is a vital conduit to human connection. As a result, storytelling has been

4 • Data Storytelling and Translation

acknowledged as one of the most versatile and valuable skills that we can have in the information age. Unfortunately, data translation is much less glamorous. While a variety of different data storytelling books have been put to print, data translation books are few and far between. Perhaps translating has not got the same sexy, mass-market appeal that storytelling has. Translators tend to go unnoticed in the background. When thinking about translating, you might picture people taking one written language and translating it physically into another language, or you may picture leaders at the United Nations listening to speeches in foreign languages and being fed their native language in real time via a headset. While these are certainly examples of translation, translating can also be so much more. In fact, translating is one of the most undervalued skills professionally and personally. Translating is expressing information accurately and meaningfully from one language or group to another language or group. This may sound simple, but it has become a more and more important activity as the world has become more complex and specialized. For example, take cars and mechanics. At one time cars, even with all their novelty, were fairly simple to understand. Accordingly, people often serviced their own cars for common maintenance. Then, cars got more and more complex through additional features and electronics. Now, most of us are unable to understand and fix our cars by ourselves. Therefore, we hire a mechanic that has the skills to fix our car. Great mechanics are not just good at fixing a car; they are also great translators. They process information about what we are experiencing and translate that into potential problems to investigate. Great mechanics can further translate potentially complex information back to us in a manner that not only helps us both feel more comfortable about our car repair experience, but also makes us better car owners. The reality is that most mechanics don’t have this expertise and instead it is a shop manager that is able to translate. Ideally, all mechanics and all of us are at least competent translators. That way, there are less chances for information to be lost in translation. No matter if you are a leader, a product manager, an analyst, a designer, or even something else entirely, you need both storytelling

The Age of the Data Translator • 5

and translating skills! Further, there are certain characteristics that will help to make you a better storyteller and a better translator. But these skills are indeed different, and learning to apply storytelling and translating skills in the right contexts will make you better at both. Consciously becoming better storytellers and translators is something we should all strive for. The good news is that storytelling and translating are skills that we can learn. There is no perfect storyteller or translator; these are skills that we perpetually can and should hone and improve. Certainly, some people are more natural at these skills than others, but everyone can become a better storyteller. Think of Hellen Keller. She was a person that was both deaf and blind, yet she was a great storyteller and translator. Becoming a better storyteller and translator takes practice. Sometimes this means you need to lean into things that you do not feel comfortable with. This discomfort will ease, and things will become more enjoyable. This process is an endless journey, not a race. Great data communicators have many traits, but three pivotal traits that we will revisit throughout this book are: (1) curiosity; (2) empathy; and (3) trustworthiness.

CURIOSITY

Ask more, better, and targeted questions. Apply better listening skills.

EMPATHY

Stand in the shoes of your colleagues and customers.

TRUST

Establish a partnership based on: • trust in you, • trust in what you say, • trust in the data.

FIGURE 1.1  Traits of great data communicators.

CURIOSITY As children, we are curious people. Unfortunately, that curiosity is often removed intentionally and unintentionally from us by familial and societal norms. Good storytellers and translators harness their

6 • Data Storytelling and Translation

inner child and stay curious. Curious people ask questions. Curious people seek out current information. Curious people look for datainformed answers. Curious people don’t just accept the status quo. Curious people look to explore and learn. Each of us benefits from being more curious. In this book, we will cover a lot of topics related to curiosity. In the meantime, if you happen to have a kid, this might be an opportunity to learn from them by observing their curiosity and appreciating it more. Take a moment and observe your kids or your relatives’ kids and note how they engage with and communicate with others. They fearlessly and actively seek to understand their environment and others while also communicating their own perspective through their forthright tone, confident body language, and unabashed communication. We can all benefit from being more childlike in our curiosity. As a storyteller and translator, let your curiosity lead you in asking new questions followed by active listening, thoughtful experimenting, and continuous learning. Then, help other people be more curious. These practices will help you be a better storyteller and translator and will improve your reputation. In particular, Chapters 3 and 4 will address how to ask good questions, be a better listener, and frame problems. In the meantime, a quick challenge for you is this: consciously ask three questions to someone else in order to understand something that you wouldn’t have asked normally.

EMPATHY Empathy is best defined as the ability to understand what another is experiencing, feeling, and thinking. People may also relate empathy to emotional intelligence, or emotional quotient (EQ). Most simply, empathy can be summed up as the ability to put yourself in another person’s shoes. This can be challenging to put into practice. People tend to overestimate their level of emotional intelligence. Or, they may have a high EQ in certain circumstances, and a low EQ in others. Your goal, of course, should be to apply a high EQ as often as possible just like you want to be applying a high IQ as often as possible. This not

The Age of the Data Translator • 7

only takes effort but also planning. We will be covering some things to think about here to prepare yourself for a better EQ. Empathy is vital as a storyteller and translator. Without empathy, your communication becomes a guessing game. The more we target our communication from and to the listener, the more successful both parties will be. Throughout this book, you will hear about different topics and techniques that will enhance your empathy. Hopefully, applying these techniques will not only help you to better empathize with your colleagues and customers, but also with your family and friends.

TRUSTWORTHINESS Trust is a building block of all relationships. The more we trust someone, the more we like them and are willing to sacrifice for them and with them. The more we trust someone, the less we need to check and verify their work, resulting in faster outcomes. The more we trust someone, the easier we look past their mistakes and towards the next win. As a storyteller and translator, you will have people that trust you instantly until you break that trust. We as people want to trust; trust makes work more efficient and easier. But, for some, trust means vulnerability and in turn fear. In some cases, you will need to build that trust. Certainly, the ability to empathize will help you to be better at this, but trust is more than just empathizing with others. Trust means doing what you say you will do. Trust is acting ethically even when there is pressure to not do so. Trust is suggesting or making human-centered and data-driven decisions, not purely selfinterested decisions. Trust involves caring about what you do and why you are doing it. And most importantly, trust means doing these things consistently. As the saying goes, trust can take years to build, and seconds to break. In this book we will cover topics that are aimed at helping you build trust. It is vital to remember that trust can be destroyed quickly and takes a long time to build back up. Even when people are not looking, keep being a person worthy of people’s trust.

8 • Data Storytelling and Translation

WHO IS THIS BOOK FOR? As previously mentioned, data communication skills can and should be learned by all, no matter what your role and experience. This is a point that is worth repeating because too many people either think that data communication is someone else’s job or that they just cannot be good at it. These negative mindsets are detrimental to your progress, and the quicker you can get engaged and past these two hurdles the faster your learning will be. It is important to note that this book was intentionally made for people of all experience levels, and so it has been written as something even a beginner can pick up, read, and apply. However, as we move further into the book, more intermediate and advanced topics will be covered. After all, our goal is to help beginners become advanced data communicators.

HOW THIS BOOK IS LAID OUT AND WHAT TO EXPECT This book is laid out so that each chapter can be read individually, and you can skip ahead and read a chapter of interest. There may be some reference to prior chapters, but each main chapter stands on its own. This book is not meant for passive engagement. Each chapter has thought experiments and activities to conduct. Applying knowledge to situations is the best way to nail down concepts. Further, applying these approaches in real life situations can be important practice. Learn from what works for you and what doesn’t. A big part of becoming a better storyteller and translator is being curious and willing to experiment. Then, based on those experiments identify what is successful for you and what isn’t and adjust. At the end of each chapter, there are references and citations. This book is based on the wisdom and experience of so many others. These references don’t reflect all there is to know, but they do cover any especially pertinent and relevant topics you might need to start your journey.

The Age of the Data Translator • 9

There are additional resources available online that you might want to explore. This additional learning content is broken down by chapter and can be used to further expand upon and supplement the information within this book. In addition to the nine main chapters following this one, there is one subchapter for each main chapter. These subchapters are created from segments of video interviews conducted with experts in data communication and storytelling. The text within this book consists of the highlights from these interviews; access to the entirety of the interview can be accessed in video, audio, and transcript form online. If you have the time, take the opportunity to access these materials. Lastly, at the end of this book there is an epilogue that shares with you many recommendations to continue your data translator advancement journey. This book hopefully is just one step of many you will take along this journey.

CHAPTER

2

All Decisions Start With People

W

e live in a world where we each make hundreds of decisions each day. Some decisions are life changing. Many are small and seemingly inconsequential. Some are conscious choices, and some are not. All these decisions have an impact on the health, wealth, and happiness of yourself and others. In other words, all decisions start with people and impact people. Thus, the more you focus on understanding and connecting with the people you work with and for the more successful you will be in your decision-making. The ability to understand and empathize with those around you is the basis of being a good storyteller and translator. After all, the goal of a data communicator is to simplify complex problems, inform decision-making processes, and provide solutions. Understanding people is not easy. People are messy. Our personalities, our actions, our thoughts, and our emotions are bigger factors in what we say and do and the decisions we make then we want to admit. This “messiness” may be the result of a bad day or a hasty decision. This messiness may be the result of an uncooperative or negative colleague, or it could be caused by a lack of direct experience producing an uninformed decision. It may even be that this messiness stems from company policies that foster an environment of competition and aggression amongst coworkers. Regardless, how much you embrace understanding and connecting with people will be a big determinant in how good of a storyteller and translator you will be. It is a choice and an ongoing journey that you can always improve. Before we dive into understanding others,

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let’s start first by discussing the human side of data and, in turn, of inwardly understanding ourselves. Because we can only understand others if we understand ourselves first.

START UNDERSTANDING PEOPLE BY LOOKING INWARD FIRST All great data translators and storytellers must consider the people that their data affects. Sometimes this can get lost as is reflected by an ominous quote from Josef Stalin, namely that “The death of one man is a tragedy. The death of millions is a statistic” [Stalin_1]. All too often we take the human side out of data and decisions in the name of being objective. Objectivity is important, but it is also important that we don’t forget that every decision has an impact on people. The human side of data can take many forms. Certainly, the direct impact data has on the people you know and work with is most personal; however, the human side of data can be indirect. For example, how your customer data is used. Or decisions made that impact the health and welfare of your customers negatively. Or maybe the data you collect impacts your customer’s customer. There are as many examples as there are people. One way to start understanding the human side of data and yourself is in personally capturing and then relaying data. Giorgia Lupi and Stefanie Posavec pioneered a creative way of doing this in their book Dear Data [Lupi_1]. Each week for a year they picked a random area in which to capture data. For example, some of the data they recorded covered topics like complaints, laughter, and goodbyes. Once they captured and recorded the data, they created a unique visual component to explain the data, wrote it on a postcard, and mailed it to the other person. One thing Giorgia and Stefanie seem to have gotten from this activity is a better understanding of the underlying data in their lives. Data around us often is abstract and impersonal, but recording things that matter to us in our lives gives us a deeper understanding of data around us, but also the challenges in recording this data and communicating it. By undertaking this simple activity, you will not only develop relevant data communication skills, but you will also gain a better understanding of your coworker’s

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thought processes. In turn, this will allow you to more effectively work with and communicate among one another. Before seeking to identify, understand, and influence our customers, partners, and audience, we must deeply understand ourselves. When looking inward we seek an understanding of what makes us tick. Certainly, this includes acknowledging our strengths, personality, emotions, and preferences. It also necessarily involves recognizing our blind spots, biases, and weaknesses. We all have strengths and weaknesses. Our goal should be to lean into and leverage our strengths while at the same time not letting our weaknesses mitigate our success. Let’s go through a couple of techniques that I hope you can undertake so you can more fully understand yourself and communicate with others. One suggested activity is a variant of the Johari Window. The Johari Window as originally designed is a technique to “help people better understand their relationship with themselves and others.” [Johari_1] To begin, you are provided with a list of adjectives. Within the supplemental materials, we have included the original list of Johari Window adjectives for your use in this exercise. From this list, you choose the five adjectives that you think are your greatest strengths and five adjectives that you think are your greatest weaknesses. The first part is easy enough, but now comes the fun part. You identify a group of people that know you well—family, friends, coworkers, even former managers or customers—and you provide them with the same list of adjectives. Then, they will also select what they believe to be your greatest strengths and weaknesses. Ideally, this is done anonymously so they will be most honest. You can use Google Forms or a similarly free and easy-to-use tool that allows for anonymous feedback. Once you have responded and gotten responses from others, compare your list of strengths and weaknesses against the lists generated by others. Note the discrepancies between your list and others. Generally, not everyone will agree, and these different viewpoints will help you gain valuable perspective about yourself. In a traditional Johari Window you would lay out in a two-by-two matrix where you align and where you differ from others [Johari_1].

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Given that we have the added complexity of both having strengths and weaknesses, we added shading to represent differences. This is demonstrated in Figure 2.1. Pay particular attention to where you might identify something as a strength yet your respondents identify it as a weakness and vice versa. You should also pay careful attention to strengths or weaknesses perceived by others but not by you. For example, in Figure 2.1 “Perfectionist” is identified as a weakness by others but not by you. This may be something that you need to consider and account for.

 

FIGURE 2.1.  Johari Window: Template and fictional example.

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This Johari Window variant allows for input from others in addition to self-assessment, and thus is meant to assist you in self-understanding from your perspective and also from others. Periodically doing an exercise like this is good for everyone but especially as a storyteller and translator. This is just one exercise you might use for self-understanding. Something else that is helpful is creating an “About Me” or “User Manual” for yourself. The idea is that you are compiling your strengths, weaknesses, preferences, and pet peeves all in one document. Pay particular attention to key preferences concerning communication, environment, and so on. Let’s go through different items that you may want to include in your User Manual. Developing it however, is an exercise in self-reflection and self-learning. QQ

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Overview and Goals: A description of you that encompasses your personality and what makes you unique. It should also include something about your goals. Communication: List your communication preferences. What do you love, what do you hate, and what do you feel neutral about? Make sure to include information about what your preferred communication time is and set expectations concerning your response time. Environment: What is your preferred work setting? When do you work best? How do you work best? Meeting: Meetings make up a large part of work, so share your meeting preferences. For example, what time of day do you prefer meetings to take place? What format of meeting do you prefer? What are your meeting pet peeves? Learning and Feedback: Understanding how you learn best and how you best receive feedback or advice is important. Learning formats vary from person to person so helping others know how you learn best is helpful. Some of us want feedback immediately and often. Others prefer a more formal process at some interval frequency. There may also be feedback format preferences. Strengths and Opportunities: Providing your greatest strengths professionally helps others understand both what you value but also how you can contribute. Further, providing opportunities

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where you could use the help of others will allow others both to feel comfortable offering assistance and to build teams that will be able to compensate. Remember that you might want to harness learnings from the Johari Window (see Figure 2.1) when creating an accurate strengths and opportunities portfolio. QQ

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Things That Delight and Annoy: Providing a list of things that make you happy will help people understand what efforts may fit better for engaging. Further, understanding what annoys you will help minimize these items. Fun and Interesting Facts: A section for fun and interesting facts It is a good way to share some things you are proud of and to show off your personality.

Having a User Manual as a storyteller and translator is valuable for providing others insight into yourself. It can also facilitate a personal connection at the same time. If someone shares their User Manual with you make sure you receive it warmly and review it carefully. And, if you are a team leader, then having your team create and share such a manual can be part of a good team-building activity. And, as a team leader, if you do this make sure to periodically review these and update communication methods, learning activities, meeting times, and other relevant operating procedures in order to align with team preferences. Being a successful leader includes being an empathetic leader for your team. Before we move away from understanding ourselves to understanding others there is one more tip to cover. Seek to surround yourself with different points of view, different opinions, and people with experiences different from your own. As a storyteller and translator, you are naturally going to be interacting with many people, but sometimes people can get stuck in echo-chambers and siloes. We need to be conscious of echo chambers and siloes that will influence us and our organization for the worse. Embrace different points of view, learn from them, and at the same time grow personally and professionally from them. It may take time to seek these different perspectives out, but it is generally worth the effort and will hopefully help minimize your blind spots and biases and complement your skills.

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Dave Mathias User Manual QQ

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Overview and Goals: Driven human-centered and data-driven change agent. Passionate about making an impact in whatever I do while enjoying the journey along the way. Strong passion about creating and developing communities. Environment: Work best in the morning. You will see me in the office early or if working from home will be early. I like to have flexibility and a communal work environment. Communication: OO Energizing: One-on-one and small group conversations. Text or chat messages. Video chat over phone chat. OO Draining: Long emails. Emails with bad subject lines. Too many emails. Meetings without agendas. Feedback: I am a direct person and like others to provide direct input with a touch of kindness. Don’t sugarcoat things and don’t delay discussions, but the more done in private the better. Meeting: Best meeting energy for me is in the morning and in-person or video conference. Enjoy standup meetings. Love one-on-one or small groups walk meetings. Hate too many meetings, long meetings, and meetings without agendas and purpose but who doesn’t. If you schedule a meeting over lunch, I may reject attending because it is important for everyone to get a break. Learn: I like learning as a team and in an applied format. Not much of a read and absorb it person. Strengths and Opportunities: OO Strengths: Big picture thinking, empathy, problem identification, communicating with internal and external customers. OO Weaknesses: Detailed thinking (but good in spurts), get easily bored of getting in the weeds activities, and general lack of patience. Things that Delight and Annoy: OO Delight: Enjoy engaging with others on nearly anything intellectual. Grabbing lunch with interesting people. Love sports in general and especially soccer and tennis. Podcast and MeetUp junkie. OO Annoy: People that: are not authentic; overpromise and underdeliver; are not ethical and honest. Fun and Interesting Facts: OO Used to be an organic chemist, attorney, and EMT and now a customerfocused and data-driven entrepreneur. OO Love playing any sport with a racket and benefit from being ambidextrous. OO Lived in 10 states. OO Love doing and watching Improv Comedy. OO Have studied six different martial arts and used to be in the college judo club. FIGURE 2.2.  Sample User Manual: Sample User Manual of the author.

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UNDERSTANDING INCENTIVES AND BIASES As a storyteller and translator one thing that is critical is understanding that incentives do matter. Yes, it is true that sometimes behavior may go against the on-the-face incentives for what might initially seem like an irrational reason. However, when we decode our customers and their likely behavior, we can often predict how they might behave by understanding our customers, their incentives, and their behavioral tendencies. Let’s jump into a few examples of these behavioral tendencies because they play an important and recurring role in your customer’s thought processes and decision making. These items also play a role in your thought process and decision-making tendencies. Just being aware of these tendencies will not immunize you from falling victim to them, but there are tips and processes that can mitigate poor decision making. In this section, let’s start by covering five of the over 180 behavioral science cognitive biases that exist. The concept of a cognitive bias is “a systematic pattern of deviation from norm or rationality in judgment” [Haselton_1]. This is essentially true but the phrasing “norm or rationality in judgment” is not really accurate. These cognitive biases are fundamentally related to our biology as humans so they are not things we can discount but need to account for in others and in ourselves when communicating with data. Without getting too much into semantics let’s get into those five cognitive biases. 1. Confirmation bias: Confirmation bias is the tendency to seek out and prefer information that supports our preexisting beliefs. Maybe we try to find supporting data that supports our beliefs or we more rigorously interrogate data or findings that go against our beliefs. This latter case is something that is more common than we would like to admit and it can result in inaccurate or skewed data. Our best way of mitigating confirmation bias is to create a culture of experimentation. Good experimental design tied with diverse teams that have open dialogue can reduce this form of bias. 2. Status quo bias: Status quo bias is the tendency of an individual or organization to maintain the current state of affairs and resist ben-

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eficial change. Often cognitive biases will have alter ego cognitive biases that push back against them. In this case the “status quo bias” has the alter ego “do something bias.” The “do something bias” has the desire to make a change for change’s sake. This can also be dangerous and understanding if people you are working with just want to be part of a change is important. Understanding that we face both “Status quo bias” and “Do something bias” and how we can leverage these and balance them in our data communication is important. 3. Choice architecture: There is a lot of behavioral science around choice. You can influence choice by “organizing the context in which people make decisions” per Thaler and Sunstein [Thaler_1]. This may be how things are displayed in the sense of what is shown first, what is highlighted, what is at eye-level, and much more. As a storyteller and translator you have a great degree of influence in picking what is presented and determining how it will be depicted. Understanding how your audience will process information and make choices and aligning your presentation of data with an ethical usage of choice architecture makes you a more trustworthy and reliable partner. 4. Inattentional blindness: Inattentional blindness is the failure to notice something that is visible but unexpected when your attention is engaged with something else. The classic Invisible Gorilla Experiment conducted by Christopher Chabris and Daniel Simons demonstrates inattentional blindness [Chabris & Simons_01]. In this experiment, the viewer is supposed to count how many times six players pass a basketball. When they are doing this a person dressed in a gorilla suit walks into the court and past the players, right in full view. Nearly 50% of people watching this experiment for the first time do not realize there is a gorilla. This example may be comical, but it still indicates that communicating clearly with customers involves discussing the data multiple times and in multiple ways to ensure things are not missed. 5. Hindsight bias: Hindsight bias is our tendency to look back at an event that we could not predict at the time and think the outcome was easily predictable. It stems from a sense of superiority and a sense that “I knew it all along.” When new information comes

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along, we don’t properly process it and factor it in and can make wrong decisions. We can of course mitigate this by creating and recording predictions and then reviewing those predictions and keeping scores. It is harder for our minds to change recorded history. These five cognitive biases were chosen because they influence how data is understood by the consumer. These biases are important to understand and be aware of as a storyteller and translator. In fact, understanding behavioral science is becoming a more important part of differentiating yourself from a good to a great storyteller and translator.

UNDERSTANDING, ENGAGING, AND COMMUNICATING WITH YOUR CUSTOMER As a storyteller and translator, you are always working with different partners and audiences. Let’s start off by treating everyone as our customer. If you treat everyone as your customer, even people internal to the company in which you work, then you will be more focused. So, from here on out, the term customer will encompass everyone you engage with. You will be more effective when you have a personalized understanding of a personalized engagement with, and a personalized communication with your customer. However, you will not be able to personalize everything for every individual customer unless your customer list is short. This is why great storytellers and translators start by segmenting their customers. 1. Customer segmentation: Customer segmentation is a fairly simple process, but it requires a flexible and thoughtful mindset. On the simplest level, a customer segment is a group of people that have similarities. Let’s use human resources on the talent management side of things as an example. Put yourself in the role of a recruiter more specifically. As an individual recruiter you are seeking to fill job openings. Who might the customer segments be for such a recruiter? Certainly, hiring managers would be a customer segment, but this can be broken down further. If you are a recruiter that hires for multiple departments, then each department might represent a different customer

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segment. Or maybe you might break it down into technical and nontechnical hiring managers. Or maybe you might break it down into manager-level hiring and individual-contributor hiring. Other customer segments for different positions would be broken down similarly. The concept of customer segmentation is simple but when defining the different segments it can become complicated. Try to break down the different customer segments within a given role yourself. Remember you can only engage and interact with so many customer segments differently so you may need to consolidate. My recommendation is to manage no more than ten customer segments individually. This is not a magic number. If you have less time to personalize communication and engagement, reduce this number. If you have more time, increase the number of customer segments. Whatever your number, ensure that your aggregated customer segments have aligned interests and behaviors. Remember the purpose of customer segmentation is to help you better understand, engage, and communicate with the customer, and don’t be afraid to fine tune these segments in response to new information. 2. Customer understanding: Once you identify your customer, you still have to do the hard part of understanding your customer. The easiest way to begin this process is to gather information about them. This information may come from conversations with individuals that fall within a customer segment. This information may come from past history of how customers interacted with you or others in your team. This information may come from customer surveys. This information may need to be inferred based on other information. These are all valid information inputs for you to understand your customer as a storyteller and translator.   Specifically, when leveraging data, you want to make sure you create good input mechanisms to better understand your customers. Surveys are certainly imperfect but they provide good feedback from existing customers if done well. Accordingly, create a periodic, anonymous customer survey related to yourself and what you do. Tips to make your surveying process more successful include: QQ

Keep your survey short (less than ten questions).

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Provide both quantitative fixed and qualitative open questions.

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Keep your surveys anonymous for more honest input.

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Keep your surveying costs down with tools like Google Forms, SurveyMonkey, or other free surveying tools unless your organization has a policy against doing so. Communicate decisions factoring in survey responses back to customers that asked for input.

These are all good tips to follow to provide a more successful survey. Ideally, you seek buy-in from other peers and their managers to implement such a customer survey method instead of just going it alone. Not only does this reflect positively on you as a leader, it also will provide better value for your peers and customers. Sample Survey from Talent Management to Hiring Manager Customer About Survey: This is a short hiring-manager survey that will help us in talent management serve you better. This usually takes less than ten minutes to complete, and most colleagues get this back to us within twenty-four hours. It is completely anonymous. Help us help you and thank you again for your feedback and advice. We appreciate your time.



1. On a scale of 1 (poor) to 5 (amazing), please rate each of the following: a. Communication and responsiveness to you and your needs b. Quality of talent identified c. Quantity of talent identified d. Overall experience with talent management 2. What were we outstanding at? 3. What could we have done better? 4. Ideas for making the hiring experience better for you? 5. Ideas for making the hiring experience better for candidates? Thank you for providing your input! This will help in our continued support to get better!

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Another way to seek out information as a storyteller and translator is simply through conversations with others. One-on-one or small group conversations are a great way to get to know your customers and those similarly situated. Aim to have many coffees, lunches, walks outside, or even an in-office conversation with customers. Make sure to capture and write down nuggets of information and advice they provide. Be open in these customer conversations and spend more time listening and understanding. In Chapter 5, we will more thoroughly cover the role of measures, metrics, targets, and objectives, but here we need to touch on these concepts a little as part of understanding our customer. Everyone is being measured, rewarded, and penalized based on different objectives and targets. How hard is it to answer if someone asks you, “What are the objectives and targets that you are being assessed and rewarded for as a professional or a student?” For most people, it is easy to respond. Then, if someone asks “Which of the objectives and targets that you are being assessed for matter the most?” Again, most people don’t have a hard time answering. But, when someone asks, “What are your customer’s objectives and targets?” This question often results in blank stares and questioning looks. Then, maybe a little sadistically you add the question, “And which of your customer’s objectives and targets make the biggest difference in their career? ” Again, the blank stare and questioning look maybe with a little pain added. If you are one of the people that can answer both these questions related to your customers with confidence then great. Congrats, you are among a small minority of people. But, if you are like most, then this is an opportunity to build this into your conversation with them. Engage with your customer in the following areas: QQ

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Problems and Challenges: One of the best things you can understand is the problems and challenges your customer is facing. These problems and challenges will drive much of their time and attention. History: Customers understand history tends to repeat itself and so history often has an oversized influence on people. Understanding your customer’s history will help you understand their lens.

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Communication: Communication preference for both style and frequency is an important item to understand for your customers. Leveraging communication that lines up with a customer’s preferences will give you an empathy advantage from the start. Tradeoffs: There are many potential tradeoffs that exist in every problem and decision, so it is important to understand which tradeoff preferences your customers value most. For example, maybe your customer is risk averse so tradeoffs around quality and risk metrics matter more than growth metrics. Biases, blind spots, strengths, and weaknesses: Biases, blind spots, strengths, and weaknesses can be touchy subjects to discuss. However, these are important items to understand with your customer.

One important reminder is to keep conversations as positive as possible. You don’t want much of the time spent complaining about things or being negative. In addition, you need to ensure that customer information is captured and retained. Having customer information be shareable to others in your team or organization is doubly beneficial (assuming this sharing is in line with internal and governmental data policies). Regardless of whether your customer is internal or external or an individual or team, knowledge of a client can only help you and others to better understand, empathize, and communicate. QQ

Creating customer personas: After a customer segment and customer data input mechanism have been designed, one might create a customer persona to act as an example of a typical person within that segment. By creating a proxy of a customer segment, a team can gain a better understanding of how to engage with the customer segment as a whole. Basically, a customer persona is a way to simplify communication within a team and also provide a joint level of focus to ensure as a team you are presenting a unified and cohesive front for that customer persona.

As part of the customer persona creation, leverage empathy mapping in understanding and forming the persona. Creating an empathy map involves taking a customer segment and identifying what people in that segment “say,” “do,” “think,” and “feel.” Try to eliminate natural biases based on individuals that stand out and are loudest. For example, there may be one or two hiring managers

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that tend to be the loudest in our recruiter example. Doing this empathy map as a team helps minimize this by at least expanding perspectives. Further, as a team, make sure to ask yourself how do we know what the customer “says,” “does,” “thinks,” and “feels?” What data do you have to support it? Is it just one-off data or is there consistent input on this item? This is why when creating an empathy map that you reference supporting data—customer surveys, one-off conversations, observations, and so on. Let’s take the same example of a recruiter in human resources and create an empathy map for the technical hiring manager customer segment. In each of the four segments of “says,” “does,” “thinks,” and “feels,” we have our team’s thoughts identified. Note that we have added supporting data references. The “thinks” and “feels” is a little sparse and we make a note that we need to get more information. Additionally, the “feels” section it looks like it is all based on “inference” or “belief” so we as a team make a note that we need to investigate this more and get more information.

FIGURE 2.3.  Fictional empathy map: Created by recruiting team related to technical hiring manager customer segment.

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When creating your customer persona, you give it a background story. Give it a name and a picture. The goal is this customer persona can be picked up by someone new to the team and they have a good impression of the customer segment. It leverages information gleaned from your empathy map and more. Have fun with developing these as a team. In Figure 2.4, there is a sample customer persona that has been built relating to the technical hiring manager segment we have been discussing.

FIGURE 2.4.  Fictional customer persona: Created by recruiting team related to technical hiring manager customer segment.

Remember, our goal is to help our customers make better decisions. The better we are at understanding, engaging, and communicating with them, the better decisions our customers will make. In this chapter we have covered a lot of topics. Not only suggestions about better self-understanding and better human behavioral understanding, but also ways to better understand our customers. Certainly. leveraging customer segmentation, customer discovery, and customer persona techniques will help you better empathize and engage with your customer. Give these techniques a try and you will not only grow in your knowledge and understanding of your customer; your customer will also grow to sincerely value and respect you as a result of your added understanding and engagement.

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REFERENCES 1. [Chabris_1] Chabris, Christopher and Simons, Daniel, “Selective Attention Test,” The Invisible Gorilla, http://www. theinvisiblegorilla.com/videos.html, 1999. 2. [Haselton_1] Haselton, Martie, Nettle, Daniel, and Andrews, Paul, “The Evolution of Cognitive Bias,” In The Handbook of Evolutionary Psychology, John Wiley & Sons, Inc., 2005. 3. [Johari_1] “Jorhari Window,” Wikipedia, last modified June 9, 2023, https://en.wikipedia.org/wiki/Johari_window. 4. [Lupi_1] Lupi, Giorgia, andPosavec, Stefanie, Dear Data, Princeton Architectural Press, 2016. 5. [Stalin_1] Stalin, Joseph, attributed to Kurt Tucholsky in the Washington Post, January 20, 1947. 6. [Thaler_1] Thaler, Richard, andSunstein, Cass, Nudge, Yale University Press, 2008.

CHAPTER

3

Start With Good Questions and Great Listening

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he world is in constant flux. New technologies, political shifts, cultural trends—all these things continue to transform the functioning of commercial, governmental, and humanitarian enterprises worldwide. This change can result in amazing benefits— improved efficiency, minimized waste, greater convenience— but it often also comes with hidden drawbacks and unforeseen consequences. Even as we continue to advance and innovate, the world is full of problems to be solved.

THE IMPORTANCE OF GOOD QUESTIONS Many of these opportunities for improvement go unnoticed. As a data storyteller and translator, one of your main goals should be to detect what is overlooked and uncover the true problems that lie beneath the surface. The best way to do this is by asking good questions and being a good listener. By fostering communication within your organization, you will be able to uncover and identify opportunities for growth and refinement. If you are reading this as a leader or an experienced practitioner and dismiss yourself from benefiting with this knowledge—don’t! Most of us aren’t great at asking questions and even worse at listening to answers. Verify yourself by recording a couple of your meetings and then watch and listen to your questions and how you listen to others. Some of your worst misses here are the questions you don’t ask. In addition to being better able to identify and solve meaningful problems, a side benefit of asking more questions is that you will

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generally be viewed more favorably by others. According to research published in the Harvard Business Review, asking questions is an important form of interpersonal bonding [Brooks & John_1]. Asking good questions in a productive manner will lead to positive results both professionally and personally. The good news is you have the experience of asking and answering tens of thousands of questions. If you have a child or remember back when you were a child, you might remember all the questions you were asked or asked yourself. In fact, there are various studies quantifying the number of questions children ask. Most young children ask a startling 200 or even 300 questions a day depending on their age. [NoFussTutors_1] Parents are almost fielding a press conference all day long with their young kids. While we start off well asking a lot of questions, the volume of questions we ask declines significantly as we age. As we grow, we not only gain more knowledge concerning the world around us, we also start facing barriers to questions. There are barriers imposed by societal norms or figures of authority within our lives, such as: raise your hands first, save your questions to the end, or direct your concerns to the manager. There are also the barriers that we place on ourselves. As we get older, many of us are afraid to ask questions because we don’t want to be perceived as foolish or lesser-than. In reality, these barriers only limit us. Finding ways to enhance your question-asking skills will only boost your confidence, which in turn allows you to remove your personal barriers. Then, it comes down to you simply taking the initiative to ask questions. If you are a leader, then your goal should be to remove these barriers to good questions. Set an example and ask questions of others. Nudge others into asking questions and getting a discussion going by providing a nonjudgmental setting. Asking questions and having productive discussions is a central part of being a good leader.

THE DEFINITION OF A GOOD QUESTION It is very likely that you have asked hundreds of thousands or even millions of questions. But have you considered the following

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question: What is a good question? Better yet, what is the purpose of a question? Take a moment to answer this question to yourself. Generally, the purpose of a question is to elicit information or to learn something new. Let’s push this a little further though and say a good question is a question that elicits an answer providing meaningful information toward an objective. This definition is not meant to suggest that the meaning behind the question is its presupposed intent; rather, it proposes that good questions seek to advance an objective. Good questions keep the conversation moving forward and are responsive to new information. Good questions also seek meaningful information—information focused on your main goal. Limit questions that focus on minor details when the big picture should be the focus. This is especially true toward the beginning of projects; there will be a time when details need to be dived into after a proper, foundational understanding has been laid. Part of eliciting information is asking open-ended questions, especially early on in a discussion. Avoid asking simple yes-or-no questions will not do that since they limit discussion and ignore context. Further, don’t ask leading questions where you are trying to hint at the desired answer in the question itself. In fact, within legal proceedings, lawyers are prohibited from asking leading questions in this manner. Thus, to improve communication within your workplace, it is important to offer up more open questions that start with why or how and less questions starting with can, is, are, do, and does. Another conversational tactic, common in improv comedy training, is to begin your sentences with “yes, and.” This not only demonstrates that you are listening and absorbing what the other person has to say, it also allows you to build off of what they have put forward. Then, instead of providing a roadblock to conversation, you are more productively moving things forward and branching out in well-informed directions. Good questions are clear questions. Clear questions are straightforward and direct, and they don’t make the customer guess what things mean. Clear questions are tailored to the customer; they balance the line between respecting the customer’s knowledge and not assuming in-depth knowledge. This often involves using terms

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people are familiar with, as opposed to acronyms or industry jargon. For example, some executives ban the use of acronyms entirely in order to build better habits and promote communication. Another important factor when crafting a good question lies in making it concise. Questions shouldn’t have six parts and take ten minutes to ask. Asking questions should involve conversation, not full-blown, one-sided interrogation. Challenge yourself to use as few words as possible to get your point across. Continue to use as little jargon as possible. After diligent practice, it will get easier, and you will be more effective. One exercise that can help you make questions more concise is this: prior to asking an important question, write it down on a piece of paper. Record yourself reading the question aloud. Then, after a break, return to your piece of paper and listen to your recording. With fresh eyes, consider which sections of your question are superfluous. Think about what can be cut. You could even consider sharing your question with an outside party to get feedback on if it is understandable and digestible. Not all questions warrant this level of attention, but important questions can only be helped with a bit of polishing. Another aspect of good question asking lies in being able to get to the heart of a problem. Think of how a child asks questions: directly and bluntly. Good questions don’t dance around the issue or deflect toward minor concerns. They aim at the root cause of a problem. Once again, there is a balance that must be maintained. You need to be polite and courteous, while also being direct and accomplishing what needs to be done. Before we move forward, let’s go through some examples of bad and good questions. For this example, let’s focus on the following scenario: a meeting between a sales manager and a sales analyst after a disappointing quarter. The sales team’s goal is to figure out why they missed expectations and determine how to mitigate such problems in the future. Based on this setting, here are some potential bad and good questions.

Start With Good Questions and Great Listening • 33

QQ

QQ

Bad Questions What is wrong with our sales number? Isn’t it true that sales were down last quarter because the product release was missed?

QQ

What happened here?

QQ

Whose fault is this?

QQ

QQ

QQ

QQ

QQ

Good Questions Who is missing from this conversation that could provide more light on what happened and potential solutions? What could have caused sales to be down last quarter? Based on the answer to the question above, what could we do to verify if this was the case? What were any unexpected activities last quarter that might have impacted sales? What can we do to detect a potential sales quarter miss earlier and act?

These example questions demonstrate the qualities that we have discussed throughout the chapter. A good question is 1. focused on eliciting meaningful information; 2. focused on the topic or problem at hand; 3. clear to the customer; 4. concise for the customer; 5. aimed at the heart of the problem. By following these general rules, you will be able to uncover more pertinent and useful information more efficiently than ever before.

HOW TO ASK GOOD QUESTIONS Now that we understand what makes a good question, the next step is to understand how to communicate said question. Let’s begin with a brief thought experiment. Think about a person who you have engaging and thoughtful conversations with. What does this

34 • Data Storytelling and Translation

person do to make the questions they ask so engaging? What does this person do that sparks a connection and prompts you to think and feel? Write down some of the things that this person does well when asking questions. People who are good question-askers tend to have certain traits in common. They tend to possess a genuine desire to understand other peoples’ viewpoints. They tend to be nonjudgmental in their approach, and, as we discussed in the previous section, they leave their questions open-ended to allow conversation to flow smoothly and easily. They understand the tone and body language that align best with the situation, and they are respectful even during intense debate. Keep these traits in mind as we dive more deeply into the ins and outs of good question-asking behavior. Asking the Right Customer A key part of crafting your questions is having your customer in mind. It is important to acknowledge a customer’s level of understanding when discussing a topic. Asking questions of a customer that they cannot reasonably be expected to answer, for example, is poor form and a waste of time. Putting a customer on the spot in this manner does not benefit anyone; on the contrary, it fosters an environment of negativity and halts the flow of the conversation. Asking unreasonable or pretentious questions can only stifle your progress. Create the Right Setting The environment of the meeting can also play an important role in getting the most out of your questions. A more casual, informal environment can result in a more candid response. Creating a boardroom-style discussion can be effective to bring attention to a particular issue; on the flipside, it is not the most conducive environment for a fleshed-out conversation. Accordingly, evaluating the situation and weighing the pros and cons of different environments makes sense. Understanding the preferences of the person you are asking questions to will help you determine which environment would be most engaging and productive. Consider your options. Should you grab a whiteboard session together to establish a one-on-one rapport? Should the

Start With Good Questions and Great Listening • 35

discussion be recorded for later reference? Would a walk in the park or a visit to a coffee shop result in a more energized conversation? Don’t be afraid to think outside the box and consider creative ways to boost engagement. Your goal should be to understand your customer and what settings will prove most productive for them and you. Asking in the Right Time and Place A quick thought experiment for you to consider: how do you feel when someone speaks up at the end of a long meeting with a specific question that only impacts themselves? Annoyance? Anger? Even if this person was asking the best question in the world, they have not set themselves up for a charitable, good-natured response. Even a good question can go bad by not asking it at the right time and place. Being a good data translator means that you are perceptive to time and place. The easiest way to determine if the time is right to ask a particular question is to put yourself in the other person’s shoes. Think about how their mood might influence their responsiveness. Sometimes that time and place is dependent on the customer. For example, I used to work for an executive that was a little obsessed by the stock market. If you caught her on a good stock market day, you could get nearly anything you wanted approved and she would be willing to converse deeply about things. But if you caught her on a bad stock market day, watch out! No legitimate discussion or decision was going to happen. This might not have been right but being a good data translator means you are perceptive to the right time and place. Most of all, consider (1) how you would feel if you were on the receiving end of the question at that time, and (2) how you would react if you were observing someone else ask that question. It sounds simple and it is. As a data translator, empathy must be your superpower. Defuse With Your Questions Being ready to leverage questions to defuse situations and move conversations forward is a powerful use of questions. It is more efficient to use questions defensively, than to use questions to assign blame or to gain an edge. For example, in a situation where an executive is overconfident but uninformed, it might be tempting to ask aggressive, pointed questions to poke holes in his blind surety; however, it is more productive to discuss something actionable, such

36 • Data Storytelling and Translation

as, “How might we test that?” This question doesn’t unnecessarily target the executive, and it brings them into the conversation. It seeks progress forward. There are several questions that you might use in different situations to resolve conflict, generate new ideas, move ideas forward, or stop wasted discussions. Here are a few questions that you should consider and try: QQ

How might we test that?

QQ

If we started from scratch, what would we do and how?

QQ

If we could only focus on one thing here, what would it be?

QQ

What piece of information do we need that we are missing?

QQ

QQ

If we had an unlimited budget, what would we do? And, if we had our budget cut in half, what would we do? What do we think the root cause of the problem is?

Body Language and Tone Body language and tone are crucial factors in determining how your questions will be perceived. Think of an authority figure—a boss, a teacher, a manager, and so on—that was challenging to work with. What aspects of that person do you remember most clearly? Even if specific instances do not come to mind, it is likely that you recall the way that this person carried themselves and their tone of voice. Perhaps they were angry, domineering, lazy, or simply loud. What this person was saying is likely lost to time, and yet the way that they said it remains embedded within your memory. Tone is something that is powerful. It can be alarming and push people away, or it can be welcoming and bring people toward you. It can be boring and unmemorable. Understanding the impact your tone has upon the listener and determining which tones are more effective is vital to communicate effectively. For example, if you start a conversation with a loud, aggressive question, then it will come off as combative and put the other person on the defensive, regardless of whether or not the words themselves are accusatory. A good way to confirm whether your tone matches your intended mood for a given conversation is to record yourself. Afterward, close your eyes and listen. Experimenting with the playback speed can

Start With Good Questions and Great Listening • 37

allow you to determine whether your conversation was at the right pace—was it too fast or too slow? The people around you can provide feedback on your tone to help you improve as well. Your body language is also an important factor when conversing with others, so much so that entire books have been written on the subject. Recommended books include: QQ

The Definitive Book of Body Language [Pease_1]

QQ

What Every Body is Saying [Navarro_1]

An understanding of body language is necessary for a true expert storyteller and translator. Here, we will focus on a handful of useful and applicable points for you to consider on the topic. To begin, an important part of body language is alignment. Do your words and tone align with the position and motion of your body? Your intentions for a conversation are reflected in your body language. Leaning forward toward a person demonstrates that you are engaged and listening intently. Open conversations are reflected in open body language; you don’t take up a defensive posture by covering your body or crossing your arms. The way that you angle your feet, knees, and shoulders is important. Being square to a person is a more engaging and open position. On the flipside, being at an angle is a subconscious indicator of defensiveness or a lack of excitement. When at a table with perpendicular sides, make an effort to angle your body toward someone. Remain cognizant of the way you hold yourself as you speak, but even more importantly as you listen. Now let’s discuss body spacing. If you are in-person, you want to be close enough to discuss comfortably. At the same time, it is important not to encroach unnecessarily upon an individual’s personal space. Be mindful of any size or height differences between you and the person you are discussing with. Most importantly, if a person steps back and away from you during a discussion, allow them to take some space. Different people have different comfort levels; respect people’s space when having conversations and asking questions. But do not get extremely close in most conversations. There is a person I highly respect and have really engaging conversations with, but whenever we are standing together talking he moves closer to me.

38 • Data Storytelling and Translation

When I back up, he moves toward me again. Yes, I have told him multiple times that this makes me uncomfortable, at first subtly and then directly, but this behavior has continued. At one particular event we attended I decided to have a little fun with the situation, while at the same time I hoped to teach him a lesson. We started a conversation in a crowded reception room. During our thirty minute or so conversation I would move back a little to a distance I didn’t find too close, however he would immediately step toward me. We literally had the conversation all over the room and probably looked like we were dancing a slow Tango, and he did not seem to notice this at all. Afterward I mentioned it to him hoping this would provide a lesson, it didn’t and it hasn’t. The reality is that while I really enjoy intellectually talking with this person and engaging with him, I also do not contact or talk with him as much as I would if not for this tendency of his.. In short, respect people’s space when having conversations and asking questions. Facial expressions and hand gestures can also express a lot of information. Your facial expressions and hand gestures are always relaying emotion: excitement, anger, neutrality, and so on. People are generally most reactive to a person’s facial expressions. This is why something like eye contact can be so important. Try to maintain eye contact while conversing and make sure to blink! This is especially important when doing video conference meetings. Further, smiles can be disarming, but make sure that a grin fits the tone of the conversation. Nodding your head or leaning forward shows engagement in the conversation and is good for fostering positive communication. People are also receptive to gestures made with your hands and arms during discussion. Gestures that leave the plane of your body open and show the palm of your hand tend to be disarming to the listener. Using natural, unhurried movements can help show that you are in control of a situation; fidgety motions—nervously messing with pens or phones—and frantic gestures distract your listener and decrease your listener’s engagement. With the knowledge we just gained concerning body language and tone in mind, now we can move to application. Begin to leverage some of the knowledge you have learned in your communication. First, pay close attention when conversing with others. Go people-

Start With Good Questions and Great Listening • 39

watching. Try muting a television show or movie and focus on the actors’ movements. You can even record your own conversations and try to observe your own signals. Through it all, try to dissect the facial expressions, postures, and gestures that you observe and think on how this body language is correlated with tone and mood. This section is not meant to act as a comprehensive discussion of body language and tone. Rather, it is meant to help you understand that thoughtful body language and tone are necessary for successful communication. We have a lot of habits built up; these tendencies will not change overnight, but hopefully your enhanced awareness of tone and body language will enhance your ability to communicate when asking questions as a data storyteller and translator. Let’s bring together what we have covered so far in this section. To recap, in order to ask a good question—a productive and efficient question—consider whether you are: 1. Asking the right customer 2. Creating the right setting 3. Asking in the right time and place 4. Defusing with the productive questions 5. Using engaging and appropriate body language and tone Before we move onto the next topic, take some time to tackle the following challenge.

  Section Challenge Activity ACT

Now that you know what makes a good question, it is your turn. Think of an upcoming tough discussion you are facing either personally or professionally and write down some questions ahead of time. Make sure your questions meet each of the five points to be a good question. Speak the prepared questions into a video or audio recorder. Listen to your questions and make a note of your tone and body language. Feel free to ask a trusted individual to provide input on your written and recorded questions.

40 • Data Storytelling and Translation

LISTENING: BEING HEARD BY BEING A GREAT LISTENER The famed Dale Carnegie put it simply in his classic book How to Win Friends and Influence People, “Be a good listener” [Carnegie_1]. Tenzin Gyatso, the 14th Dalai Lama expands on this mindset, “When you talk, you are only repeating what you already know. But if you listen, you may learn something new” [Vetter_1]. Data storytellers and translators need to be phenomenal listeners just as much if not more than they need to be good at asking questions. Let’s begin, once more, with a thought experiment. Think of the two people in your life that are the best listeners you know. Why do you feel they are good listeners? What traits do they exhibit? Now, think of the reverse. Who are the worst two listeners you have had in your life? what made these people bad listeners?” We all want to be heard. The reality is that being heard is more than just sitting in the same space as a person and being quiet. It is more than hearing the words people say and being able to repeat them back. There are numerous ways to listen. For example, reflective listening involves restating and clarifying the speaker’s words back to them to limit misunderstandings. On the other hand, empathic listening prioritizes providing speakers with an outlet for their emotions and an open-minded space for being heard. Our main focus is going to be on active listening, which requires the listener to nonjudgmentally listen, fully concentrate, deeply understand, and actively respond with the speaker. Being a strong, active listener is important as a storyteller and translator for three reasons: 1. It helps you build a rapport with the speaker and strengthens communications between the speaker and listener. 2. It allows you to connect to your customers and clients on a more personal level, which leads to a stronger business. 3. Most importantly, active listening will build trust between yourself and the speaker, and it will encourage a willingness to share. The FUR framework, developed by Sarita Parikh and me, is a simple acronym meant to help people quickly and easily apply active

Start With Good Questions and Great Listening • 41

listening within their personal and professional lives. FUR stands for: (1) focus; (2) understand; and (3) respond. Before getting into the FUR framework, it is important to remember that active listening is a skill and like any skill it can be enhanced. No matter your role, you want to put in the effort and continue to be a better listener because it will pay off both professionally and personally. Now, let’s explore the FUR active listening framework together. Focus Focus is about keeping your attention on the speaker and minimizing outside interruptions. In a world that constantly distracts us, this can be a challenge. Here are some tips and tricks to think about and practice so that you are a better focused listener: QQ

QQ

QQ

QQ

QQ

Environment: Put yourself in an environment that is conducive to working and focusing. This may be a quiet room. It may be a walk outside. It could be a quiet library or a bustling café. The same environments that are suitable for asking good questions are often beneficial for active listening as well. External distractions: Remove external distractions. Quiet your notifications and close your office doors to limit interruptions from passing friends or colleagues. If working in a loud environment, use a headset or go to a quiet room before making a call. Internal distractions: Give yourself the space to devote your focus entirely to a single conversation. Toward this goal, try not to line up important meetings back-to-back, Meetings around lunch or toward the end of the day should be avoided if possible. This will help minimize internal concerns and stray thoughts. Beyond that, the only thing to do when distracted is to try to bring yourself back on task as best you can. Positive expectation: You will retain more of a conversation if you listen with a purpose. Ideally, this purpose is an intention to learn. Keeping your focus on the purpose of the meeting and going in with positive expectations will enhance your ability to listen. Getting off track: Sometimes, our minds wander despite our best intentions. We are only human. When you do become distracted, do your best to refocus. If you continue to lose your concentration, it could be a sign that this meeting deserves to be rescheduled

42 • Data Storytelling and Translation

for a later time. Be honest with yourself and the other parties involved and likely an understanding can be reached. An important part of being a good active listener is listening with intentionality. Keeping these concepts in mind and deliberately practicing listening actively is in and of itself an important step toward becoming a better, more active listener. Try to restructure your thinking to become a more focused listener. If you find yourself thinking the following: Instead of . . .

Try . . .

Thought: “He’s like a broken record. . . It’s the same thing, over and over.

Thought: “This is clearly important to him. I need to understand his context and assumptions. What am I missing?”

Thought: “That last meeting went off the rails, what the heck happened?”

Take a deep breath and bring your focus back to right here, and right now.

Thought: “I’m so tired right now.”

Can you listen effectively right now? if not, ask if you can talk at a time when you can focus more effectively.

Another way to minimize missteps and broken concentration is by creating a pre-meeting checklist. This can help you stay on task and be more attentive. Pilots, for example, are required to complete preflight checklists before take-off; this has significantly reduced the number of accidents. Here is an example of a checklist created for a virtual meeting: Pre-Meeting Checklist TT

TT

Reread the meeting invite and any other related emails or messages or prior meeting notes to make sure my mind is refreshed and ready to engage. Make sure to have any pre-meeting research, notes, or questions accessible.

TT

Make sure I am in a quiet space.

TT

Turn off all notifications on cell phone and computer.

TT

Close all programs that are not related to meeting.

TT

Make sure microphone and video are working.

TT

If using video, make sure lighting is good (lit from the front).

Start With Good Questions and Great Listening • 43

TT

TT TT

If using video, make sure I am lined up well to my video camera so my full face is viewable and looking at the camera. Make sure to have a glass of water or other drink. Make sure to have a pen and paper near me so that I can take notes.

By fostering an environment and mindset geared toward focusing our thoughts and actions, we can improve our ability to listen and understand others. Understand Active listening also involves understanding what the speaker is seeking to convey both verbally and non-verbally. This not only includes understanding the superficial meaning of a speaker’s words, but also the purpose with which the speaker said them. The better you are at understanding and communicating on this deeper level, the more you can avoid confusion when engaging with others. Here are some tips and tricks to think about and practice so that you can begin to understand people at a deeper level: QQ

QQ

QQ

Open mind: Keeping an open mind is a critical first step if you want to be an active listener. Coming with a predetermined path is not recommended. This doesn’t mean you shouldn’t prepare prior to a meeting; it does mean that you should not be rigid and firm in your expectations. Tone and body language signals: As we discussed in the previous section, tone and body language both play an important role in communicating and understanding one another effectively. In this case, your goal is to read the tone and body language of the person you are engaging with. Do their words not align with their tone and gestures? This might mean that more questions are warranted. Keep in mind that different people have different ways of emoting and communicating. It is important to understand a person’s normal behavioral patterns before coming to any conclusions. Don’t interrupt: This is one of the most important rules on this list. Do not interrupt. Do not interrupt. Do not interrupt. It will immediately create a combative tone and cause friction. This gets

44 • Data Storytelling and Translation

more challenging when doing virtual engagements; pause before interjecting and be courteous. QQ

QQ

Listen then respond: Focus on listening attentively when someone is speaking. After they have completed their thought, pause, reflect, and then interject with a now fully formed concept of your own. You don’t want to be thinking about how to answer while the other person is speaking because you are more likely to miss things this way. Taking in the speaker’s viewpoints fully before responding allows you to have a more fruitful discussion. Take notes: The goal of active listening is to retain what is said. This can involve grabbing a pen and paper to take a few quick notes. One thing that is becoming more common is recording meetings and having an automatic voice transcription transform the meeting into text for you. This allows you to capture things in detail to review later. Just make sure you get consent from those involved.

Once again, consider these tips as you refine the way that you listen to and process information. If you find yourself thinking this: Instead of . . .

Try . . .

Thought: Release in November? That will never work!”.

Thought: “He is asking for a November release”

Thought: “He is so dramatic. . . ”

Thought: “This topic is really important to him”

Thought: “I’m going to remind her that we tried this before and it didn’t go well”

Thought: “She wants to try a beta release”

Thought: “I’m starving, I wonder what the sandwich special is, today”

Bring focus back

Instead of . . .

Try . . .

Defensive or aggressive posture → arms crossed, fists clenched, eyebrows furled, . . .

Open and welcoming posture → leaning forward, eye contact, face and body relaxed

Disinterested or preoccupied → looking at phone/watch, tapping pencil or foot, looking at other things going on, typing on computer.

Showing enegagement → “Yes” head nods, “Uh huh” or “mmm” verbal cues, more eye contact

The more time we spend considering the viewpoints of others, the better we will be at understanding and empathizing with others

Start With Good Questions and Great Listening • 45

on a deeper level. This in turn will only improve your ability to listen actively. Respond An important part of active listening is the back and forth verbal and nonverbal communication you provide to the person talking. When you respond, you should not seek to usurp the conversation. Instead, the goal should be to acknowledge the essence of what the person has said, to ask for clarity concerning anything that has you confused, and to learn more about the discussion at hand. Responding in this way pushes the conversation forward and is an important part of active listening. Not responding in a meaningful way during discussion leaves the conversation stagnant and with no place to go. Giving a conversation your all, only to receive a lackluster, one-word response is incredibly frustrating for the speaker. This type of disengaged, passive listening is both discouraging for the speaker and a waste of time for the listener. Information is not properly retained, and no forward progress can be made. Great translators appreciate this problem and respond to, acknowledge, learn, and clarify as they converse. Here are some tips and tricks to think about and practice so you are a more responsive listener: QQ

QQ

Pause: Don’t jump to respond right away. Let yourself pause to fully consider the other person’s words and give yourself time to formulate a thoughtful response. Collect your thoughts. In fact, let yourself ask for time to consider and come to a conclusion if needed. Acknowledge: There are many ways to acknowledge the speaker. It can be as simple as making small interjections of acknowledgement—a “uh huh” or “yeah” or “mmm” to nudge the speaker along. Engagement with the speaker can be expressed by leaning in, maintaining eye-contact, and nodding. Facial expressions can communicate things to the speaker without you ever having to interrupt directly; a confused look could prompt an explanation, a smile can provide encouragement, and so on. In this way, even if you are relatively silent, you are contributing to the flow and direction of the conversation.

46 • Data Storytelling and Translation

QQ

Ask: Asking the right sorts of questions can help you more deeply understand the topic at hand. This can mean asking clarifying questions. It can mean asking questions that build off what has been discussed and moving the conversation in new directions. Remember all the factors we talked about earlier asking good questions.

Remember that being a listener must be active and consciously leveraging your body language and words to engage with the people asking you questions will make you a better translator. Even when listening on the sidelines, have the mindset of an active participant rather than a passive observer. Common thoughts and actions: Instead of . . .

Try . . .

Responding right away. . .

Pause. Reflect on what the person has said and gather your thoughts. “Let me think about this for a moment. . .”

Instead of “so you’re looking for a dashboard with X, Y, and Z?”

Paraphrase and ask “It sounds like you’re looking for a dashboard.” [yes] “Can we take a few steps back? What is the client hoping to solve with this dashboard?”

Instead of “A November release? Not a chance.” Instead of a leading question like “Wouldn’t you agree that X is better than Y” Instead of . . .

Try: “It sounds like the date was moved to November. Can you help me understand the background on that?. . .” Try open-ended questions: “What are your thoughts about X and Y?” Try . . .

Defensive or aggressive posture → arms crossed, fists clenched, eyebrows furled, . . .

Open and welcoming posture → leaning forward, eye contact, face and body relaxed

Disinterested or preoccupied → looking at phone/watch, tapping pencil or foot, looking at other things going on, typing on computer

Showing enegagement → “Yes” head nods, “Uh huh” or “mmm” verbal cues, more eye contact

Conveying annoyance/anger in tone → loud tone, edginess

Vocal tone that aligns with message → understadning, partnership, win-win

Being a responsive listener can be difficult, especially when there is a strong difference of opinion or a negative history between you and the other person. Regardless, being actively involved in a conversation allows you to better understand diverse viewpoints and opinions outside of your own. It gives you an opportunity to learn, grow, and understand.

Start With Good Questions and Great Listening • 47

To conclude, let’s briefly bring together what we have covered so far in this section. Active listening involves: 1. Focus 2. Understanding beyond the surface level 3. Responding Do not allow yourself to be passive when listening to others; actively engage. Not only will this help to foster a positive environment for the speaker, but it will also help you deepen your understanding and grow your knowledge. Avoid being a weak passive listener and be a strong active listener! Avoid being a weak passive listener: Mentally multitasking What I’m going to say next Judgment Waiting for chance to talk Cognitive biases and selection bias Status quo

Be a strong active listner by: Mentally unitasking and focused What does the speaker wamt me to understand? Open-minded and curious Taking notes so I don’t forget Open-minded and curious More trust and stronger relatioship

  Section Challenge Activity ACT

Now that you know what it takes to be a strong active listener. Consider the FUR framework for active listening, and identify one thing within each letter—focus, understand, and respond—that you struggle with. Write these three things down and keep them in front of you when on calls or refresh your memory with them before an in-person meeting. Then, for the next month diligently practice on changing behavior related to these three items. Additionally, when you fail to do so, make a note of it, and write down what you could have done instead. When the month is over, assess how you did and consider the ways you have improved.

48 • Data Storytelling and Translation

REFERENCES 1. [Brooks_1] Brooks, Alison Wood, and John, Leslie K., “The Surprising Power of Questions,” Harvard Business Review, May– June 2018. 2. [Carnegie_1] Carnegie, Dale, How to Win Friends and Influence People, Simon & Schuster, 1936. 3. [NoFussTutors_1] “Why Does My Child Ask So Many Questions?” No Fuss Tutors, https://nofusstutors.com/blog-posts/whydoes-my-child-ask-so-many-questions, November 14, 1999. 4. [Navarro_1] Navarro, Joe, and Karlins, Marvin, What Every Body is Saying: An Ex-FBI Agent’s Guide to Speed-Reading People, Harper Collins, 2009. 5. [Pease_1] Pease, Allan, and Pease, Barbara, The Definitive Book of Body Language: The Hidden Meaning Behind People’s Gestures and Expressions, Random House Publishing Group, 2008. 6. [Vetter_1] Vetter, Amy, “These 4 Tips Can Transform You Into a Greater Listener and Business Leader,” Inc., https://www.inc. com/amy-vetter/why-richard-branson-dalai-lama-value-stronglistening-skills.html.

CHAPTER

4

Being Fluent in the Language of Data

A

s a data storyteller and translator, you certainly need to be able to analyze and understand data. Even more importantly, you will need to help others do the same. Understanding data is an integral part of the modern world, regardless of profession: the nurse reading a patient’s chart needs to understand data; the small business owner reviewing her monthly financials needs to understand data; the product manager designing an experiment for a new product concept needs to understand data. Data storytellers need to be able to effectively communicate to and perhaps even teach all these different sorts of people the meaning of data. Accordingly, this chapter is on data fluency or the language of data. Anyone and everyone can become a proficient data translator. In fact, understanding data is a bit like understanding a foreign language. In language classes, you learn the parts of speech, the vocabulary, and then how to put things together into sentences and phrases. Eventually, after enough practice, you are writing and speaking the language fluently. Similarly, learning to communicate data clearly and effectively requires in-depth knowledge of data terminology and procedures. Your goal for this chapter is to gain a solid understanding of key terms, concepts, and applications around data, statistics, and analytics. If you have a large amount of knowledge about or experience with data, then this chapter may seem a little mundane. Contrastingly, if you are in school, are early in your career, or are

50 • Data Storytelling and Translation

simply not fully comfortable with data and analytics, then this chapter will help you begin to form the foundational understanding you need to progress further. We will begin by discussing the building blocks: data itself.

EVERYTHING STARTS WITH UNDERSTANDING THE DATA Data is simply information. This information can come in many forms. It can be compiled in spreadsheets, databases, or files. It can be comprised of sales figures, employee information, or transportation data. Even personal information like the music we listen to, the pictures we take, and the steps we walk could be considered data. It is essential as a data storyteller and translator that you have a strong understanding of your and others’ data. More importantly, you need to understand why data is meaningful and what purpose it serves. To understand and communicate data in this way, you must first understand widely used classifications and terminology.

STRUCTURED VERSUS UNSTRUCTURED DATA Data exists in two basic forms: structured and unstructured. Structured data is data that has a structured or predefined format. Tabular data organized in Excel or Google Sheets, for example, would be considered structured data. For many years, much of analytics was focused solely on structured data like sales, marketing, and inventory data, as well as other tabular forms of data. Now, unstructured data—data packaged in an undefined format—makes up nearly 90% of existing data. Text documents, audio and video files, and images are just some examples of unstructured data. This differentiation between structured and unstructured data is still important. Certain specialized tools and techniques work differently with structured data versus unstructured data. Currently, structured data is easier to manipulate, analyze, and control. As technology advances, the difference between data types becomes less relevant. Still, knowing the difference between unstructured

Being Fluent in the Language of Data • 51

and structured data is valuable when discussing data with those directly involved—namely; data scientists, analysts, and engineers. Categorical versus Numerical Data Another common way to sort data involves categorizing it by type. Most data falls into one of two groups: categorical or numerical. Categorical data is data that can be divided into groups. For example, categorical data can take the form of numbers on a rating scale of 1 to 10, or it can be comprised of text categories like car models or country names. Categorical data is further broken down into two main forms: nominal data and ordinal data. 1. Nominal Data: Nominal data is data that cannot be meaningfully or naturally ordered. This includes data concerning things like car manufacturers, countries, and zip codes. 2. Ordinal Data: Ordinal data is data that falls into natural, ordered categories. Rank is a classic example of ordinal data. For example, you might rank countries according to gross domestic product (GDP) in an economic analysis. Numerical data is data that is part of a measurement or a count. Temperature, distance traveled, and annual sales are all considered numerical data. Just like categorical data, numerical data is made up of two types: continuous and discrete. 1. Continuous Data: Continuous data is data that is measured on an infinite scale. It is not limited to an integer. Temperature is an example of continuous data. There are an infinite number of temperature values. 2. Discrete Data: Discrete data represents data that can be counted. For example, the number of cars that go through an intersection each day is a discrete variable. Discrete data is composed of integer values. Being able to differentiate between these different types of data will help you know how to analyze, manipulate, and format the data. This foundational knowledge is also essential for having meaningful conversations about the nature of the data you collect.

52 • Data Storytelling and Translation

  Section Challenge Activity ACT

Take a quick inventory of five types of data in your life (personal, professional, or better yet both). Then, identify whether this data is categorical or numerical. Lastly, further classify your categorical data as nominal or ordinal and your numerical data as continuous or discrete. As you practice, get used to quickly categorizing data according to type. Clean versus Messy Data Your analysis of the data can only be as strong as the data itself. As a data translator, it is your responsibility to understand the quality and reliability of your data. Fostering this understanding might involve conversations with analysts, engineers, or data scientists or it might involve directly reviewing data yourself. Regardless, understanding your data and its strengths and weaknesses will help you to resolve issues, get the most value out of what you have, and clearly communicate the opportunities and limitations inherent within your data. No organization has perfect data in every instance. There are an infinite number of reasons for messy data. Some of the most common data formatting mistakes include: 1. Missing Data: Missing data is extremely frequent. It could be that someone forgot to record information. It could be an incomplete web form that was submitted. It could be a sensor failure that omitted some data. Regardless, there are multiple ways to compensate for the missing data. One technique involves applying an average or weighted average of the nonmissing data to missing data. If enough data is missing, then it might be necessary to omit the affected data entirely from an analysis. 2. Incorrect Data: Incorrect data is often just as big of a problem as missing data. Incorrect data can be more difficult to identify unless the errors result in obvious outliers. Still, there are various analyses that can be conducted to identify incorrect data inputs. Consider such measures as you evaluate the state of your data.

Being Fluent in the Language of Data • 53

3. Duplicate Data: Duplicate data is when the same data points are recorded multiple times. There are many ways that analysts, data engineers, and data scientists can perform checks to identify if duplicate data is present. Your goal as a data translator is again to take the necessary steps to test for this type of error. 4. Outdated Data: Outdated data is data that is no longer relevant. An email list, for example, will become outdated as people switch accounts. In general, data needs continuous attention to maintain relevance. Ideally, when handling data, you should establish a system to identify and resolve outdated data. 5. Inconsistent Data: Inconsistent data refers to instances where there is contradictory data within a single database. Ten different spellings for a client account, for instance, would be an example of inconsistent data. Inconsistent data can be prevented by implementing sensible data capture processes that will maintain the integrity of your data. Depending on your role, you will have different levels of control over the data. Still, no matter your role, you should seek to understand your data either directly or by asking questions of your colleagues. One of your obligations as a data translator is to ensure and enhance data quality. Now that we have covered some of the key concepts around data that any data translator should understand, it is time to jump into understanding our data better with statistics.

STATISTICS IS THE LANGUAGE OF UNDERSTANDING DATA Having a foundation in statistics is important for all data translators. Statistics is a powerful tool used to communicate the meaning of data. Immense amounts of data can be condensed into a single, impactful statistic. Thus, being able to interpret the validity of a statistic and determine its meaning is vital. Being able to further communicate statistic’s worth to your audience is even more important, especially if that audience has limited experience with and training in statistical interpretation.

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In this section, we will cover the major statistical topics that you will need as a data translator. If you understand these concepts and perform the exercises, then you will have the foundation to build off of as a data translator. Descriptive Statistics Descriptive statistics are statistics that are meant to summarize and describe a data set. This type of statistic is incredibly common. In fact, as a data translator, you will likely use descriptive statistics every day. When you describe the average, the maximum value, or the percentile of a given data set, you are using a descriptive statistic. Throughout this section, will be using the data contained within Figure 4.1, Figure 4.2, and Figure 4.3 to explain and calculate concepts within descriptive statistics. Note that if you normally use Excel or Google Sheets you might find it worthwhile to copy these figures into your program of choice and follow along. The other nice thing about using Excel or Google Sheets is that there are statistical formulas for most of the statistical concepts that we will cover. If you are not familiar with these formulas in Excel or Google Sheets and you use one of these often in your job, then use this as an opportunity to learn more about the tool you already use. Name Abbie Bill Bart Cathy Dave Gary Jill Josh Latisha Manuel Peter Sam Tonya Victor

Test A 82 76 65 76 91 73 98 80 82 76 60 84 66 92

Test B 84 79 75 77 94 78 95 84 78 79 63 83 68 90

Test C 85 73 72 79 93 72 99 87 84 79 68 87 65 87

Grade B C C C+ A C A B B C+ D+ B D+ A-

FIGURE 4.1.  Class distribution example: Fictional data representing a class’s individual tests and grade distribution.

Being Fluent in the Language of Data • 55

Country

Revenue

Cost

Tax

Profit

Brazil

34.2

26.4

0.8

7.0

Canada

15.1

12.6

1.2

1.3

China

106.5

40.5

2.6

63.4

Germany

85.2

74.3

1.5

9.4

Italy

20.8

19.5

4.2

-2.9

Japan

60.4

52.1

3.5

4.8

Mexico

49.8

26.4

2.2

21.2

Turkey

33.6

31.1

0.1

2.4

United Kingdom

80.6

75.2

2.8

2.6

United States

108.2

80.6

8.4

19.2

*All values in table above are in millions of U.S. dollars FIGURE 4.2.  Profit example by country: Fictional data representing company’s profit inCentral millions ofTendency USD in various countries.

Net Promoter Score

Number of Responses

0

0

1

1

2

0

3

4

4

5

6

11

7

13

8

17

9

23

10

14

FIGURE 4.3.  Net promoter score example: Fictional net promoter score (NPS) of a company’s customer feedback.

Now that we have the data, let’s leverage some descriptive statistics to better understand our data. Central Tendency

The central tendency is a measure meant to quantify the distribution of values in the center of a dataset. There are three main

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measures of central tendency that you should understand, namely the mean, median, and mode. 1. Mean: The mean is the average of all the data in a set. It is generally referred to as the average and is calculated by adding all values within a dataset together and then dividing them by the number of data points. For example, the meaning of Test B in Figure 4.1 is 81. 2. Median: The median is the middle value in a dataset. If there happens to be an even number of data points, then it is the average of the two middle values. For example, the median of Test C in Figure 4.1 is 82. 3. Mode: The mode is the most common value in a dataset. For example, in Figure 4.1, three students in Test A scored a 76, so the mode is 76. Understanding the values of mean, median, and mode separately is important; it can be just as important to understand how these different statistical concepts come together and interact. For example, if you plot the (NPSs) identified in Figure 4.3 and add markers for the mean and median values, the result should be something like the bar graph in Figure 4.4.

FIGURE 4.4.  Bar chart of example net promoter score data: Fictional NPS data plotted with mean and median values annotated.

The chart indicates that the median NPS value is 8.0 and the mean is 7.5. Remember the median indicates that the median is the

Being Fluent in the Language of Data • 57

middle of the data set, meaning the amount of responses greater than 8 is equal to the amount of responses less than 8. The mean value is 7.5, meaning that the average is less than the median. This difference indicates that the data is skewed to the left, as the bar chart demonstrates.

Understanding the distribution of your data allows you to draw conclusions about the data set. Consider the shape and skew of your curve. A bell-shaped curve would indicate a normal distribution. Another important factor is the number of peaks, also referred to as the modality. Figure 4.5 displays the three common distributions. Graphs with one peak are unimodal, graphs with two peaks are bimodal, and graphs with three or more peaks are multimodal. Understanding the modality of your data will help you identify patterns and come to conclusions about the data. Each peak helps to indicate a unique segment of the data.

 

FIGURE 4.5.  Data modality examples: The three main types of data modality are unimodal, bimodal, and multimodal.

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Variability

The variability of the data is another important factor to consider during analysis. Variability describes how far data points lie from each other and the center. The most common measures of variability are range, interquartile range, standard deviation, and variance. 1. Range: Range is the difference between the minimum value in a dataset and the maximum value in the same dataset. For example, calculating the range of revenue in Figure 4.2 gives you a value of 93.1, which is the difference between the maximum of 108.2 and the minimum of 15.1. Range is helpful both in determining the dispersal of the data and in recognizing potential outliers. 2. Interquartile range: Interquartile range or IQR is the difference between the 75th quartile value and the 25th quartile value. This value helps you measure the spread of the middle of your data since 50% of your data will be between the 25th and 75th quartile. Even though IQR is a popular measure, there might be different percentile ranges to consider depending on the data set, your objective, and particular industry standards. For example, the 10th and 90th percentile range is a popular percentile range that might also be calculated. 3. Variance: Variance is a measure of the dispersion of your data. The greater the variance, the greater the dispersion of your data. This measure is calculated by taking each value in a dataset, subtracting the mean, and squaring this value. Then, you sum up all these squared differences and divide the result by the number of data points. Many data analysis tools also include a function to compute this tedious calculation for you. 4. Standard deviation: Standard deviation is the most common variability measure. Standard deviation measures the dispersion of your data relative to its mean. It is referenced more than variance because, unlike variance, standard deviation is measured on the same scale as your data. This makes the concept and scale of standard deviation values more straightforward and thus more understandable for your audience. Standard deviation is calculated by taking the square root of the variance. Once more, standard deviation is often able to be computed via a data analysis tool.

Being Fluent in the Language of Data • 59

The standard deviation can indicate a great deal about the data. For example, a small standard deviation reflects a centralized data set. The larger the standard deviation, the more highly dispersed the data. In addition, if the data demonstrates a normal distribution, then roughly 67% of your data will be within one measure of standard deviation above or below the mean. Further, approximately 95% of your data will be within two standard deviations above or below the mean. Finally, approximately 99.7% of your data will be within three standard deviations above or below the mean. These patterns of distribution can be demonstrated using our own data set. The standard deviation for Revenue in Figure 4.2 is $32.5M. The mean of the revenue was $59.4M. This means the range of data one standard deviation above the mean is $26.9M. One standard deviation measure below the mean would be $91.9M. There are ten countries included in this data set. Thus, assuming that 67% of the countries are within one standard deviation of the mean, around six or seven countries should fall within the range between $26.9M and $91.9M. As expected, Figure 4.2 shows six countries within this range. This example demonstrates the power of standard deviation as a measure of variance. You will often hear people talking in the framework of we want to be within two standard deviations. One industry where standard deviation is commonplace is manufacturing. In fact, there is a famed quality process improvement framework called “six sigma” that people in manufacturing often utilize. Six sigma is a reference to six standard deviations of quality since sigma (s) is the Greek letter used to indicate standard deviation in calculations. If three standard deviations of quality represent 99.7% quality, how much do six standard deviations of quality represent? Amazingly it means only 3.4 defects or less per million or 99.99966% quality. Correlation

In statistics, Correlation is anytime two sets of data are linearly related. Data sets can be positively correlated, meaning they move in the same direction, or negatively correlated, meaning they move in opposite directions. In Figure 4.6, the upper chart demonstrates positive correlation. The chart on the bottom is an example of negative correlation.

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FIGURE 4.6.  Demonstrate data correlation: Line chart on top plots A against B which appears positively correlated. The chart on the bottom plots A against C which appears negatively correlated.

It is important to note that even though the data is correlated in Figure 4.6, the movement of each graph does not dip or rise in perfect tandem. Perfectly correlated data sets would move up and down with or against each other at the exact same time and by the exact same proportionate amount. Data sets that demonstrate perfect positive correlation would possess a correlation coefficient of 1.0. Conversely, data sets with perfect negative correlation have a correlation coefficient of -1.0.

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When two data sets are correlated, especially in the perfect fashion described previously, it can feel natural to speculate that the dip in one graph is what causes the dip in the other. It is almost instinctive to assign a cause-and-effect relationship to the data sets. Unfortunately, correlation does not automatically indicate causation. In fact, data sets that are correlated as a result of random chance or an unmeasured, confounding variable are said to exist in a spurious relationship. There is a website, aptly named Spurious Correlations, that has compiled multiple examples of instances where correlation does not indicate causation [Spurious_1]. One such example of a spurious correlation from the site is included in Figure 4.7 [Spurious_2].

SOURCES:  USDA & National Science Foundation; tylervigen.com FIGURE 4.7.  Correlation coefficient example: Per capita consumption of mozzarella cheese correlates with civil engineering doctorates awarded with a correlation coefficient of 0.958648 [Spurious_2].

In this instance, the per capita consumption of mozzarella cheese is unlikely to have any direct causal relationship with the number of civil engineering doctorates awarded. It’s possible that both variables are increasing due to a confounding variable, such as increasing population size. Alternatively, it is far more likely that the correlation between these two variables is simple, random chance. Thus, before establishing causation between two correlated data sets, make sure to experiment. The best proof way to determine causation is through the scientific process. Define the question that you are seeking to answer. Then, formulate one or more hypotheses deriving likely outcomes as a result of each hypothesis. Formulate hypotheses by thinking of different ways you think causality could

62 • Data Storytelling and Translation

exist and seek to test those cases. Then, design an experiment where each hypothesis is tested. The gold standard for testing causality is the randomized control experiment. This means you have a random group of test subjects where only the thing you are testing is different between the two groups. In fact, the best randomized control experiments have the person carrying out the experiment doing so in a double-blind manner so they don’t know which subject is getting a treatment or not. The gold standard for testing causality is often not practical because of cost and timing, so there are other approaches. We will get into one of the common approaches related in building predictive models in the analytics section of this chapter. For now, it is important to remember when initially thinking about causality once a problem has been identified to start by formulating different hypotheses and how different actions and processes may be connected. Think about ways to investigate and test causality. Now you can start leveraging inferential statistics and analytics to start identifying likely causality. Inferential Statistics Unlike descriptive statistics, inferential statistics are not meant to explicitly describe and summarize a data set. Rather, in cases where evaluating an entire population is not feasible, inferential statistics allow you to make broad inferences about populations based upon a sample. As a result, inferential statistics are invaluable when applied to clinical trials, digital marketing, and any such instances where a hypothesis needs to be tested and a sample size needs to be established. Moving forward, we will focus on inferential statistics as a tool used in surveying. Keep in mind that the concepts discussed apply to all applications of inferential statistics. Let’s consider a scenario where you are analyzing shopping trends across hundreds of malls. As an analyst, you will need to be concerned with the following concepts: 1. Population: In statistics, a population is the group that you will make inferences upon. Populations should be defined with as much precision as possible at the beginning of an analysis. Location, demographics, and other factors can be used to identify the population. For example, using the mall example, you might target adults ages 25 to 45 with one or more dependents that live within a 25-mile radius of the stores in your survey. Now that

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you have determined a specific subset that is relevant to your objective, you can move to the next step. 2. Sample size: Once you have identified a population, you will have to determine the number of people you will need to sample to come to a statistically significant conclusion. In determining the sample size, there are three things that you need to take into account: population size, margin of error, and sampling confidence level. 3. Margin of Error: Margin of error reflects the possibility that the results from your sample do not reflect the overall population that was defined. The smaller your margin of error the better. Usually, a margin of error of 5% is considered appropriate for surveying. Reducing your margin of error often requires you to increase the size of your sample. 4. Confidence level: Confidence level is a measure of how confident you are that the sample reflects the population being studied. This value should generally be above 95%. Once more, increasing the confidence level means increasing the sample size. 5. Calculating the sample size: Now that we know our estimated population size, desired confidence level, and desired margin of error, we can calculate our sample size. This is often as simple as plugging the necessary variables into a tool. A couple free online calculators that are easy to use are Qualtrics and Survey Monkey’s [Qualtrics_1] [SurveyMonkey_1]. Accordingly, we calculated the sample size in Figure 4.8. Segment Researching

Population Confidence Margin Size Level of Error

Sample Size

Group 1: Adults 25-45 with at least one child living within 50 miles of the mall

546,211

95%

5%

384

Group 2: Adults 21-65 living within 2-hour drive or 3-hour flight that have done a mall vacation one or more time in last year

1,203,548

95%

5%

385

FIGURE 4.8.  Mall example survey sampling: Sample size calculated for the two fictional groups identified to perform market research.

64 • Data Storytelling and Translation

  Section Challenge Activity ACT

Take the two groups identified above and try implementing different confidence levels and margins of error to see how much the required sample size changes. Then, create a data visualization to better understand the impact of both confidence level and margin of error. Thus, as this example demonstrates, understanding how to implement and interpret descriptive and inferential statistics is incredibly important if you mean to understand and manipulate data with nuance and clarity. As you continue, remember: you cannot fully communicate that which you do not fully understand.

THE SUPERPOWER OF ANALYTICS AND DATA SCIENCE Analytics and data science both encompass powerful ways to interpret, communicate, and understand data. Analytics is the process of segmenting, scoring, predicting, and otherwise evaluating data. Similarly, data science is a field of study that leverages scientific processes to discover and communicate patterns and insights. Both these disciplines are closely tied. They are so interrelated, in fact, that for the remainder of this book, we will view data science or analytics as synonymous terms, as they typically are for many nonprofessionals. Moving forward, the goal of this section is to help you understand the foundational terminology and concepts around data science so that you can make informed decisions concerning your usage of these technologies and techniques in your work as a communicator and translator. Types of Analytics There are four types of analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Each of these forms serves an important role in analytics.

Being Fluent in the Language of Data • 65

1. Descriptive analytics: Simply put, descriptive analytics explores what happened. Often, this means leveraging descriptive statistics to understand data and trends. For example, a retail store manager might use descriptive analytics to judge how their store performs each day. As a data translator, when you begin to investigate a problem, it is often best to begin with descriptive analytics. 2. Diagnostic analytics: Diagnostic analytics helps to explore why a particular event happened. In keeping with the previous example, perhaps our retail store manager leverages diagnostic analytics to identify why store profitability has gone down over the last month. One of the simplest, most effective diagnostic techniques involves evaluating the correlation between data sets and then testing for a causal relationship. 3. Predictive analytics: Predictive analytics evaluates what will likely happen in the future. This is a more sophisticated and uncertain area of analytics. Still, it can give birth to incredibly worthwhile observations. For example, predictive analytics might predict an uptick in employee attrition in the coming months, which would allow the store manager to proactively recruit new employees so as not to be short staffed. 4. Prescriptive analytics: Prescriptive analytics is another form of forward-facing analysis. It helps to project what should be done next. This analysis is only possible with prior data. Prescriptive analysis, for example, might help you know what and how much extra inventory should be stocked in the event of an incoming hurricane. It could also be used to predict inventory needs during holidays or other extraordinary circumstances. Although this type of data science can be incredibly useful, it is also worthwhile to keep in mind the considerable time it takes to develop and maintain prescriptive analytics. There are a variety of intricacies, both good and bad, associated with leveraging advanced analytics and data science. It is important to note that the area of analytics and data science is evolving tremendously fast, so there will always be new techniques and technologies to account for; however, understanding the basic tenets and foundational technologies will always be essential.

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Foundational Analytics Concepts Artificial Intelligence

The term artificial intelligence refers to computer systems designed to perform tasks that would normally require human intelligence. This includes tasks such as: speech recognition, visual perception, decision making, etc. Machine Learning

Machine learning is a subset of AI that allows a computerized system to learn and adapt without specific, explicit instructions. Rather, the system will evolve based on what it is exposed to. There is so much potential for advancement or growth within using machine learning. Indeed, cars, cell phones, food supply chains, and much, much more have all leveraged machine learning to enhance their efficiency and utility. Simultaneously, there is a significant potential for disaster. Microsoft’s Tay Twitter bot is a famous example of everything that can go wrong with machine learning. When the bot was released in 2016, it was a blank slate, but in less than 24 hours, it had learned a litany of misogynistic, racist, and generally inappropriate comments. Shortly after, Tay was removed. Now, it is simply a reminder of just how quickly machine learning without guardrails can take a turn for the worst in real-life scenarios. Specific versus General Artificial Intelligence

Specific purpose artificial intelligence (SPAI) refers to a computer system that can perform a single, specific task using artificial intelligence. For example, this might be driving or it might be translating a language from English to Spanish. SPAI is developed by leveraging data related to the task and creating a model off this data. However, the concept of artificial general intelligence or AGI is being able to perform any task with artificial, human-like intelligence. This type of artificial intelligence, although under development in the real world, is most commonly seen in the realm of science fiction. One classic example of AGI is the robot Marvin in The Hitchhiker’s Guide to the Galaxy. Marvin is a super intelligent, curmudgeon robot capable of doing all the things humans do.

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Classification versus Regression

Analytics can be broken up into classification problems and regression problems. Classification models are aimed at sorting a given data set into discrete labels or categories. For example, a city planner might need to sort different cars, SUVs, and trucks according to their model or gas mileage in order to better understand the traffic patterns within his city. This would involve designing a visionbased machine learning classification model. Regression models on the other hand are aimed at predicting a particular quantity. For example, the city planner might instead design his model to predict the number of vehicles on a given point at some designated time in the future. This sort of problem would require a regression model. Both classification and regression models are used regularly, and you will almost certainly be exposed to examples of both numerous times in your career. In order to properly utilize these tools, you should have at least a basic understanding of the differences between each model. Supervised versus Unsupervised Learning

Machine learning models can be trained through a supervised learning approach or an unsupervised learning approach. A supervised learning approach provides the desired output to the computer, allowing the system to learn directly from the output and the data provided. For example, providing a computer with a file that includes thousands of labeled pictures of Hondas and Fords to teach the computer how to differentiate between the two would be an example of a supervised learning model. Some models don’t require labeled data. For example, in circumstances when you are investigating patterns and trends, you would not want to bias the computer by giving it prelabeled data. Instead, you would use an unsupervised learning model.

IT’S ALL ABOUT THE DATA Having valid, clean data is incredibly important when performing an analysis. Ensuring you have the volume of data to work with is an equally important consideration. The more data you have, the more accurately your models will perform. In fact, many of the more

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sophisticated artificial intelligence models today require immense amounts of data. Supervised models may require thousands to tens of millions of samples. On top of that, these samples must be labeled appropriately. The validation process can be especially costly and time intensive. All models need to be tested against a validation data set comprised of entirely new data points to measure model performance. The amount of test data needed can be anywhere from 20% to 40% of your total data set depending on the situation. Thus, as you begin designing a project, ensure that you have enough data to properly work on a problem and provide a solution.

DATA AND ANALYTICS AS SERVICES As technologies become more accessible, the amount of data as a service (DaaS) and analytics as a service (AaaS) providers have steadily increased. Amazon, Google, and Microsoft all offer services where a computational model is created for your use. Simply give your provider the data, and they will produce the necessary output. A common AaaS, for example, is able to transcribe a PDF or document image and produce a clean version of the text. Another common tool allows you to send in your customer reviews and receive the sentiment of your customers. Often, using AaaS or DaaS output is an easy way to reduce costs and produce more workable, clean data to further analyze. Consider the resources available to you and take steps to make your work more efficient.

THE TENSION BETWEEN TRANSPARENCY AND PERFORMANCE When designing a model, transparency and performance can become directly competing priorities. The problem is that model performance generally improves with increased model complexity, but the more complicated a model is, the less likely a user will be to determine how its decisions are made. Essentially, increasing transparency can have detrimental effects on model performance, and prioritizing performance can mean losing transparency. Thus, finding a balance between performance and model transparency can be quite challenging.

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Maintaining a model’s transparency is important even for complex models. A neural net used to distinguish huskies and wolves can provide us with a great example of why [Ribeiro_1]. Although the model seemed to perform well enough, the researchers soon discovered that the underlying mechanism driving their model was incorrect. Because they fed the machine a string of pictures like those in Figure 4.9, the model did not register the biological differences that help to distinguish a wolf from a husky.

FIGURE 4.9.  Wolves and huskies: The two pictures on the left are huskies while the two pictures on the right are wolves.

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Instead, the model differentiated wolves and huskies based on the presence of snow. If the environment was cold and snowy, the machine flagged the image as containing a wolf. Thus, understanding the underlying data and mechanisms is important for removing unintentional variables and minimizing model bias. Understanding of the push and pull of model transparency and performance is a vital part of engaging with data professionals. You need to understand potential failure points and know how to ask the right questions in order to work toward solutions. Sometimes the answer is to simplify models. Sometimes the answer is to introduce more complexity. As a data translator you will need to communicate with different parties on these tradeoffs of model transparency and model complexity.

PERPETUAL MODEL BIAS Models learn and develop according to the troves of data that they consume. They are shaped by the data that we choose to input and the data that we choose to exclude. As a result, like with all human endeavors, they can be subject to bias. Sometimes this bias is intentional, other times it is an unknown blind spot. It might even be a simple lack of data from an underrepresented, small population. Regardless, model bias is a rampant problem that all data translators should seek to identify and minimize within their own work. A classic example of potential model bias exists within hiring models. Consider a large health system that has interviewed and hired hundreds of thousands of people. Historically, men made up the preponderance of doctors, and women made up the preponderance of nurses. Although your model might not specifically target an individual’s gender, other factors within a person’s history reflect their sex. Thus, even without conscious intent, your model might be predisposed to favor one gender over the other for a position based on the historical people for that position. The best way to determine whether your model is biased or not is to manually test it. If you see too much variance, then adjust the model as needed until the model is as objective as possible. There are entire books and courses that focus on model bias, and it is an important topic for data translators to understand. Build

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off the foundation provided here and continue to be mindful or your own assumptions as a data translator and storyteller.

  Section Challenge Activity ACT

Think of a model that is used in your organization or that you have read about in the news. Now theorize potential ways that bias could creep into this model. Identify ways that you could mitigate or even eliminate this bias. As you grow as a data translator, continue to broaden your understanding of data, statistics, and analysis. Find new and better ways to understand and communicate your data. Develop your critical thinking skills. In the modern world, we are bombarded with data constantly. As data-fluent translators, we are meant to find meaning for ourselves within the noise and to help others do the same.

REFERENCES 1. [Qualtrics_1] Sample Size Calculator. Qualtrics xm. March 21, 2023, https://www.qualtrics.com/blog/calculating-sample-size/ 2. [Ribeiro_1] Ribeiro Marco Tulio, Singh Sameer, and Guestrin, Carlos, “Why Should I Trust You?” Explaining the Predictions of Any Classifier, arXiv, 2016, https://arxiv.org/pdf/1602.04938.pdf 3. [Spurious_1] Spurious correlations, tylervigen.com, https://www. tylervigen.com/spurious-correlations 4. [Spurious_2] Attribution of Spurious Correlations under CC by 4.0, tylervigen.com, https://www.tylervigen.com/spurious-correlations 5. [SurveyMonkey_1] Sample Size Calculator, Survey Monkey, https://www.surveymonkey.com/mp/sample-size-calculator/

CHAPTER

5

Identify, Understand, and Frame Problems

M

ost problems have multiple solutions. The challenge, then, lies in determining which solution is best. Choosing the ideal solution begins with understanding the problem. One of a data storyteller and translator’s main responsibilities is to identify, understand, and translate problems. In other words, their goal is to determine a solution based on their customer’s needs and aligned with their company’s strategy. Then, they harness the resources necessary to execute their proposed solution. Unfortunately, this is the step in the process where pitfalls begin to emerge. Most organizations are not as efficient in this process as they could be for a combination of reasons: 1. Didn’t know. In this case, a problem is identified. Unfortunately, the person identifying the problem doesn’t know what to do. Who should they contact? How are they meant to utilize data and resources to solve the problem? 2. Miscommunication-frustration loop. In this scenario, the right people are contacted, but the issue is not well communicated and translated. Because of this miscommunication, the intended solution is not fully realized, resulting in an endless loop of issues, miscommunication, and frustration. 3. Wrong Issue. In this scenario, the problem is not framed properly. It could be that the root cause of the problem is not identified and thus continually causes smaller problems that must then be

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addressed. It could also be that a certain problem is too massive to handle, and no attempt is made to break the problem down into manageable, solvable parts. 4. Don’t want the pain. In this case, there is a lot of organizational bureaucracy required to utilize resources. Convoluted protocols result in systems that are challenging to navigate. As a result, only truly massive problems are solved, while other important but less urgent problems are not pursued. Good storytellers and translators help navigate through these pitfalls and minimize the negative impact these inefficiencies have on their organization. The more time that is spent at the beginning identifying, understanding, and framing problems, the more efficiently an organization will be able to analyze and exploit the data. To demonstrate this point, let us start by considering the many areas in which data-informed decisions lead to better results. For example, human resources could screen candidates for a job opening in a non-biased, efficient way using data. Data on advertising trends can give insight to a marketing department that needs to determine the best way to build product awareness. In finance, data can be used to determine the optimal location for a new corporate headquarters. A parks department can exploit spatial data to determine the number and size of facilities necessary for a new park, and an information technology department can calculate the amount of processing power needed for its employees to limit a waste of resources. As these examples demonstrate, knowing how to understand, analyze, and exploit data can help you in nearly every field you encounter. The ability to take advantage of data in this way is gained through experience within an industry and with a role. This necessary experience and knowledge are referred to as domain expertise. Strong domain expertise is a valuable asset for any prospective data storyteller and translator; you should always be seeking to enhance your domain expertise. As a storyteller, domain expertise will help you relay information to your audience in an authentic and relatable manner. As a translator, domain expertise will help you grow more comfortable gauging your audience and communicating with them effectively, this book assumes that you either already

Identify, Understand, and Frame Problems • 75

have or will obtain the domain knowledge you need. Augment this domain expertise with skills concerning problem identification, understanding, and translating, and you will be a valuable data storyteller and translator.

IDENTIFYING PROBLEMS MEANS UNDERSTANDING PAIN Most problems are born out of pain. Either the direct pain of yourself, your colleagues, your customers, or your customers’ customers. Accordingly, identifying problems is observing existing points of pain and anticipating future points of pain. Then, it is investigated that pain and breaking it down into other, smaller points of pain to identify and then analyze the root cause. Too often, we seek to identify solutions prior to understanding the source of the problem—the pain—and then end up falling in love with our potential solution before we really understand the problem. Albert Einstein divvies up his problem-solving time differently, saying “If I have an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about the solution” [Einstein_1]. This sort of mindset prioritizes properly engaging with and considering the problem. Since we cannot go through every type of problem or this book would become a multivolume set, let’s harness some approaches that will help you identify, understand, and frame problems better.

PROBLEM-ASK-VALUE FRAMEWORK Let’s start with leveraging the problem-ask-value (PAV) framework when talking with our customers. The PAV framework is meant to help you better understand your customer’s pain and the possible ways to rectify that pain. PAV asks that each time you engage with customers you need to deeply understand: (1) the problem that your customer is facing, (2) the question that your customer is asking, and (3) the value that a solution would have for your customer and for your organization. This relationship is visualized in Figure 5.1.

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FIGURE 5.1.  Graphical breakdown of the PAV framework.

Understand the Problem Being a good data storyteller and translator necessarily involves understanding the problems faced by your customer. One of the best ways to go about understanding a problem is through good user research and conversations with your customer. Some worthwhile questions to pose to your customer include:

Notice that none of these questions are trying to pass judgment. They are simply geared to understanding the problem. Although these questions do not apply in every situation, this type of nonjudgmental, constructive inquiry can be helpful to internalize when tackling problems. These questions are meant to lead to more questions; they give way to a conversation, It can also be necessary to gather information beyond what your customer can provide directly; you may need to look through a different lens to properly tackle the problem. There are different ways to get this broader perspective. One common way is having

Identify, Understand, and Frame Problems • 77

additional conversations with people who are situated similarly to see if they have the same perspective on the problem. For example, if your client is a recruiter in human resources, you could interview other recruiters in different departments or even outside of your organization. Another option is to consider the perspectives of people indirectly impacted by the problem and its potential solutions. For the human resources recruiter again, this might mean seeking out input from internal hiring managers, recently hired employees, and unhired candidates. The goal is to quickly understand the context around a problem, so that you can ensure your efforts toward a solution is framed well from the beginning. This can mean having conversations with the relevant people, as discussed above; it can also mean conducting survey research or monitoring how people use your application portal through website traffic data. A solid understanding starts with building a proper foundation, and this means you can’t just jump into the analysis. Instead, you must fully acknowledge the relevant people and data. One immediately relevant thing to understand is what type of problem is at hand. In 2007, Gregory Treverton described two types of problems: puzzle problems and mystery problems [Treverton_1]. Puzzle problems have a specific right answer, just like a puzzle that fits together and has a single end result. How many nuclear weapons did the U.S.S.R. have in the 1940s? Treverton considered this question to be a puzzle problem; there is a right answer and information you gather is used to determine that answer. In the context of a business, a more common puzzle question would be this: what segment was most profitable in our retail stores? Or here is a less quantifiable problem: what is a restaurant layout that maximizes front and back staff efficiency when employees possess a great degree of customer experience? Even when a problem is not as easily or cleanly measured, it still may have a particular solution. It is a puzzle problem still. In fact, many of an organization’s day-today issues are puzzle problems. In contrast, mystery problems do not have a definitive answer; rather, they depend on many factors, both known and unknown. They are messy and complicated just as a good mystery is. Many well-intentioned and well-informed people approaching a mystery

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problem may come to a different solution. This is the challenge and beauty of a mystery problem. Treverton’s example of a mystery question involves the rise of terrorism. There is no one answer that will solve the problem of terrorism. Understanding the type of problem at hand will help you to determine the best way to go about finding a solution. Differentiating between mystery problems and puzzle problems gives you the ability to manage your expectations and direct your conversations toward a productive end. Understand the Question It is also important to understand precisely what your client is asking of you. What are their expectations and how can you meet those expectations? Every customer you interact with—whether it be your manager, a colleague, or an external customer—has a desire or need that must be understood. This question may be spoken outright or communicated nonverbally through their actions. Be attentive to other peoples’ pain and struggles. This is an opportunity to put your active listening skills to good use. As previously described, a customer’s problem often expands beyond them. Still, before you begin to widen your net to include other stakeholders’ input, it is important to engage with your customer and establish a foundation. When engaging with the initial customer, here are some good questions to consider:

These questions are meant to help you gain a better understanding of what your customer’s priorities are. This is necessary in a multitude of scenarios. If you are designing a new reporting mechanism for a business partner, you need information concerning the customer’s use and expectations for that mechanism. If you are to supplement a product with a new feature, it is important to consider your customer’s goals and the options available. With every job you take

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on, consider the goal that hides beneath it and how best to go about achieving it. Understand the Value Understanding the value of solving a problem is critically important. Being able to identify the potential value of reducing or eliminating a problem will help you identify the amount of effort and resources that you should spend on a solution. The difficulty lies in determining what value is. After all, value is not a quantifiable factor; you will find yourself unable to measure it precisely and numerically. Rather, you must consider the situation and weigh the problem in your mind for yourself. One way to guide your thinking is to think of the needs of the customer; however, value can’t just be assessed from the lens of the person asking. It can be even more important to consider the value of your actions from an organizational lens. You and your client both fit into a broader system. Here are some questions to consider when determining the value of solutions:

The key is to ask questions that help to contextualize both the needs of the individual and the organization. There are many perspectives to consider when you consider solutions. There are many people who can be affected by a choice. Your ability to sift through these perspectives and weigh their significance and value to your broader goals becomes essential. On that front, an important thing to remember is that individuals are limited by their own perceptions, and each of us are subject to biases and other mental roadblocks. In particular, here are two cognitive biases to be wary of: 1. Time discounting. Time discounting is prioritizing the present more than the future [Frederick_1]. Let’s consider a simple,

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common example. When we grab an ice cream, we understand that it is not good for our long-term health and fitness, especially as the ice cream days become more frequent. Unfortunately, the short-term satisfaction outweighs the longer-term health consequences, and we eat the ice cream.   Let’s consider a more business-oriented example. Would you choose $100 now or $110 a month from now? Now, you are asked a different question: would you choose $100 a year from now or $110 a year and a month from now. Would you believe that far more people will choose the $100 a year and a month from now versus a month from now? Just waiting a month gives you an annualized 214% return and most of us would choose any investment that gave us this. People tend to prioritize receiving money in the short term, as opposed to waiting for a more impressive, long-term reward.   One way of mitigating the impacts of time discounting is to not directly compare current and future rewards. Another way to mitigate time discounting is to use terms like long-term and future when introducing people to concepts in order to prime them for forward-thinking [Sheffer_1]. 2. Loss aversion. Loss aversion refers to the idea that “losses loom larger than gains” [Kahneman_1]. People do not like to lose their money, power, or reputation. Often, these potential looming losses may impact how people think about and prioritize problems. In this way, loss aversion plays a role at a broader, organizational level.   Here is a more tangible example of loss aversion: when you give people the following choices, 50% chance to win $1,000 or a 100% chance to win $500. Clearly on average you should win the same. Generally, more people will choose to win $500. Then, you give people a 50% chance to lose $1,000 or a 100% chance to lose $500. In this case, people choose to risk losing $1000. This is because of loss aversion; people don’t want to lose money. Since there is a 50% chance of losing $0, many won’t want to take the risk. Since there is a 100% chance of winning $500, people tend to go for the guaranteed option. But by always taking the safe option, they miss opportunities to achieve greater things and push beyond their limits.

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  There are different ways to counteract loss aversion. The most important thing is to have an awareness of the aversion to loss that all individuals possess. This will help you acknowledge and mitigate it; it is best not to make decisions out of fear. There might even be a chance that you could manipulate the situation by decreasing the level of loss or increasing the amount or likelihood of gain. Look for opportunities and keep a clear mind. With an improved understanding of the way that value should be measured, some sample questions, and the knowledge of how to counteract two more cognitive biases, it is time to move onto problem reframing.

REFRAMING PROBLEMS An important part of problem solving—of understanding the context of the issue—is being able leverage reframing techniques. Afterall, problems are not only that which is first known and expressed to us. Problems develop and evolve as more information is uncovered. The two main benefits of reframing are that: (1) it will help you adapt to what the true problem is, and (2) it will help you better communicate problems and solutions to others. Reframing should be thought of as an iterative process; you should be revisiting your understanding of the problem throughout testing and analysis to ensure that you are attacking the root cause of the issue. There are many different ways to go about reframing problems. Experience shows there is not one single approach that is best; leveraging multiple techniques is ideal for any complex problem. Engage your inner scientist and experiment on what works best for you and your organization. Consider the following methods for reframing a problem. 1. Point of view: One way to reframe a problem is to consider the issue from multiple different perspectives. This is a valuable thought experiment because most problems touch and impact different groups in different ways, understanding these different perspectives can help you better understand the problem itself and how best to come to a solution. This can involve framing

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problems based on different personas. We talked about personas in Chapter 2. There are a couple of ways to put this into practice: a. You can frame problems by internalizing perspectives based on the personas built. b. You can also gather a representative group of these personas together and engage them in the discussion directly. The latter can be resource intensive, but it can result in invaluable information. 2. System mapping: Another way to reframe an issue is to map out the system’s effected by the problem. Organizations are made up of many departments and often understanding the different systems and how they interact will provide insights on the problems you face. For example, say you are a cardiologist. Your focus is on the heart and other components of the cardiovascular system; however, when there is a treatment or change in the cardiovascular system, it can also impact the brain, kidneys, or other organs in your body. As a result, to fully understand a problem with the heart, you must have an understanding of the related systems and their alignment as well.   The same holds true when you attempt to solve problems in the course of your work. When you begin to investigate a problem, you should start by mapping the systems it touches and identifying the interactions it has in those systems. Doing this should prompt you to think of the problem in new ways; it forces you to consider how to frame the issue. Is there a way to refine this problem so that it impacts less systems or impacts systems less? It might be easier to consider this question with some system diagrams or business process maps as starting points. Otherwise, consult a diverse group of stakeholders that can speak to different systems and processes. 3. Path mapping. Every organization has standard rules and procedures. There are ways that things are done, and there are certain steps that must be taken in order to perform certain tasks. Mapping out each individual step of these processes can help you to understand the good and the bad that comes with each individual step of the process. In turn, this understanding will allow you to determine the root cause of any problems and the consequences

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of any proposed solutions. Are there any steps that can be taken for greater efficiency? What steps are essential? By breaking down the procedural paths within your organization that are relevant to your problem, you will be able to determine the best solution possible.   In the user experience and customer experience spaces, this approach is referred to as journey mapping. Specifically, journey mapping involves diagramming the distinct paths users or customers take when interacting with an organization’s system.   Figure 5.2 provides an example of a journey map for reference.

FIGURE 5.2.  Sample customer journey map demonstrating three customer personas going through a preventive care doctor visit.

4. Timeframe view: Almost everything changes over time. Systems change. Processes change. Norms change. Thus, it can be worthwhile to consider the problem as it exists in the past and the present. Doing this can help to spur unique insight, break down problems more intricately, and project how the problem will advance in the future. 5. Priorities view: When reframing a problem, another important point to consider is this: what is the priority? What can you afford to lose? What do you most hope to gain? It can be useful to weigh the cost of a certain solution against its benefits. This can be done by mapping the value of various solutions against the effort they required on a 2x2 matrix. This process helps provide a more sys-

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tematic method for evaluating different solutions according to finite resources.   Figure 5.3 provides an example of one such 2x2 matrix. The two items highlighted in orange at the bottom right of the matrix represent the items that would have high value and low effort. Unsurprisingly, items within this range are the best to approach initially.

FIGURE 5.3.  Sample 2x2 matrix based on effort versus value. The area highlighted is the hypothetical items that would be most attractive with low effort and high value.

6. Mapping why: The 5 “Whys” is a classic technique used to get to the root cause of a problem. Generally, this technique involves working your way back through a series of causes using why questions. In this process, you continue to question your responses until you don’t have a reasonable answer to give. In that way, you will have discovered the root cause of a problem. The 5 Whys technique is best utilized via a discussion. Multiple knowledgeable people should participate to move the process forward.   Figure 5.4 demonstrates how the 5 Whys technique is performed. For this example, the problem was that the famous Jefferson Memorial in Washington DC was deteriorating and causing a serious hazard to tourists. Interestingly, no other nearby monuments were deteriorating similarly, and so the 5 Whys technique was leveraged in order to reframe the problem and strike at the root cause [ASQ_1].

Identify, Understand, and Frame Problems • 85

FIGURE 5.4. The 5 Whys as applied to the Jefferson Memorial deterioration problem.

By working backward in this fashion, the root cause of the problem was uncovered, and a solution was reached. Ultimately, the Jefferson Memorial was saved simply by turning off lights an hour earlier. To conclude, let’s bring this together from this chapter. In order to put yourself in the right state to identify and evaluation solutions you need to first: QQ

Understand the problem.

QQ

Understand the value.

QQ

Understand the ask.

To truly make sure you understand the problem deeply you should leverage one or more of the six problem reframing techniques

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that we covered. There is no easy button but there are proven techniques if applied will let you understand and frame problems so that you can be a successful data translator.

  Section Challenge Activity ACT

Now that you know how to identify, understand, and frame problems, you should try to put this information into action. Engage with a colleague that is a customer of yours that is seeking something from you. Leverage the PAV framework along with at least one of the problems reframing techniques and put them into practice. Be specific and write out your PAV questions prior to writing down the responses. Test out different reframing techniques until you find the ones that suit you. Make sure to write a diary to yourself after each activity to get your own perspective on how the problem reframing went and what went well and what didn’t. Furthermore, seek out the input of your colleagues that participated in this problem reframing with you. Ask them what went well and what didn’t. And most of all, remember to keep an open mind and think creatively.

REFERENCES 1. [ASQ_1] “Five Whys Jefferson Memorial Example” by ASQ, https://www.youtube.com/watch?v=BEQvq99PZwo(August 12, 2023). 2. [BehEcon_1] “Time Temporal Discounting, ” behavioraleconomics.com, https://www.behavioraleconomics.com/resources/miniencyclopedia-of-be/time-temporal-discounting/ 3. [Einstein_1] “Albert Einstein, Goodreads Quotable Quote, https://www.goodreads.com/quotes/60780-if-i-had-an-hour-tosolve-a-problem-i-d (January 11, 2021). *Note there is dispute if this is Albert Einstein’s quote.

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4. [Frederick_1] Frederick, Shane, Loewenstein, George, and O’Donoghue, Ted, “Time Discounting and Time Preference: A Critical Review,” Journal of Economic Literature (2002),  40(2): 351–401. 5. [Kahneman_1] Kahneman, Daniel, and Tversky, Amos. “Prospect Theory: An Analysis of Decision Under Risk. Econometrica (1979), 47, 263–291. 6. [Sheffer_1] Sheffer, Christine E., Mackillop, James, Fernandez, Arislenia, Christensen, Darren, Bickel, Warren K., Johnson, Matthew W., Panissidi, Luana, Pittman, Jame, Franck, Christopher, T., Williams, J., and Mathew, M., Initial Examination of Priming Tasks to Decrease Delay Discounting, Behavioural Processes (2016), 128, 144–152. doi:10.1016/j.beproc.2016.05.002 7. [Treverton_1] Treverton, Gregory F., “Risks and Riddles,” Smithsonian Magazine (June 2007), https://www.smithsonianmag.com/ history/risks-and-riddles-154744750/ 8. [Wikipedia_1] “Five whys,” last modified January 11, 2021, Wikipedia.org, https://en.wikipedia.org/wiki/Five_whys

CHAPTER

6

Simplifying Insights Through Metrics and Objectives

T

he primary goal in decision making is to determine what the best choice is given the available information. As discussed in Chapter 5, this involves gaining a thorough understanding of the problem and its context in order to frame it effectively. You take the information available to you at the time, understand what you know and don’t know, and then make the best judgment you can. Whether you are creating reporting mechanisms, providing an executive a hallway update, or presenting research findings to key stakeholders, as a storyteller and translator, you are meant to simplify solutions and make data easier to digest. Metrics and objectives are a shortcut in this process. Our focus in this chapter is to learn how to leverage metrics to help others understand an issue and to trigger new insight. A metric is a measure used to assess success and failure and to establish organization objectives. For context, let’s investigate a quick, simplified example of a metric. Many sales teams work with a metric of margin. This business margin is calculated first by subtracting the cost from the revenue and then dividing that number by the total revenue. The result is a margin or profit percentage that measures the profitability of an item, a product line, or even an entire business. This margin may fluctuate year to year based on market conditions, but it helps set expectations for the organization and establish a target to work toward.

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Margin is just one metric that exists. Every organization, no matter if it is a big company, a local government, or a nonprofit, has many metrics and targets that serve multiple purposes within an organization. There is a lot of hard work that goes into identifying, developing, implementing, and maintaining metrics that will help you and your organization achieve your and its mission. And yet, while metrics are useful tools, they are not a panacea. Individual people having contradictory and at times selfish goals can undermine established metrics. As Goodhart’s Law states, “when a measure becomes a target, it ceases to be a good measure” [Goodhart_1]. In other words, when people become more focused on the metric and less on the overarching objective behind it, it can halt your progress and leave room for people to exploit the system. Let’s take a common example that impacts salespeopleheavy organizations with significant commission plans. There are many ways to structure these plans and you are trying to establish a framework that maximizes salesperson performance and organization profitability. To accomplish this goal, suppose that you establish a metric and create a sales commission plan that prioritizes the number of new deals closed. This incentivizes people to bring in new clients, and discourages servicing existing deals and long-term customers, which can have a significant, negative impact on the company’s present and future. The Cobra Effect is another more famous example of metrics gone wrong. It refers to the creation of a metric that unintentionally incentivizes people to make a problem worse [Wikipedia_1]. The example is named after a particular incident concerning the venomous cobras of Delhi. The population of cobras was too great, and the government wanted to reduce their numbers. Thus, they offered a reward for every dead cobra. At first glance, paying people to kill the cobras is a valid way to decrease the population; however, because of the way this mandate rewarded people for bringing in dead cobras rather than for reducing the population, people simply bred cobras to kill them for the reward. Ultimately, this resulted in the population of cobras increasing even more, and the government soon ceased giving people a reward.

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This example is certainly not the only example of incentives leading to unintended results. You might even be able to think of a metric you implemented that had unexpected, negative results. This does not mean that you can get rid of metrics entirely. They are an essential component for all organizations and only become more important as organizations get larger. Having a good process for identifying, developing, implementing, and sustaining metrics is key. Therefore, you must learn to minimize missteps and develop ways to formulate and understand metrics efficiently. This can be done by using the purpose-audience-communicate-target framework (PACT), visualized in Figure 6.1.

FIGURE 6.1.  PACT framework: Graphical breakdown of the PACT framework for putting together winning metrics.

WHAT IS THE PURPOSE? We start by understanding and articulating the purpose of the metric. A metric should: QQ

communicate priorities

QQ

align people and processes

QQ

show progress

QQ

motivate behavior

QQ

define expectations

QQ

reduce uncertainty

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Metrics do not have to fulfill every single objective on this list, but they should have a purpose. Consider these objectives carefully and think about what exactly your metric seeks to accomplish. This will help you to create, communicate, and implement your metrics more effectively. Now, let’s dive a little more deeply into each of these objectives. Communicate Priorities Metrics can help to communicate priorities. Metrics do this by providing a shared language with a shared meaning. There are many objectives to accomplish at any given time, so communicating the priority is important to ensure that everyone is on the same page. This can also help to foster a productive and fruitful discussion. Align People and Processes Ideally, an organization’s metrics are smoothly aligned with an individual’s own goals. In real life, unfortunately, organizations are complex, and there is generally some friction. Mapping out the metrics within an organization can help you better understand where there might be misalignment. Once the source of the tension is located, refining, and updating the metrics can help fix the problem and align people and processes. Metrics align people and processes by sharing aligned goals and incentives that people and processes are measured by. Organizations are complex, so aligning people and processes is important. Show Progress Metrics are also a way of showing progress by helping you understand where you are currently relative to one or more identified objectives. Tracking your progress is important because it gives you the ability to project into the future and correct your course. It gives you the ability to acknowledge mistakes along the way and to improve on those missteps at both an individual and organizational level. Motivate Behavior In theory, metrics are meant to incentivize beneficial behavior and disincentivize detrimental behavior in accordance with broader priorities. People are rightfully an organization’s most valuable asset, so being able to properly reward them when they are doing well and to provide help when they are struggling is important. It is necessary

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to think about the metrics impact on people. After all, people are the ones that are making many decisions each day in your organization. Define Expectations Metrics help to define expectations. They clarify how success will be measured and emphasize what matters most. This allows both employees and customers to establish reasonable assumptions and staves off disappointment and misunderstandings, leading to a healthier working relationship. Reduce Uncertainty Metrics help reduce uncertainty. They allow you to more consistently and confidently sort through priorities and meet targets that align with organizational strategy. In addition to understanding and identifying the primary and secondary purposes of your metrics, you should identify three other core characteristics of metrics. Leading and Lagging Metrics One of the most important concepts to understand is the difference between a leading metric and lagging metric. A leading metric is a metric that indicates another metric will likely occur. The metric that results from a leading metric is known as the lagging metric. Depending on the situation, a metric might be considered a leading metric or a lagging metric. Figure 6.2 is a visual representation of leading and lagging metrics and components that make them up.

FIGURE 6.2.  Leading versus lagging metrics. Graphical breakdown of the four key determinations needed around leading and lagging indicators.

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There are four things you must understand about leading and lagging indicators: 1. Relation: It is important to understand the relationships between metrics. Consider whether a given metric is a leading or lagging indicator. 2. Time: Consider the amount of lead and lag there is between the metrics based on experience. The time between metrics is incredibly variable. For example, it might be six years, six months, or six days. 3. Confidence: Based on experience, consider the amount of confidence you have in the leading metric and the lagging metric. The more experience you have with a metric, the greater certainty and confidence you can demonstrate with its use. Ideally, you might even formulate a model where what-if scenarios can be predetermined and adjusted as needed. 4. Behavior: Finally, consider what sort of behavioral change or action your metric means to prompt. What is the end goal? How do you leading and lagging metrics contribute towards that goal? Efficiency, Effectiveness, and Outcome Metrics The following classifications are also used to clarify the purposes of metrics: 1. Efficiency Metrics: An efficiency metric measures whether we are managing our resources wisely. For example, if we measure the number of calls a salesperson makes per day and create a “sales calls per day” metric, that would be considered a measure of efficiency. 2. Effectiveness Metrics: An effectiveness metric is concerned with whether the use of resources provides a high-quality output. Taking the same salesperson example: if you were to measure the amount of leads gained per call and create a “leads per sales call” metric, this would be a measure of effectiveness 3. Outcome Metrics: An outcome metric investigates how outputs impacting the bottom line. Once again, let us consider the

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salesperson. Calculating the amount of leads that resulted in closing a deal and creating a “closed deals per lead” metric would be a way to measure outcome. Efficiency, effectiveness, and outcome metrics should all work together to ensure that your organization is running efficiently. For example, if you just have outcome metrics, then you will actually be less likely to reach desired outcomes. Your efforts will not be focused, efficient, and effective, and this will result in a loss of resources and time. Focusing solely on the result allows you to neglect the steps towards that outcome. Accordingly, when forming a new metric, make sure to understand if it is an efficiency, effectiveness, or outcome metric. Seek out other metrics to accompany and complement your own. Ensure that there is a balance within your organization. Upward and Downward Metrics Metrics have a hierarchy. High-level metrics affect the entire organization and represent the primary objectives that said organization wishes to obtain. Second-tier metrics target tasks that are related to supporting the primary objective. Beneath that, there are levels of metrics that extend all the way down to the individual goals. All of these metrics build upon one another. Accordingly, whenever creating or updating a metric and its target, you should consider the metrics above and below in the hierarchy to ensure that there is alignment. It is important to strive for harmony and cooperation amongst the different levels, so that priorities and expectations are clear.

WHO IS THE AUDIENCE? A core part of understanding metrics is understanding the audience for your metrics. Just as customer personas informed our decision making in Chapter 2, so to can the formation of different personas inform our use of metrics. Sales Activity Metric Example For example, let’s return to our salesperson example and consider a common sales metric called the “customer contacts

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per month” metric. This metric measures the number of calls an employee makes per month. Now, to narrow down on the audience, consider whose behavior is impacted by the metric? This metric is geared towards increasing a salesperson’s activity with the expectation that more activity will result in more sales. Thus, the audience for this metric is the salesperson and, by association, their manager. Customer Experience Metric Example Here is a more difficult example for you to explore on your own. The net promoter score (NPS) is a high-level proxy for customer satisfaction. NPS is measured by surveying customers and asking if they would recommend your service to others on a score of 0 to 10. Ask yourself the same two audience questions: (1) Who are the NPS metric audiences?, and (2) What is the intended behavior or outcome of each audience based on NPS?

HOW DO YOU COMMUNICATE? In future chapters, we will cover creating narratives, leveraging visuals, and communicating stories. In this chapter, we will focus on high-level communication strategies for initial metrics rollout and ongoing communication. Before getting into communication strategies, you as a data storyteller and translator need to have trust to be able to build trust around metrics. First, let us consider the following thought experiment: think about the ways that customer feedback data is captured by a large restaurant chain. Certain surveys can be captured on paper in the restaurant. Other surveys can be entered by the restaurant supervisors and managers. Customers can also enter surveys digitally by going online and sending feedback. The company has been measuring customer satisfaction using a couple of metrics. First, is the survey NPS asking if you would recommend the restaurant to others? Second, is the number of customer complaints per ticket part of the survey? Thought experiment: How would you rate your trust in these two metrics? Are there ways that you might gain greater trust in these metrics? Why are certain methods more trustworthy than others?

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Trust in metrics is based on a few things. Accurate metrics require data to be accurately and reliably captured. Inconsistent data capture and improper data processing lead to inaccurate metrics; if you cannot be trusted to perform the proper calculations and to reliably report the data, then your metrics will not be trusted. Many people ignore or discount metrics simply because of a lack of trust. Regardless of whether this lack of trust is warranted or not, as a data storyteller and translator, your goal is to be reliable. Be skeptical about data and the processes that generate it. Understand the data as fully and completely as you can. Communicate the results effectively. All these things will build trust and make your results worthy of acknowledgment. This book can’t go through all the ways to determine trust in your data and its process. A few guiding principles that are relevant here are: QQ

Don’t communicate metrics without understanding the data and process to create it.

QQ

Don’t assume others have asked your questions.

QQ

Don’t let perfection get in the way of progress.

QQ

Remember that trust takes years to develop but can be destroyed in minutes.

All these concepts are important to establish and maintain trust. Now, let’s continue the conversation and dive into how to introduce others to a new metric. Initial Communication People can be overwhelmed with too many objectives, metrics, or orders. When rolling out a metric initially, you need to be empathetic rather than forceful or stern. You only get one chance to make a first impression. Here are questions you should think prior to designing your communication: QQ

How will your audience likely perceive it?

QQ

How will this metric impact the audience’s existing workflow?

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QQ

Does your audience understand the purpose of the metric?

QQ

Does your audience understand how this metric is calculated?

QQ

Does your audience understand the target?

QQ

Do other audience metrics and goals get counteracted by this metric?

Think deeply about these questions, and seek out input from people that make up the audience group. If you have a metric related to salespeople and sales managers, get some preliminary input not just on the metric itself but also on how best to communicate it. Try to provide new metrics in a form that the user is used to processing. This input process should start early on—when you are designing the metrics, not when you are about to communicate the end product. Whether you are communicating metrics through dashboards, physical reports, email updates, or some other communication, you want to make sure that you clearly communicate four things: QQ

what the metric is and is not

QQ

what the expected outcome is and why

QQ

how the metric will be communicated and how frequently

QQ

how to access further information and ask questions

This is all about treating people with respect and not having a domineering, controlling mindset. People respond better and are better advocates when they are empowered and respected. Thought experiment: Think about a recent metric that you were involved with either in development and rollout or for which you were on the receiving end of being subject to the metric. Think about what was done well in the rollout of the metric and target. Then, think about what could have been done better regarding the rollout of the metric and target. Ongoing Communication Another aspect of empathetic and respectful communication is being open to further discussion before, during, and after your metric is put into motion. An open line of communication should be

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established between you and your audience with the initial rollout. This ongoing communication helps to bring about long-lasting, permanent change; it makes it clear to your audience that the new metric is something they must carry forward with them. Ongoing communication also helps make it easier for new people just joining a department to be brought up to speed. Consider how to encourage audience engagement over the long term. It is important to think about the method of communication. Keep track of prior communications and evaluate where your audience is most responsive. If engagement is dropping off, then it is time to take action and investigate. A lack of engagement might mean that the introduced metric is not the right fit, or it might indicate that the metric has exceeded its shelf life.

WHO IS THE TARGET? The target of a metric is the expectation that is articulated for the audience. When establishing your target, there are several factors to consider. Many researchers that study metrics, targets, and other methods used to motivate people to achieve objectives suggest that you begin by understanding the purpose of the target [Riopel_1]. Sometimes, targets are created to stretch or push people and teams beyond their limits. Other times, targets represent a bare minimum that you are expected to exceed. If you look at public sector stock market quarterly reporting, for example, it is common to slightly exceed expectations. Understanding what your target represents is important when forming expectations. It is also important to allow people directly impacted by the metric and target into the metric-making process. This is especially important when establishing internal metrics that directly evaluate these individuals’ performance and service. People will be much more likely to achieve metrics if they have a role in helping establish them. In making the metric, they will understand the reasoning behind the metric more intimately and they will be able to have their concerns addressed directly, and they will understand what they are expected to do.

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Accuracy versus Precision Precision is about consistency. In contrast, accuracy is about correctness. It is concerned with whether the target is on the mark towards the goal. You can have cases where on average you are accurate but not precise (see Figure 6.3 [A]). You can also be precise but not accurate (see Figure 6.3 [B]). When establishing your target, the question you need to ask yourself is whether you prioritize precision or accuracy. Your goal should be to be both precise and accurate (see Figure 6.3 [C]), but that is not always feasible in a realworld scenario. Depending on the context, decide which you value, and make sure to communicate this priority to the team.

FIGURE 6.3.  Accuracy versus precision: Visual representation of the three standard accuracy and precision outcomes of metrics and targets.

Thought experiment: Do you think a chief financial officer (CFO) and her team would value accuracy or precision more? What do you think a chief marketing officer (CMO) would prioritize? How would you confirm your suspicions? What is a question you might pose to confirm? Operationalizing Metrics Now that you have gained an understanding of how metrics operate by following the PACT framework, there are only a few more concepts to consider about metrics: 1. Metrics return on investment (ROI): Every metric ideally has a positive ROI. This means the cost of implementing and maintaining the metric is outweighed by the benefit of the metric being present. It is important when doing a metrics ROI calculation to factor in likely outcomes and likely costs.

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Periodically, you should revisit the cost of metrics and determine if it still makes sense to maintain. 2. Number of metrics: Organizations rely on many series of metrics to operate. Metrics tend to be connected to one another; one metric leads to two, which leads to three, which leads to a whole series of metrics. It is important to allow people to process, think, and act rather than overwhelm them with too many metrics. This is especially true when a big change is occurring. As you continue to learn, be cognizant of the number of metrics present and grow your understanding of the system. 3. Metrics outcome redundancy: Metrics outcome redundancy is when a single desired outcome, like a certain changed behavior, is being encouraged through multiple metrics. Often, this redundancy creates additional friction or confusion. If you identify metrics outcome redundancy, then you might want to ask questions and understand whether this redundancy is needed, or whether the metrics can be consolidated. 4. Metrics rollout: A big part of a metric’s success lies in how it is rolled out to an organization. Whether this metric impacts a small subset of a department or the entire organization, it is important to consider your audience and how best to communicate the expectations and purpose of your metrics with them. Do not skimp on preparing to roll out your metrics. 5. Metrics owner: Every metric should have an owner. This owner might be a team or an individual. 6. Metrics lifecycle: Metrics have a lifecycle. You should expect a beginning, middle, and an end. Killing a metric should not be feared. In fact, when initially implementing a metric, try to predict when a metric is likely to expire. 7. Metrics inventory: It is important to understand the various metrics that impact your work. Ideally, your organization has a repository for metrics and associated targets. Figure 6.4 is a sample Metrics Development and Tracking Worksheet. This worksheet is included in the companion files accessible by writing to the publisher at [email protected].

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FIGURE 6.4.  Metrics Development and Tracking Worksheet: Two screenshots of sections of the Metrics Development and Tracking Worksheet with sample information contained.

When looking to implement your metrics, going through each of the items in the preceding list will help to ensure their success. As a storyteller and translator, you may not always be responsible for the actual implementation of metrics, but you can still assist others in making your organizations’ metrics more successful. Metrics play a central role in motivating people, establishing priorities, and communicating goals. The better you are able to understand what metrics people are incentivized by, the more successfully you will be able to act on your data and cause meaningful change. Accordingly, seek out the metrics and targets within your organization and begin to learn.

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  Section Challenge Activity ACT

Now that you know what it takes to have better metrics, it is time that you go through and inventory the metrics that guide your life both personally and professionally. Use the worksheet in Figure 6.4 to guide your reflection. Once you have catalogued those metrics, mark those that are redundant. Ideally, removing irrelevant metrics will help to lighten your workload. Then, consider whether there are any metrics that are not working as good as they could be and think about ways to alter or replace them. Finally, ask yourself if there are any metrics that might be missing. For example, maybe you have been taking steps to improve your health, and you have a metric and a target of weight—a good outcome metric—but you need some efficiency metrics. For example, a goal to eat 1,800 calories a day. Remember that metrics only matter if you measure, evaluate, and act.

REFERENCES 1. [Goodhart_1] Strathern, Marilyn, “Improving Ratings: Audit in the British University System,” European Review (1997), 5(3): 305–321, https://doi.org/10.1002/(SICI)1234981X(199707)5:33.0.CO;2-4 2. [Riopel_1] Riopel, Leslie, (2023, April 6), “The Importance, Benefits, and Value of Goal Setting,” PositivePsychology.com. https:// positivepsychology.com/benefits-goal-setting/ 3. [Wikipedia_1] “Cobra Effect,” Wikipedia, last modified July 6, 2023, https://en.wikipedia.org/wiki/Cobra_effect

CHAPTER

7

Painting Your Data Story

S

tories play an important role in making data meaningful. After all, data is made up of thousands or even millions or billions of stories, and we can’t tell them all individually. Stories are one of the most natural ways people communicate. For thousands of years, stories were passed down as a vehicle to share and better understand history, traditions, and valuable lessons. Storytelling even existed before the written word existed. This natural evolution of storytelling manifests in our books, films, and shows. When thinking about your favorite story, you are likely brought back into the world of interesting characters and the engaging storyline. You may even feel the emotions you felt back when you first read or saw it. We even quote and reference our favorite books, films, and shows, enough that popular culture has become a shared language. When it comes to communicating through data, people tend to ignore and disregard what has worked for so many great fiction and nonfiction books, films, and shows. Instead, they employ an intentionally unmemorable and dull form of storytelling in the name of objectivity. This is done in an effort to let the data speak for itself, but all too often it leads to an audience’s indifference. In this chapter and other chapters, we will cover ways that you can create memorable stories in a trusted manner. We are going to start this off by introducing the Data Story Canvas where we will paint our data story. This chapter focuses on designing and architecting our data story and its elements.

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DATA STORY CANVAS INTRODUCTION The Data Story Canvas identified in Figure 7.1 is a systematic way of designing and laying out your data story. This approach will not work in all instances and certainly isn’t necessary for minor data storytelling efforts. Its efficiency relies on the context of the situation. The Data Story Canvas is most valuable when some of your audience has a narrative that doesn’t align perfectly with yours. Still, as you use this framing technique more often, you will get better at internalizing proper storytelling concepts and leveraging the Data Story Canvas efficiently.

FIGURE 7.1.  Data Story Canvas template.

First, we will go through each of the segments of the Data Story Canvas individually. There will be some sections where we will dive in-depth to provide appropriate context. After laying out the Data Story Canvas, we will cover three examples of the Data Story Canvas being used in relatable examples to give you better perspective on how the Data Story Canvas may be used in practice. Make sure to download your digital and printable versions of the Data Story Canvas included in the companion files by writing to the publisher at [email protected].

DATA STORY TOPIC Think of this first section in the top-leftmost portion of the Data Story Canvas as providing a place to provide the elevator

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pitch of your data story. It should consist of a single, high-level sentence to describe your data story. Using a single sentence forces you to be concise. This sentence should be written in a way that is understandable to all potential audiences. Delivering Your Data Story A data story can be delivered in many forms and over varying lengths of time. In fact, the best data storytellers understand the value of multiple touchpoints communicating with their audience. This is the section to identify the touchpoints and the format that are ideal for your audience. This section will need to be updated as you refine the way your data story is communicated. This section can also become quite large as it lists the multiple formats you will be using to communicate your data story.

THE AUDIENCE This section is dedicated todetailing the audience your data story is targeting. In fact, you may find it helpful to use a different Data Story Canvas for each audience since your communication and messaging may be different according to each segment. It is important to consider the segments that your audience falls within. This segmentation can be performed according to things like personality traits, geography, industry, or demographics. In Chapter 2, we talked about creating audience personas. Leveraging these audience personas is a great start. However, there may be additional segmentation needed based on your audiences’ expectations. We will touch on this preexisting narrative-based segmentation next. The Existing Narrative When you put your data story together, it should contain a narrative. A narrative is belief that shapes your understanding of a situation or series of events. For the purpose of this book, it is the perspective you want your audience to hold once they have consumed your data story. People’s canvases are already covered in paint. Or put less whimsically, everyone already has a preexisting narrative prior to you engaging them. Sometimes that preexisting narrative is strong and sometimes it is not. Sometimes that preexisting narrative aligns

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with your desired narrative and sometimes it does not. Regardless, this preexisting narrative can make sharing your own perspective challenging. Part of understanding your audience is understanding their preexisting narrative. It is important that you have an informed opinion on their beliefs so that you understand the steps you must take to be well received. The best way to come to this informed opinion is by listening to your audience. Consider how they speak about the topic and respond to questions. Another important thing to note is how flexible people are with their narrative. Some people are firmly entrenched in their opinion, and they absolutely will not sway no matter how hard you might push. Others are more flexible and willing to listen. Your experience interacting with others will help to inform your understanding of how your audience might react. Directly conversing with your audience is another way to gauge how open-minded they are and to learn about their perspective. Don’t gossip but do learn how to most effectively communicate with other people. They will benefit from your learnings just as much as you do. Once you have formed your opinion, segment your audience into: (1) promoters, (2) passives, and (3) detractors.





a.  Promoters: Your promoters have a narrative that is aligned with your own. The promoters are your biggest asset and make your role easier. Leverage your promoters to amplify your message. Your promoters’ voices can diversify your message and provide context and reasons to support your proposal. An amplified and diversified voice will help your message be heard. b. Passives: Most of your effort should be directed toward your passive audience members. Because they do not have a strong opinion of their own, you might be able to convince them to your side. To sway your neutral audience, consider: OO

Why is the passive’s narrative misaligned?

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OO

What does the passive prioritize in each situation?

OO

What might get the passive to change their mind?

Persuading passives can be challenging, but it is certainly worthwhile. By changing a passive into a promoter, you have created a strong asset to support your objective. A known passive that becomes a promoter on an authentic basis is a great voice for others. c.  Detractors: Your detractors have a narrative misaligned and are difficult to move at best and immoveable at worst. It is important to listen to different perspectives; however, if you believe in your position, maximize your chances. Work with the detractor as best you can but try to minimize their negativity. Foster a positive environment and continue to make progress forward toward your objective. It is important to have an idea of the breakdown of promoters, passives, and detractors within your audience. If the level of detractors is simply too high, then it might be a sign that it is not the right time or place for your idea. Redirect your efforts elsewhere. Determination is admirable, but blind stubbornness is not productive. What They Need to Know Within this section, identify what you want your audience to do (or not do) or understand. This section can be different, and often is, for different audiences. Be as specific as possible. If there are multiple items that you want an audience to do or understand, list them all. After all, data stories are created to help us make better decisions and obtain certain objectives. Another simple and effective way to think about and lay out your data story is through considering three essential elements: get, keep, and compel.

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THE HOOK Determine what your audience cares about and use that to get them invested in your data story. Your hook should be stated in a personal and relatable manner. The best hooks are both vivid and memorable. Write out your hook in the upper half of the box. For example, you could write, “Let me tell you how we can prevent another case like Rick, a 15-year franchisee of ours who lost his store because of a 9.6% increase in fraud.” Another example of a good hook is, “Last month we lost another great salesperson, Tracy, because of our inability to implement work–life balance that young parents demand.” Also take the time to sketch one or two important data visuals. Data visuals are an important tool; they can be instrumental in helping your audience process data and they are memorability, which adds to strength of your overall story. Later in this book, we will be covering how to create an effective data visual. Keep: Holding Their Attention Within this section, identify the key steps you are taking to keep your audience’s attention. The importance of this section cannot be understated. All too often people spend a significant amount of time crafting their hook and phrasing their call to action. They don’t put any effort toward thinking about how to keep the audience in the middle. The problem is that even if you manage to hook your audience initially, if you do not keep them engaged, they will not be compelled to act on your words. When narratives are aligned, keep your story concise and to the point, like a brief news blurb. Everyone agrees, so it is more respectful to everyone’s time to keep to the salient points and remain focused. There is no need to convince people who are already convinced. When narratives are not aligned, keeping engagement up is vital. This is where stories are won or lost with your audience. Being intentional and thoughtful to your specific audience is important. Therefore, when narratives are not aligned, the “keep” section

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within your Data Story Canvas might become quite complex. It might be necessary to plan a multipronged approach. where a single presentation or report is only the first step in the communication process. It can also mean leveraging a compelling example story that has people in it. After all, people remember stories, not data and charts. Earlier, we discussed how memorable certain books, films, and shows can be. One thing that makes these stories so memorable is the way in which they are structured. There are six classic story arcs used in great books and movies that are laid out in Figure 7.2, namely: QQ

Rags to Riches story arc

QQ

“Man in a Hole”’ story arc

QQ

Double “Man in a Hole” story arc

QQ

Cinderella story arc

QQ

Oedipus story arc

QQ

Icarus story arc

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Painting Your Data Story • 113

FIGURE 7.2.  Six classic story arcs: The six classic story arcs plotted on positive-to-negative emotion on the Y-axis and time scale on the X-axis.

Each of these six story arcs can be effective based on the context of the situation and your own goals and needs. For example, maybe you have a neutral audience that you need to turn to your side. In this situation, you may intentionally use an Icarus story arc—a story with a low emotional endpoint—to motivate them to action. For other groups, it might be more effective to encourage them with a positive story. A Cinderella story arc might be more effective in that instance. Utilizing stories in this manner does not involve changing facts or data. Remember, you should always be seeking to build trust. You

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are simply structuring your story to engage with and motivate your audience. Once again, sketch some potential visuals that might be of benefit to your end consumer within this section. Identify the key visuals that would be important for your audience and helpful for your story arc. Compel: The Call to Action In this section, identify the result you want from your audience in a concise, specific, and actionable way. This might involve having them choose between specific choices or it could point to a more involved task. Once again, you can include supporting, takeaway visuals for your audience within this section. Think of how these specific visuals might compel your audience to action.

THE DATA SOURCE In this section, list your data sources. This will help you evaluate which sources to include in your analysis and which ones to leave out. Transparency about this process—about the data used and the sources its coming from—are essential for building trust and a shared understanding. Both these things allow for a worthwhile discussion to occur.

THE TRADEOFFS This section is for detailing any tradeoffs that are relevant to your audience. A tradeoff involves finding balance between two or more potentially desirable items and making a compromise. In your data storytelling, just like in any analysis, tradeoffs are often necessary. There are two types of tradeoffs that you want to identify here: 1. Audience-specific tradeoffs: Here you will identify audiencespecific relevant tradeoffs that you or others made in any relevant analysis as respects the data story. For example, if you have an audience made up of auditors, then there might be a tradeoff on emphasizing more on risk than return in your data story.

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2. Data and analysis tradeoffs: Here you will identify any relevant data–and analysis related tradeoffs. For example, this may be things like how data was collected, what data was collected or analyzed and what was not, or what data was excluded. Depending on the audience and data story it may be as much as even how things like how missing values were treated or other data preparation approaches. The overall Tradeoffs section can become much more complex and long than it first appears in the Data Story Canvas. Generally, your best bet is to limit your tradeoff list to the top three tradeoffs for each of the two categories above. Remember, this section is meant to get you thinking of the tradeoffs that matter to your audience so that you can communicate with them more effectively. Make sure that your tradeoffs are framed according to your target audience.

YOUR CONFIDENCE In this last section, consider your confidence level in the analysis and recommendations you offer. Remember the two cognitive biases, confirmation bias and hindsight bias, that we discussed earlier. Humans tend to be overconfident; we are evolutionarily wired that way. Accordingly, taking the time to write down a confidence level a is important. It helps us for a few reasons. First, it allows us to genuinely assess ourselves and be honest with our expectations. Second, it allows us to better process the outcome later. Acknowledging our disbelief can allow us to update our analysis and more learn from our mistakes. Third, writing down a confidence level provides a level of honesty and transparency with our audience and builds trust. Expressing zero doubts makes you seem untrustworthy and suggests to others that you haven’t thought things through. Consider what might be incorrect and why, and this will help you to shape your final story. Just like the Tradeoffs section, the Confidence section can become longer than the space indicated on the Data Story Canvas. Try to focus in and record your confidence level on only the top three things.

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DATA STORY CANVAS EXAMPLE We have now walked through all the sections of the Data Story Canvas, so let’s dive into an example Data Story Canvas being used. This situation has been adapted from actual use but anonymized. One of the hardest things to do as a product manager is to advocate for killing a product. In this case you have a software as a service product (World Focus Wave [WFW]) that has had some client uptick, but the landscape is difficult. WFW is sold to businesses to better underwrite new loans. It is used to assess new loan risk by a company’s underwriters prior to taking on a new loan. WFW has a lot bigger competitors. WFW is made up of a series of external data that requires significant cost to access. WFW doesn’t align with core product offerings but was intentionally created originally to stretch the organization into new areas. Add to this, WFW was originally brought to market by the current division leader in his previous role as head of product development before being promoted. You are now the product manager for WFW, and you realize that the economics simply don’t make sense. Even in the best-case scenarios you don’t think it will have the profitability that your company demands of its products, and it takes up a significant amount of engineering resources. You think there are other existing and new products that would better use these engineering resources. Since WFW is an existing product with some clients, and a product your sales team has been selling you need to tell a data story on why the product in question should be sunset. The people that you will need to convince include the division director, director of finance, senior director of sales and sales team, director of engineering, existing customers paying for the product, and the engineering team working on the product. As background, here is the landscape: QQ

Division director: Joy is the originator of the product. You may think that she has a bias toward this product, however, she is also a numbers person that is focused on product success metrics.

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QQ

QQ

QQ

Director of finance: Stan is a traditional director of finance who is razor focused on the numbers and confidence level in those numbers. Senior director of sales: Joe has been a longtime sales executive that is focused on having his team exceed the numbers and sell more products. Director of engineering: Roselyn has been leading engineering for several years and you have a good working relationship with her. Her challenge has been motivating the team, and she has really liked WFW because it was a product engineering really enjoyed creating and evolving.

After doing some additional analysis where you received updated projections from sales for the next three years, including best case numbers, and you have updated your revenue projections. You have also worked with Roselyn to cost out the engineering efforts for maintaining WFW and additional costs for continued investment in enhancing WFW. Further, you have gone through your vendor contracts that WFW relies on and factored in additional costs coming at renewals or annual increases. Now you are ready to schedule a meeting with the division director, director of finance, senior director of sales, and director of engineering to discuss WFW and its future. In preparation you have leveraged the Data Story Canvas to make sure you have everything thought through and covered. You know it is going to be a tough meeting and your desire to sunset the product is not going to be welcomed by all. Data Story Topic: Describe your data story in one sentence: WFW was a well-intentioned experiment, but we cannot be successful, so instead of continuing to invest in WFW, we should spend our resources investing in other opportunities for our customers. How will you deliver your data story? Deliver your data story in one-off meetings with the division director, director of finance, senior director of sales, and director of engineering prior to the Future of WFW meeting. Then, you

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will have the Future of WFW meeting with those same executives along with a couple others in production as well as your engineering manager colleague. Depending on the outcome of this meeting there will be other communication requirements to customers, to the entire engineering team working on the product, and to the entire sales team responsible for selling the product. Most of these avenues will be presentations or conversations. Who is your audience? The audience is the division director, director of finance, senior director of sales, and director of engineering. What is their existing narrative? The division director was originally responsible for bringing WFW to market. They like the product a lot and want to make it work if there is a way, but they also know that the finances of the product have never lived up to expectations. The director of finance doesn’t have a particularly strong view for or against WFW as far as you can tell, but they want to make sure the division is hitting its numbers. The senior director of sales especially likes WFW because it is a more demonstrable product that he thinks is easy to sell. There have been a couple customers that are using it and like it a lot, and the senior director of sales was part of bringing those customers over the line. The director of engineering likes WFW because it is a product that requires a more modern engineering stack and development approach, which makes it easier to recruit and keep her engineers happy to be working on the project. What do you want them to do/know? You want the audience to understand the true WFW profitability (current, likely future, and best case). Get: What is your hook? WFW has been the David fighting Goliath, but unfortunately in this case there are multiple Goliaths that we didn’t recognize at first and now that we do we need to determine the future of WFW. TT

Accompanying visual: Show the different Goliaths that the WFW David is facing.

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Keep: How do you keep their attention? Keep their attention by going through each Goliath concisely and making the session a conversation and not a presentation. This includes regularly having the audience do the presenting of items/ numbers that they had input in putting together. TT

Accompanying visual: A series of visuals that tell the product’s financials from when the product was first being ideated to where we are now.

Compel: What is your call to action or key understanding? Knowing what we know now would you recommend bringing our WFW to market? Now it is time to use this information and make our company stronger by putting WFW behind us. Going back to our analogy of David and Goliath, we must sacrifice David so that his entire family and friends can succeed. TT

Accompanying visual: Show the projected profitability of WFW relative to other products by the company and against the minimum expectations of WFW products.

What are your data sources? QQ QQ

QQ

QQ

QQ

QQ

prior WFW customer numbers and annual sales revenue existing WFW sales pipeline numbers that include current projections but also best-case projections engineering estimates for WFW operational maintenance without new feature development engineering estimates for WFW for continued product evolution factoring in modest feature investment hourly loaded engineering, product, and design costs provided by finance that is tied to the hourly estimates. market share and margin data of competitors in marketplace based on a variety of sources.

What are your tradeoffs? Given this is about executive input it is being focused on getting them to determine the tradeoffs and giving them options. Therefore,

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there have been different sales and engineering estimates put together along with relevant market information. What is your confidence? While you cannot be 100% confident, this is as close as you get based on past experience, the competitive marketplace, and even the best projections that WFW will not even closely make profit margin demanded by our company.

  Section Challenge Activity ACT

Now that you are familiar with the Data Story Canvas, take one of the prior data stories that you put together and plot it on the Data Story Canvas. Do you notice any gaps in your canvas? Take a data storytelling effort you are currently working on or will likely be working on and again leverage the Data Story Canvas from the start. Practice filling it out. Seek out the input of others such as your manager, your peers, and even a trusted person that is a representative audience member for this data story. Ask these people what advice they have to make your Data Story Canvas better.

CHAPTER

8

Leveraging Visuals to Share Insights and Compel Action

I

t is often said that a picture is worth a thousand words. Pictures, pictographs, and information visualizations are natural forms of communication found in early civilizations and cultures dating back many thousands of years. For modern data storytellers and translators, these tools are just as important for understanding information ourselves and, just as importantly, communicating it to others. One of visualization’s greatest strengths lies in its ability to reveal connections that might have otherwise been overlooked and to help others do the same. In particular, data visualization can leverage important aspects of behavioral science to bridge the gap between instinctual thinking—referred to as System 1 thinking— and deliberate, rational thinking—referred to as System 2 thinking [Kahnneman_1]. All individuals have a mixture of System 1 and System 2 behaviors. In fact, these two forces—the emotional System 1 and analytical System 2—have been analogized as an elephant and a rider [Haidt_1]. The elephant is emotional, powerful, and instinctual while the rider is rational, deliberate, and analytical. The rider seeks to have control while on top of the elephant, but that control is left to the decision of the elephant, because at any time it can leverage its much bigger size and strength and take control. Well-designed data visualizations leverage people’s natural tendencies and engage both the rational and emotional aspects of an individual. We will go more into these natural tendencies in the Principles of Good Data Visualization section.

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THE PURPOSE OF DATA VISUALIZATION Data visualization serves two main purposes. The first is as a tool to help us explore data more seamlessly. The second is to communicate information to others and ideally as part of a story. Exploratory Data Visualization Data visualizations allow us to process thousands or even millions of pieces of data together. This allows for comparison, which in turn enables the viewer to recognize trends and uncover patterns within the data quickly and efficiently. Exploratory data visualization was famously used to great effect by an English physician named John Snow. Figure 8.1 illustrates his efforts to identify the source of a cholera outbreak in London in 1854. [Wikipedia_1]. Snow hypothesized that cholera was likely transferable by water.

FIGURE 8.1. JOHN SNOW’S CHOLERA 1854 OUTBREAK MAP.  Emphasis added indicating central population of cases centered around Broad Street water pump [Snow_1].

Accordingly, John Snow mapped the cases of cholera alongside the locations of shared water pumps within London. Due to this visualization, he soon recognized a pattern; the cholera cases were clustered around the Broad Street water pump. Snow determined that this was the likely source of the outbreak. The pump was deactivated, and the cholera outbreak subsided.

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When exploring data initially, there is a tendency to focus solely on descriptive statistics like mean, variance, and correlation. An analysis of the four data sets contained within Figure 8.2, however, quickly demonstrates the weaknesses of such an approach. These four data sets, referred to as Anscombe’s Quartet, have identical or near identical means, variances, and so on. These descriptive statistics are summarized in Figure 8.3. I x

II y

x

III y

IV

x

y

x

y

10

8.04

10

9.14

10

7.46

8

6.58

8

6.95

8

8.14

8

6.77

8

5.76

13

7.58

13

8.74

13 12.74

8

7.71

9

8.81

9

8.77

9

7.11

8

8.84

11

8.33

11

9.26

11

7.81

8

8.47

14

9.96

14

8.1

14

8.84

8

7.04

6

7.24

6

6.13

6

6.08

8

5.25

4

4.26

4

3.1

4

5.39

19

12.5

12 10.84

12

9.13

12

8.15

8

5.56

7

4.82

7

7.26

7

6.42

8

7.91

5

5.68

5

4.74

5

5.73

8

6.89

FIGURE 8.2.  Example data: Displaying Anscombe’s Quartet data [Wikipedia_2].

Property

Value

Mean of x

Accuracy

9

exact

11

exact

7.5

to 2 decimal places

Sample variance of y : s

4.125

+/- 0.003

Correlation between x and y

0.816

to 3 decimal places

Linear regression line

y = 3.00 +0.500x to 2 and 3 decimal places respectively

Coefficient of determination of the linear regression: {\displaystyle R^{2}}

0.67

Sample vairance of x: s

2

Mean of y 2

to 2 decimal places

FIGURE 8.3.  Descriptive statistics of example data 8.2: Displaying Anscombe’s Quartet data descriptive statistics [Wikipedia_2].

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Solely based on this descriptive data, it would be easy to assume that the difference between these data sets is inconsequential. Plotting the data, however, will instantly change your perspective. Despite sharing the same mean, variance, and linear regression line, the scatter plots in Figure 8.4 are incredibly different. Each data set has a unique distribution.

Leveraging Visuals to Share Insights and Compel Action • 125

FIGURE 8.4.  Anscombe’s Quartet visualization: Displaying Anscombe’s Quartet’s data [Wikipedia_2].

Thus, while descriptive statistics provide important insight into your data, alone they are not enough. Removed from context, statistics can even be misleading. Exploratory data visualization can and should be used to supplement your statistics to allow for a fuller understanding and to provide greater clarity. Data Visualization as Storytelling Data visualization is also an important tool for communicating the meaning of data to others. Good data visualization can leverage natural, human instincts to elicit an emotional connection. This connection can transform an otherwise dense, complex set of data into a meaningful story. In turn, this improves your audience’s engagement on top of making the data easier and faster to process. Charles Joseph Minard’s visualization of Napoleon’s march to conquer Russia is an amazing example of how invaluable data visualization can be in telling a story [Wikipedia_3]. This visualization, contained in Figure 8.5, is incredibly efficient in communicating a wide array of information. The map is representative of six different types of data. It details the number of Napoleon’s troops, the distance, the temperature, the latitude and longitude, the direction of travel, and the location relative to specific dates.

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FIGURE 8.5.  Charles Joseph Minard’s Napoleon’s Russian campaign of 1812: Relays the disastrous campaign of France’s attempt to invade Russia in 1812 by Napoleon [Wikipedia_3].

One of the most meaningful and affecting elements of the map is the thickness of the tan and black line denoting the number of soldiers that Napoleon had in his campaign. Napoleon started his campaign with 422,000 men and ended with only 10,000. The immensity of this loss is hard to fully comprehend, but seeing the tan line narrow, morph to black, and narrow even further as it travels back to the start helps the viewer quantify just how many people were lost. The sliver of black representing those that survived is miniscule next to the tan line of initial recruits. The magnitude of this tragedy is displayed masterfully in this visualization. Other examples of data visualizations focused on storytelling can be found in places like the New York Times and other news venues. Take note of the different ways these organizations utilize visualizations to communicate their message.

PRINCIPLES OF GOOD DATA VISUALIZATION Before diving into the principles of good data visualization, it is important to note that good data visualization relies on science, art, curiosity, and empathy. With those tenets in mind, the only hard and fast rule is this: always design your data visualizations for your audience and toward your purpose. Recognize what resonates with and is understandable for your audience and create your visualization with that in mind. Remember what message

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you wish to communicate and ensure that your visualization serves its intended purpose. With that backdrop, we will go through important concepts to keep in mind when creating your data visualizations. Picking the Right Chart Creating the right type of data visualization starts with picking the right chart. The right chart is the one that most effectively accomplishes and aligns with your purpose. Eight Common Purposes of Data Visualization

Data visualizations are ordinarily used to demonstrate: 1. Comparison: Comparing one item to another. Common visuals used to demonstrate comparison include bar charts, pie charts, and tables. 2. Distribution: Showing how data is distributed. Identifying outliers is an important component of distributions. Common visuals used to demonstrate distribution include scatter plots, histograms, and box plots. 3. Data Over Time: Showing how data changes over a period of time. Common visuals used to demonstrate data over time include line charts, stacked area charts, and column charts. 4. Hierarchy: Showing the hierarchy of data. Common visuals used to demonstrate hierarchy include tree diagrams and treemaps.

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5. Pattern: Showing a pattern or trend in the data. Common visuals used to demonstrate patterns include line graphs, scatter plots, and bubble charts. 6. Location: Mapping data over a geographic location. Common visuals used to demonstrate locations include dot maps and heatmaps. 7. Parts of a Whole: Demonstrating how some data fits into the overall group of related data items. Common visuals used to demonstrate parts of a whole include pie charts and treemaps. 8. Relationships: Showing how different data is related or connected. Common visuals used to demonstrate relationships include heatmaps and network diagrams. This is not an exhaustive list of all the purposes that data visualizations can serve. Further, a single purpose can be accomplished via dozens of different visualizations. Accordingly, it can be helpful to consider a reference book or website when determining which data visualization you are going to use. One website that breaks down data visualizations by function and identifies best practices for each data visualization is The Data Visualization Catalogue (https:// datavizcatalogue.com/). Tables Are Not Evil Tables are a simple way to communicate certain information. Tables are great at communicating specific data that is going to be sought especially when there are multiple categories of items related to a set of data that are related and that people want the specific data to reference. For example, let’s say you are discussing the top European countries on a number of factors. Having a table that lists these European countries and then has several columns of key data is something that may be valuable to communicate. Tables are particularly useful when used to complement and provide context to other data visualizations. Often, when people get into leveraging data visualizations, they seek to banish tables from all their data communication. It’s important to recognize that tables are a useful form of communication like any other that should be leveraged as needed.

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Harnessing the Power of Size, Angle, and Position Size

Size is something we can quickly comprehend. We see one thing next to another and can easily decode which is bigger and which is smaller. Using size in data visualization is an uncomplicated, digestible way to demonstrate the difference between objects. Still, it is important to keep in mind that the exact degree of difference is ambiguous without a clear frame of reference. For example, if you look at a bubble chart, you can quickly compare items in reference to one another, but you could not determine anything about the measure of those differences. It takes a bar chart to have a strong degree of confidence in communicating and quantifying exactly how different one item is to another. Angle

An angle is a powerful tool for showing trends or deviations from trends in a data visualization. For example, look at the line chart in Figure 8.6. The graph demonstrates an increase in sales over time. It’s simple and readable, and it clearly demonstrates the trend in the data.

FIGURE 8.6.  Power of angles: Line chart using context-appropriate use of angle and direction.

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Still, while angles are good at showing trends or deviations from trend, they are not able to illustrate exact sizes or amounts. Pie charts, for example, use angles to show the breakdown of different items in a group. As Figure 8.7 demonstrates, these slices can illustrate approximate differences between items; however, they do not give quantitative, measurable data concerning the size of each item. .

FIGURE 8.7 TOP 10 GROSSING MOVIES IN 2018.  Which movie was the highest grossing? [BoxOfficeMojo_1]

Position

Position is another element that people automatically and instinctually perceive. Acknowledging and taking advantage of people’s processing tendencies can make for a more effective diagram. For example, people tend to absorb information following a Z-pattern, beginning at the upper left corner and ending at the bottom left. As a result, it can be beneficial to have particularly important or eye-catching information positioned in the upper left corner. Close attention to positionality can also help make your visualization more digestible. Improvements should be indicated with an upward-sloping line; this helps viewers view the change as positive. On the flipside, negative changes, even if they are technically increasing, should be positioned as a declining line. This will help indicate to the viewer that the change is negative. It can also be worthwhile to consider positionality along your graph’s axis. For example, we could line things up in alphabetical

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order, on an increasing basis, on a decreasing basis, or according to time. The position we use in ordering items should align with how we expect the user to interpret our visualization. If we are creating a bar chart representing the top 10 states by gross domestic product (GDP) and your goal is to demonstrate which states have the largest GDP, then ordering the states according to size is the best approach. On the other hand, if we had a bar chart of all 50 states and wanted users to be able to quickly identify their state, then having it laid out alphabetically by state would make the most sense. You could even consider using a heat map with a rank indicator for a clearer visual. The Power of Color Color is an incredibly impactful element in data visualization. Think of how many times you have been asked your favorite color? In fact, this might even be an information security question for some of your applications. Colors plays a key recognizer around sports teams, company logos, and more. Color is something that we as people have a strong emotional connection. But color is also a scientifically researched item. In fact, color theory itself is hundreds of years old. The simple diagram shown in Figure 8.8 is the first color wheel created by Isaac Newton in 1704.

FIGURE 8.8 ISAAC NEWTON’S COLOR WHEEL.  Newton’s original color wheel dates back to 1704 [Munsell_1].

Modern color wheels have evolved to be more detailed and functional. They are useful tools for identifying which colors complement each other and can help you decide between gradations of the same color.

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Let’s demonstrate the value of color for data visualization by having you count how many eights are present in Figure 8.9. 5 1 4 5 8 4 5 1 0 9

8 7 8 0 4 7 3 7 6 7

2 1 5 2 1 1 2 1 5 4

7 9 8 7 8 3 7 9 8 9

3 5 7 3 2 1 3 5 7 8

4 6 0 4 6 0 4 6 0 6

7 3 6 7 9 3 7 8 6 3

1 3 4 1 3 4 1 9 4 0

9 2 5 9 2 5 9 2 8 2

8 6 4 8 9 4 0 6 4 7

FIGURE 8.9.  10 x 10 Table. How many eights are present?

Looking for the eights in that diagram is a challenging and tedious exercise. Now, look at Figure 8.10 and pick out how many eights are present. 5 1 4 5 8 4 5 1 0 9

8 7 8 0 4 7 3 7 6 7

2 1 5 2 1 1 2 1 5 4

7 9 8 7 8 3 7 9 8 9

3 5 7 3 2 1 3 5 7 8

4 6 0 4 6 0 4 6 0 6

7 3 6 7 9 3 7 8 6 3

1 3 4 1 3 4 1 9 4 0

9 2 5 9 2 5 9 2 8 2

8 6 4 8 9 4 0 6 4 7

FIGURE 8.10.  10 x 10 table with eights in red: Now how many eights?

Color is extremely useful here in helping us identify the 11 eights in this data visualization. As this example demonstrates, color can be instrumental in depicting data effectively in visualizations. As such, picking color carefully in a way that is intuitive to your audience is important. Now we will dive into the different considerations and tradeoffs we need to make around picking the colors we use. Color Usage

When using color in your visualizations, be discerning. Color shouldn’t be used when it doesn’t convey meaning. For example, in

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Figure 8.11, the first pie chart is quite colorful; this does not make it a more effective visual. On the contrary, the color differentiation adds no value and just makes the visualization messy and challenging to comprehend. The second visualization demonstrates a clearer and more effective use of color.

FIGURE 8.11.  Example of when not to use color: First visual mistakenly uses different colors for each movie while the second visual correctly use a monochromatic visualization. [BoxOfficeMojo_1]

Context Correct Color

Colors do not exist in a vacuum; they often take on symbolic meaning and outside associations. In fact, certain colors can generate

134 • Data Storytelling and Translation

specific, instinctual responses from your audience. Certain colors have a natural context. In brand and digital marketing, there has been a great deal of research concerned with color psychology. The goal is to ascertain what the right color for a particular situation and audience. It is important to consider the context of your visualization. Think about how the color red might be understood by the following four audiences: first a group of financial analysts, second a group of physicians and nurses, third a group of designers from Target, and fourth a group of Chinese software engineers. Financial analysts typically use red for financial statements that are in the negative; they would have negative associations with the color. Most physicians and nurses associate red with the blood they see over the course of their job. It can be an overwhelming or complicated color for these practitioners, so it is best used sparsely if at all. The third group of Target designers likely have a strong positive affinity toward red due to their red bullseye logo. Finally, the fourth group of Chinese software engineers is used to cultural perception of red as a color symbolizing luck, joy, and happiness. Clearly, if you show these four different groups a visualization using red, then you will get four different responses. There may even be instances where certain colors should not be used. Some designers will omit colors in initial low fidelity designs to get an audience’s unbiased opinion. Consider your color choices carefully and leverage these associations wisely. As you work with color, be cognizant of your audience and their potential biases and try to minimize undue influence brought on by color bias. Color Consistency

It is also important to be consistent with your use of color. If you use blue to represent a medical device’s revenue in one month’s report, then use blue to represent the medical device’s revenue the next month as well. If you use orange to represent agriculture sales in one chart, use the same orange to indicate agriculture sales in the next chart. Different colors should represent different items. Even in well labeled data visualizations, changing color context can lead to misinterpreted information.

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Number of Different Colors

There is a limit to how many colors viewers can process in a meaningful way. Generally, you should use at most five to eight colors in each visualization. Brighter, more impactful colors should be used sparingly; muted colors can be used more liberally. Your audience may benefit from a fewer number of colors. Often, simply using one color for emphasis and allowing the rest of the visualization to be grey or black can make the visual more clear, concise, and effective. Intensity of Color

The bolder a color is the more a viewer is drawn to it. Using a bold color can allow you to emphasize a certain concept or idea. Still, having too many bold colors can muddle your visualization’s message. Make sure to use bold colors strategically to direct your viewer’s attention. Categorical versus Continuous Color

There are two different types of data: categorical data and continuous data. Categorical data is composed of variables within discrete groups or categories. For example, a car’s brand (e.g., Ford, BMW, Tesla) is a categorical variable. Continuous data is data that can exist over a range of measurable values. Temperature (e.g., 25.2 degrees, 38.9 degrees) is an example of a continuous variable. As demonstrated in Figure 8.12, categorical data is best presented with a color pallet where each distinct category has a distinct color. Continuous data, on the other hand, is best represented by a gradation color pallet where there is a scale of different shades of color to represent continuous data. An example of a 1-color and a 2-color scale is shown in Figure 8.12. Continuous Data

Categorical Data FIGURE 8.12.  Continuous versus categorical color pallet: Shown under continuous data is a 1-color scale and 2-color scale respectively. Shown under categorical data is a 5-category color pattern.

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A good opportunity to get exposed to but also identify some good continuous and categorical color pallets is by going to a paint store. When you get there, you will be exposed to 100s of paint swatches, some with gradations of the same color and some with complementary colors. These paint swatches are generally free to take, so go and grab some paint swatches for yourself. Consider what color combinations would be most useful for your visualizations and get inspired. Colorblind-Friendly

Roughly 8% of men and less than 1% of women in the population is colorblind. [Wikipedia_4]. As a result, it is worthwhile to ensure your visualizations are colorblind-friendly. There are multiple ways to approach being colorblind-friendly. It can be as simple as using color pallets that are designed to be effective for the visually impaired. Such pallets are easily accessible on sites like Coolors and Color Brewer [Coolors_1] [Color Brewer_1]. Further, most organizations have predesigned color pallets that are colorblind friendly in advance. If you are unsure whether a certain color pallet is colorblind-friendly, it is possible to upload your pallet to a check like Coblis–Color Blindness Simulator [Coblis_1]. Many data visualization tools and programs also have built in colorblind checks. It is also possible to make unruly color pallets more accessible by integrating a pattern into the color. For example, you might combine a gradient pattern or crossing lines over a color.

TEXT IN A DATA VISUALIZATION Text also plays a vital role in data visualization. Titles, legends, axis labels, data labels, and annotations all work together to assign a visualization meaning. The context for data visualization is also typically provided within the surrounding text. Before we begin, it is important to identify some of the most frequent mistakes beginners make when using text within their

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visualizations, including: (1) making text too small, (2) using a font that is difficult to decipher, (3) angling the text incorrectly, and (4) cutting off the text. Each time you create a data visualization, consider these mistakes and double-check your work. Avoiding these slip-ups will help your visualization appear polished and professional. Now, let’s discuss how to best utilize text within data visualizations. Summaries

Data visualizations are valuable tools; however, they can’t stand on their own. They need to be grounded in context. Text is a great way to provide this context. Abstracts and summaries are often necessary to center your audience. These summaries may be as short as a few sentences or as long as a full-blown report. The important thing is to ensure that your audience knows the salient information necessary to interpret and understand your visualization. Titles

Nearly every data visualization should have a title. More importantly, that title should include the takeaway for the viewer. All too often titles simply list the components that make up the axis and provide nothing else. The first chart in Figure 8.13 is an example of this phenomenon. It is simply titled “Sales in $ Millions versus Sales Year.” This accurately states the things mapped in this data visualization, but it doesn’t provide anything else to the data visualization’s consumer. The second line chart has a much more effective title: “Steady 5.5% average yearly sales increase despite the 2020 pandemic hiccup.”

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FIGURE 8.13.  Descriptive component versus takeaway title example: Top visualization displays a descriptive component title example and bottom visualization displays the preferred takeaway title example.

This simple title change makes the graph significantly more engaging. The viewer now understands the context and purpose of this graph. This title tells a story: despite a pandemic, this company experienced a 5.5% growth in yearly sales. Now, the graph means something, and your audience can get invested in that meaning. Make sure to be deliberate when titling your graph in order to hook your reader. Legends

Generally, you should avoid using legends. Legends require the viewer to go back and forth between the legend and the visualization itself. This back-and-forth movement is distracting, error prone, and often completely unnecessary. Pie charts, bar charts, line charts, and scatter plots rarely require legends. Using axis and data labels properly is much more efficient and seamless. There are some visualizations that will require legends, such as heat maps and bubble charts. In cases where a legend is necessary, carefully consider placement. Make sure the legend is in an area that fits intuitively for a visualization. Typically, having the legend spread out at the bottom of the data visualization is best. Organize the items in the legend in a logical way—alphabetically, smallest-tolargest, and so on—depending on the data visualization’s audience and purpose.

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Axis Labels

Axis should always be labeled. In most instances, you should both title the axis with a general descriptor of what is being measured and label the individual data components involved. Sometimes, the overarching descriptor of the data is unneeded. For example, if you plot sales revenue on the y-axis against year on the x-axis, then the x-axis doesn’t necessarily need the overarching descriptor (i.e., “Year”) when it has the individual data components of year identified (i.e., 2018, 2019, 2020, 2021, 2022). Make a judgment call. Your visualization should be both clear and concise. Always angle the axis label at a 0º or 90º angle so that it fits the axis. When text is angled oddly, it becomes increasingly harder to read. This takes the focus away from the data visualization itself and muddles your graph. Data Labels

Data labels are labels placed at specific data points on a data visualization. They can be used strategically to emphasize certain items. For example, in a bar chart, you may want to include the specific values of the largest or smallest item. In certain data visualizations, such as pie and bubble charts, it is helpful to label all the data points. Just like with any text element, make sure your audience can read your data labels with ease. Make sure that your font type and size is visibly decipherable. Annotations

An annotation is a short segment of text added to a visualization. Annotations provide an opportunity to ensure relevant context is not missed. For example, it might be worthwhile to explain unexpected trends in your graph. Perhaps there was a significant and unexpected reduction in sales in a given quarter; an annotation allows you to explain this irregularity. Annotations are also useful for explaining procedural changes. If you changed the way data was collected at a certain point, for example, you can make a reference to that point in time on your graph. Don’t be afraid to use annotations to include concise and relevant information for your viewer.

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Consistency One of the most important aspects of data visualization is consistency. Consistently use charts in the same way. Consistently use color the same way. Consistently communicate with text in the same way. If you are not consistent in your data visualizations, then you will end up inadvertently distracting or even misleading your audience. There is no need to change your format to add variety or generate intrigue; each visualization should leverage new data or provide additional insights to keep your audience engaged. Don’t feel pressure to change things solely for the sake of variety. If you update or change your data visualizations in a way that breaks consistency, notify your user both in the data visualization and in any accompanying communications. Format When you are designing a data visualization, it is important to consider the way that the visualization is meant to be consumed. Is it going to be displayed via mobile phone, a 60” television in a boardroom, or a 20” monitor? Will it be printed off in black and white or will it be sent out via email message? Often, visualizations will need to be displayed in multiple formats. Your data visualization’s design should work in tandem with the format, not against it. Sometimes this means creating multiple visualizations. The ideal visualization for a large monitor will not work for a mobile phone. Visualizations that are engaging in color might be entirely illegible in black and white. Think about how your data visualization would operate under different conditions. A printed visualization test, where you print your visualization on a black-and-white printer, can be useful for understanding what your audience will see, and an awareness of your audience’s point of view can often be the key to creating an effective and meaningful visualization. Source All too often visualizations omit the all-important data source. A lack of clarity concerning your data’s utility and validity builds distrust in your audience. It leads to disagreements, confusion, and a lot of wasted time. Accordingly, your best bet is to include the data source in all visuals.

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Trends and References Almost all data visualizations are meant to demonstrate trends and patterns. In fact, your audience will instinctually try to apply a trend line to your graph. Putting a trend line in yourself can save your audience the distraction. By directing their focus, you will eliminate potential errors and provide valuable insight about the data. Trends are incredibly useful for helping your audience gain context about the data’s meaning and importance. Consider putting in trend lines based on both historical data and predicted trends. Using reference lines to represent data variance or error bands can also provide users with a deeper context of the data. Observe how the error bands in Figure 8.14 help to inform the viewer.

FIGURE 8.14.  Example data visualization with error bands: Sample data visualization with error bands added.

Reference lines or columns are also useful for providing averages, past year sales, or other meaningful metrics in your visualization. These references help the user compare current data to past or expected data, which can be important for drawing conclusions. For example, in Figure 8.15, the first chart shows monthly sales volume alone. The second chart has also added important reference columns showing the target goal. These additional reference columns provide a more complete story. In fact, including the actual and the target together in this way is often more compelling visually.

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FIGURE 8.15.  Reference columns example: Example data visualization on top has no reference columns while same data visualization is on the bottom with target reference columns added.

Don’t Overdo It There is a tendency to try to condense too much information into a single visualization. Think back to Figure 8.5 and Napoleon’s Russian campaign of 1812. It is a great presentation piece and a lovely example of just how much you can communicate through a single image. However, if you give that graph to someone without providing context, they will likely be lost and lose patience. When it comes to data visualization, simpler is better. Make it easy for your user to comprehend what you are communicating. The default rule of thumb is to design each data visualization to

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communicate one primary objective. Use additional visualizations to communicate other pieces of information. This means generally avoiding three-dimensional visualizations or multi-Y-axis visualizations unless the audience is familiar with these types of data visualizations. Just as importantly, consider whether these more complicated data visualizations need this extra complexity. Sometimes additional complexity is warranted to show data correlation of multiple data sets. For example, in a scenario where you are interested in displaying how revenue growth may correlate with customer satisfaction, you might create a multi-Yaxis visualizations where one axis displays revenue growth and the other axis displays customer satisfaction. In this case a visualization consumer wants both the revenue growth and customer satisfaction data plotted together to explore the level of correlation. It is also important to avoid overdoing it visually. Consider whether your visualization contains too many elements. Your visualization should align with your audience and their preferences. Some audiences are going to prefer a more minimalist approach. Other audiences are going to prefer more color and flare. Understand your audience and try to put together a visualization that aligns with them. At the same time, don’t overwhelm the audience with visually flashy elements and information repetitiveness. Be informative but concise. Gestalt Principles Gestalt principles represent the basic forms of perception. They describe the natural and instinctive ways that people interpret information and are thus are highly relevant to data translators. We will focus on six central Gestalt principles and how they apply to data visualizations. These six principles are depicted in Figure 8.16.

FIGURE 8.16.  Representation of Gestalt’s design principles of proximity, common fate, closure, similarity, symmetry, and continuity.

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1. Proximity: When data is grouped together, people viewing that data perceive the data to be related. Accordingly, when you have a grouping of items close together and they should not be clustered together, you should call this out explicitly within the diagram to overcome the Gestalt principle of proximity. 2. Common Fate: When data is grouped as if trending in the same direction, people viewing that data perceive the data as related. This is why when we see two lines on a line chart, we start identifying they must be correlated. 3. Similarity: When data is depicted as having a similar color, shape, or shading, people viewing that data perceive the data to be related or of the same type. For example, if you have data from different genders and there is an importance in differentiating the genders in the data then using a different color, shape, or shading for the different genders would be valuable. 4. Closure: When there are gaps within the visual elements presented in a data visualization, people viewing that data tend to look for a recognizable pattern to fill in the blanks. In other words, people create closure. They don’t see the gaps as an absence; they mentally fill in the blanks to create a whole image. 5. Symmetry: When data is grouped symmetrically to form a center point, people viewing that data perceive the data as being related. 6. Continuity: When data is displayed, the human eye will follow the smoothest path of least resistance. This means that objects arranged on a line or curve are perceived as being interrelated and connected. The Gestalt principles of design do not tell you what visualization to use. They just help you better understand your audience’s potential perception of data. Understanding these principles will help you create a more precise and impactful visualization.

MOVING BEYOND DESIGN AND COMMUNICATING DATA VISUALIZATIONS Being capable of creating and leveraging effective data visualizations is an essential part of being a great data translator. In

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this next section, we move beyond creating data visualizations onto the next step: leveraging them effectively in order to communicate in a trustworthy and impactful manner. Prioritize the Meaning When creating your data visualization, your goal should always be to provide your audience with the information they need to make a decision. This doesn’t mean forcing your audience toward a specific conclusion. On the contrary, your data visualization should be an important aid toward conversation. It should make the meaning of the data clear and transparent so that an audience member has the tools to make an informed choice. Their decision might be that further data analysis or data procurement is required for exploratory data analysis. It might be that they chose to open or close a retail store. It might be any number of objectives. Regardless of the specifics, when creating a data visualization, your goal should be to give your audience the tools to make informed decisions and act. Ask Questions to Engage It is also important to engage with your audience when implementing your data visualization. This is often best done by asking questions. Asking questions allows you to check that your audience fully understands your visualization. More importantly, it also forces them to apply visualization to the problem at hand. For example, let’s consider a scenario. Hypothetically, let’s say there have been challenges with attrition. To address this problem, you leverage data from the quarterly employee survey. Your company uses an employee net promoter score (eNPS) and you create visualizations of the overall eNPS score and of the eNPS scores’ distribution. Next, you create a list of the top 10 reasons why people have given high scores and the top 10 reasons why people have given low scores. This information is being shared with all managers and is meaningful on its own, but you want your audience to engage with these visualizations. What questions should accompany these visualizations? Two potential questions could be: QQ

What 2 to 3 items listed do you struggle with and how could you improve and apply these items in the next 30 days?

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QQ

What 2 to 3 items listed do you think you do best and how could you do even better in the next 30 days?

  Section Challenge Activity ACT

Take a data visualization that you created that didn’t necessarily have accompanying questions. Draft three potential questions that you could have added to this visualization to help with audience engagement. Get Second and Third Opinions As we discussed right from the start of this book, empathy is one of the three most important traits for a great data translator. Exercising empathy involves understanding how your audience will interpret and respond to your visualization. However, it is always helpful to get a second or third opinion on your visualizations before revealing them to an important audience where you want to make a good first impression. A second or third opinion is especially valuable when coming from someone that has data visualization capabilities and functional area capabilities similar or identical to your audience. You might also consider consulting individuals of a particular age or demographic to gain some perspective. For example, you might want to get the opinion of someone with poor or limited eyesight before doing a presentation. As you make your visualizations, broaden your perspective. Avoid Check-the-Box Visualizations A well-designed, visually pleasing data visualization is not automatically a necessary or valuable data visualization. Repeatedly, this text has stressed the importance of understanding your audience and your purpose when creating data stories and visualizations. Avoid creating data visualizations simply to fill space in a report. These check-the-box visualizations—these visualizations created solely out of a need to have a visualization rather than to illustrate a point— are not constructive or useful even if they might be aesthetically or structurally sound.

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Still, there are plenty of examples of check-the-box visualizations in our everyday life. For example, you might be familiar with Audible, a service through Amazon that provides audiobooks through its mobile and web applications. For several years, the data visualization in Figure 8.17 was present in Audible app.

FIGURE 8.17.  “Your Audible Titles” visualization: “Your Audible Titles” in-app data visualization circa 2019 screenshot with the number of Audible titles on the y-axis and the year on the x-axis.

What is the purpose of this visualization? Audible’s goal is to motivate you to buy more of their product. Does Figure 8.17 make you want to buy more books? No. On the contrary, if you have a large number of books already, seeing the number might make you less likely to splurge on more. This data visualization does not compare

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you to other users to motivate you to read more; it does not separate out your reading time by genre to help you determine the type of book you might like to buy. It does not serve any real purpose at all other than existing as a visualization. Probably due to its lack of utility, Audible ultimately removed this data feature. Understanding a data visualization’s purpose from the beginning will help you to avoid a similar mistake. Layer Your Visualization Layering your visualization involves unveiling your data in increments. This might mean adding elements to your visualization over time as you explain your data story, or it might involve using more sophisticated software to animate your visualization. During a presentation, it can be especially powerful and engaging to draw your visualization on a whiteboard or digital tablet for your audience. Regardless of the way you go about this process, the key is to evolve your visualization in sync with your story. This layering process allows your data visualization to become even more deeply embedded into the storytelling process, which in turn makes for a more engaging and memorable visualization. In certain circumstances, layering can be very worthwhile. Let’s say you are analyzing the success of different product types. You created a scatter plot that on the X-axis has per-unit profitability and on the Y-axis has the number of units sold. There is a great variety of products, so when the product items are plotted, the graph is incredibly dense. There is a significant amount of information to digest all at once. This is a perfect opportunity to layer your scatter plot and digest your visualization in segments. You might explain one axis before layering the other on top. You might add products one by one to discuss them individually or you might add them in groups. You might even discuss what the most preferred location (i.e., upper right) in the visualization is and compare it to the least preferred (i.e., lower left). If you are having trouble aligning your story with your visual, try creating a storyboard. Sketch each piece sequentially as you tell the story aloud. See what pieces fall where naturally. Most of all, make sure that the layering process complements your story and enhances

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your ability to communicate your purpose to your audience. Creating effects for the sake effects is not only time consuming and ineffective, but also annoying for your audience.

  Section Challenge Activity ACT

Take a data visualization that you think could have benefited from layering. Start with your storyboard and sketch out the different layers you envision. Consider whether your storyboard complements your story. Are there places where layering is distracting? Are there places where layering is effective? What should you do differently? Show Your Work and Get Detailed Some people will hear the key details and see the data visualization and that will be enough. Other people are more detail oriented and will want to dive deeper. Whether you are presenting your visualization or communicating it via report or email, plan for the inevitable follow ups. Make sure you have access to the data used to create your visualization. Record the way that you created your visualization. Keep a backup of your visualization on hand. All these precautions will help smooth future potential interactions. In addition, these steps will help you when creating future visualizations and when revisiting your own work. Build Trust Through Data Visualization The saying goes “lies, damn lies, and statistics” meaning that statistics often are used to bolster weak arguments. “Statistics” in the preceding quote could be replaced by “data visualization” and it would hold equally true. Data visualizations are often used to bolster week arguments unfortunately. The use of data visualizations provides an average reader with a sense of truthfulness and analytical thinking. Sometimes these weak arguments become more obvious when you see unethical data visualizations leveraged. Unethically displayed data visualizations have this same influence. Data translators and storytellers have a responsibility to portray their data accurately and transparently. Your audience should be able to trust that your visualizations reveal trends and do not create them artificially. In

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order to avoid these manipulations, it is best to understand them. Some common examples of unethical data visualization include: Axis Gymnastics: Axis gymnastics occurs when someone manipulates a chart’s axis to influence their audience. In a column chart, this might involve cutting off the axis and starting at nonzero to make a variable appear smaller or larger than it actually is. A similar effect occurs when a data translator changes the scale on the axis at different points. For example, a Hollywood reporter might talk about how Black Panther blew away the competition in 2018 and use the visualization in Figure 8.18 to bolster their point.

FIGURE 8.18.  Axis gymnastics example: Highest two revenue generating movies of 2018.

At first glance, this data visualization is very successful in arguing the reporter’s point. You might start to believe the reporter’s claim. Upon closer examination of the y-axis, however, you might realize that the difference in box office revenues is not as large as it appears. In reality, there is only a 3.1% difference in Black Panther’s revenue when compared to Avengers: Infinity War. With more research, you might also realize that this chart only encapsulates United States box office sales. When you take global box office sales into account, Avengers: Infinity War’s $2.05B dwarfs Black Panther‘s $1.35B [Statista_1]. Both these movies were fantastic and massively successful, but in this example, the storyteller used data visualization and axis gymnastics to manipulate the viewers’ perceptions in order to push a false narrative. Axis gymnastics is something people from all walks of life— journalists, politicians, financial engineers, and so on—will look to

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leverage. Don’t get suckered by others using axis gymnastics. More importantly, don’t play the axis gymnastics game yourself. Ultimately, manipulating people’s perceptions to twist a narrative will only lead you to be distrusted and disliked. Data Shuffle: Data shuffling occurs when people display data in a manner that doesn’t align with their message. People might manipulate color, position, or other orientation to confuse the viewer and obscure the message. Data Heap: Some data translators overmanipulate the data to such a degree that it is made incomprehensible and irrelevant. They may also overwhelm the viewer with confusing visualizations and unnecessary information in order to bury the relevant data, or they may bury unfavorable key insights in the footnotes. All this is done to discourage the viewer from examining the data more closely and uncovering the truth. Make sure that you are transparent and concise about your data and findings. Axis gymnastics, data gymnastics, and data heaps are just a few techniques that people will use to manipulate the data. Be on the lookout for these and other cases in which data visualization is leveraged to manipulate and obscure the facts. When you encounter misleading or manipulative visualizations, start by explaining your confusion and suggesting they correct their error in private. You might even be able to provide some recommendations to improve the visualization. Most importantly, you shouldn’t presume bad intent. See if they modify and adapt based on input and how much they mitigate these mistakes happening down the line. Ultimately, you might not be able to control others’ behavior. You can control yourself. In reading this section, you have gained a strong, foundational understanding of how to create and leverage data visualizations. Still, there is much more to learn in this space. There are numerous experts on and companies for data visualization. There are books and websites devoted to understanding and perfecting data visualization. There are even international events where data visualization plays a central role. Explore multiple sources and grow as an ethical and reliable data storyteller and a translator!

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REFERENCES 1. [BoxOfficeMojo_1] “Domestic Box Office For 2018,” Box Office Mojo, https://www.boxofficemojo.com/year/2018/ 2. [Coblis_1] Coblis – Color Blindness Simulator, https://www.color-blindness.com/coblis-color-blindness-simulator/ 3. [Color Brewer_1] Color Brewer 2.0. https://colorbrewer2.org/ 4. [Coolors_1] Coolors. https://coolors.co/ 5. [Haidt_1] Haidt, Jonathan, The Happiness Hypothesis: Putting Ancient Wisdom to the Test of Modern Science, Arrow, 2007. 6. [Kahneman_1] Kahneman, Daniel, Thinking Fast and Slow, Farrar, Straus and Giroux, 2013. [Wikipedia_1] “1854 Broad Street cholera outbreak,” Wikipedia, last modified June 23, 2023, https:// en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak 7. [Wikipedia_2] “Anscombe’s quartet,” Wikipedia, last modified July 11, 2023, https://en.wikipedia.org/wiki/Anscombe%27s_ quartet 8. [Wikipedia_3] “Minard’s Map of French casualties,” Wikipedia, https://en.wikipedia.org/wiki/French_invasion_of_Russia#/media/File:Minard.png 9. [Wikipedia_4] “Color blindness,” Wikipedia, last modified July 10, 2023, https://en.wikipedia.org/wiki/Color_blindness

CHAPTER

9

Leveraging Dashboards in Your Communication

P

rocessing the massive density of raw data and information generated in this modern age can be overwhelming. Even as a data storyteller and translator, it can be easy to get lost in an ocean of data and analysis. The key to managing this information surplus is to find a guiding light. You need something to help you establish your priorities and gauge your data’s meaning. In essence, you need something that can act as a map to lead you towards your objective. The solution is to turn to stories. Stories can be your map. They can help you to establish a relatable, memorable narrative for your data; they can help give your data meaning. As this book has stressed time and time again, stories are key to data interpretation and visualization. In Chapter 8, we talked about how data visualizations are helpful tools for data storytelling and translating. In this chapter, we will discuss another popular tool for communicating information: data dashboards. According to Merriam-Webster, dashboards are a “graphical report of various data relevant to a particular business, group, etc.” [MerriamWebster_1]. Dashboards are composed of multiple, interrelated visualizations grouped together according to their audience and purpose. Dashboards have become an increasingly common tool for communication not just in the workplace but also in everyday life, particularly in fitness and gaming apps. Software companies and consulting firms have emerged with the express purpose of helping manage and organize your data into a dashboard format. Although dashboards are not a panacea, a good dashboard in the proper setting is a meaningful and effective way to relay information.

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To begin, let’s contemplate a simple dashboard you might see daily: your vehicle’s dashboard. An example of a car dashboard is included in Figure 9.1 for easy reference.

FIGURE 9.1.  Example car dashboard photograph. Photo by Nick Fewings on Unsplash.

If you have driven a car before, you should be familiar with the format and concept of the dashboard shown, even if you have not driven that particular model or brand of car. In vehicles, the dashboard helps to quickly and cleanly communicate necessary information about your car’s performance and health so that you can drive safely and soundly. Professional data dashboards should operate similarly to the ones in your car. They should be easily digestible and rich in vital information. The fundamentals you have learned in previous chapters concerning the design, development, and communication of data visualizations are relevant for data dashboards as well. Keep the knowledge you have gained throughout this book in mind as we dive more deeply into the function of data dashboards. Three Types of Dashboards Dashboards come in many shapes and sizes. The three main types of data dashboards are: (1) strategic dashboards, (2) operational dashboards, and (3) analytical dashboards. 1. Strategic: Strategic dashboards are created to ensure that key goals and objectives are progressing as scheduled. It might be helpful to think of strategic dashboards as long-term health

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checks of an organization. These dashboards typically include information concerning key performance indicators (KPIs) or other important objectives for an organization or individual. Strategic dashboards are typically monitored less frequently than operational dashboards and are updated on a less frequent weekly, monthly, or even quarterly schedule. Strategic dashboards are most often presented directly to executives and executive-adjacent individuals. 2. Operational: Operational dashboards record the real-time status of your objectives. These dashboards are a way to monitor the current, day-to-day health of your project and ensure that the project is going well on a much more minute level. Ultimately, operational dashboards are there to help people act and react as needed. Accordingly, data in most operational dashboards is updated frequently, often in real-time. Operational dashboards tend to be utilized by frontline personnel and especially front and middle-level management. When creating operational dashboards, it is important to include relevant metrics and targets that align with overall strategic objectives. You might also include historical information to provide a reference point. 3. Analytical: Analytical dashboards are designed for deeper exploration of information. When creating an operational dashboard, the focus is to react and act; when creating an analytical dashboard, the goal is to question, think on, understand, and adjust the data. Analytical dashboard data may be real-time, but it can also be updated less frequently depending on your objective and needs. Although analytical dashboards tend to be utilized by analyst-types most frequently, managers and executives might also find it worthwhile to leverage these types of dashboards. Thought Experiment: Determine a real-life example for each of the different types of dashboards. Once you have three dashboards identified, ascertain the dashboard’s audience and its purpose.

DASHBOARD BEST PRACTICES Although data dashboards can take a number of forms and have a multitude of purposes, there are still certain fundamental

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best practices that can help your own dashboard be successful and effective. As you consider the following guidelines, remember that your audience and purpose should help to guide you as you consider how to present your data. Provide the What, Why, and Now What Ideally, dashboards are meant to explain to the viewer what happened, why it happened, and what is meant to happen moving forward. Making sure to account for these three talking points allows your audience to fully understand the problem and what must be done to solve it. For example, consider a scenario where same-store sales in your California stores were up by 3.2% year-over-year. This is a nice statistic, but it doesn’t leave the reader with anything to consider. Now, read that same sentence with the reason attached: same-store sales in California were up by 3.2% year-over-year because of increased marketing efforts tied with positive economic tailwinds. This sentence is vastly improved; it includes information that allows the reader to put the statistics into context and draw conclusions. Finally, take the scenario one step further and include the next steps. In this example, you might discuss how you should distribute future marketing resources and budget considering the data you’ve collected. Figure 9.2 demonstrates one effective way of formatting your dashboard according to incorporating each of the three components.

What?

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Why?

Now what? FIGURE 9.2.  Three components of a dashboard: Sample representation of each of the three types of dashboards, namely what happened, why it happened, and what you are meant to do now.

Regardless of whether you are creating one dashboard or a series of dashboards, remember to keep your message focused and purposeful by incorporating necessary information about what happened, about why it happened, and what that means for the future.

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Be Consistent Consistent communication allows you to deliver a clearer message to your audience. Especially when you encounter audiences that are inexperienced with data, make sure that your dashboards demonstrate consistency. This means ensuring that the layout, visual elements, color, font, and size are all relatively uniform within your data dashboard and even across different dashboards. Developing a general dashboard layout for each of the three dashboard types and creating a universal color palette are useful initial steps towards developing a consistent style. In Figure 9.3, there is a hypothetical example of a dashboard template that an organization might use to structure its findings.

FIGURE 9.3.  Example dashboard template: Example of a dashboard template that might be used over and over providing the user’s consistency of use.

Implementing a common template within your organization can help communicators deliver information more quickly and easily to their audience. It is akin to developing a common language between yourself and your viewer. With practice, your audience may interpret your dashboards faster. Still, it is important to mention that consistency should not be an excuse for stagnancy. If there are opportunities to enhance dashboards in an organization, then change is beneficial. Be cognizant of opportunities to change and then commit. Ultimately, your goal is to deliver information in a way that is easily digestible to your audience.

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Follow the Z-Pattern In cultures that read left to right and top to bottom, people instinctually absorb information in Z-pattern, as demonstrated in Figure 9.4. As a result, the most important information in your dashboard should be placed in the upper-left corner. Then, your narrative should progress across the dashboard to the right, down, and repeat. Following the Z-Pattern allows you to leverage instinctive human processing behaviors to better communicate to your audience.

FIGURE 9.4.  Follow the Z-pattern: The most important information should appear in the upper-left corner progressing down to the bottom-right corner.

Balance Interactivity Dashboards are unique amongst data communication tools in that they give users the ability to directly interact with and filter through your data and analysis. Building easy-to-use interactivity based on your audience’s needs, aptitude, and training is key to creating an effective dashboard. There are a multitude of ways users can interact with dashboards, including: drop-down selection filters, hover-over tooltips, buttons, selectors, and inter-dashboard interactions. Keep in mind that just because you can build in another filter or add more functions to a tooltip, it does not mean you should. Including all these different features will only muddle your dashboard and confuse and overwhelm your user. Picture how the points of interactivity within Figure 9.5, highlighted in orange,

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interact and overlap with one another, and consider ways to minimize confusion and maximize clarity.

FIGURE 9.5.  Build in sensible interactivity: Building in sensible interactivity that a user is aware about, understands how to utilize, and is valuable is key.

Make sure to incorporate interactivity with a specific intention in mind. Consider what will be most helpful to your audience and work from there. Don’t Shy Away From Text Text tends to be significantly underutilized both in dashboards and in data visualizations in general. After all, dashboards are not just a series of visuals. A good title can be what grabs your user’s attention. Proper labeling and descriptions can help provide your visuals with context and meaning. A good takeaway section can move your audience to action. Especially in high-impact dashboards, focus in on making your takeaway section concise and action-driven. Text can also be used to reference data sources, summarize the overall problem, and to emphasize the purpose of the dashboard. In short, make sure to use text to your advantage when creating your data dashboard. Make Sure the Data Source is Obvious One way to minimize potential misunderstandings and confusion is by clearly identifying your data sources. Including the source of

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the data allows your audience to understand the context of the data and limits unproductive debate. In a similar vein, letting the user know significant data preparation steps can also be important. For example, it might be worthwhile to include information about outliers or missing data. Unspoken data cleanup can result in confusion, or even worse, broken trust. As always, being transparent about data sources and preparation is the safest option. Trust is your biggest asset as a data storyteller and translator. Defaults Matter People instinctually tend to revert to that which is familiar. In fact, this effect is so pervasive that it is also referred to as the default effect. People tend to have a particular way that they are used to interpreting information, and they don’t usually move beyond this default. A classic example of the default effect concerns retirement savings. A study showed that, if the default is to save for retirement, then individuals are likely to devote a considerable sum of money to a retirement fund. In fact, people that defaulted on saving money for retirement tended to have 88% more savings than those who did not [Madrian_1]. Alternatively, if the default is not to save for retirement, then individuals are unlikely to save. Like a lot of cognitive biases, the default effect comes into play in many forms as a data storyteller and translator. Looking to leverage the natural ways that your audience’s brains will respond to things is not only effective but also intuitive for your audience.

DASHBOARD LIFECYCLE Like metrics, dashboards have a lifecycle. Each dashboard has a beginning, a middle, and an end. To utilize your dashboards effectively, it is important to design your dashboard with a clear vision of its lifecycle and purpose in mind. Beginning In the beginning, your focus should be on creating a dashboard that is tailored to your audience and laser-focused on your purpose. Often, this means taking part in an iterative process, where you

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adjust your project according to rounds of user and test group feedback. As a data translator, you may represent the dashboard’s creator, the user group, or a neutral party in this process. Regardless of your specific role in the project, keep your goal at the forefront of your mind and work towards it. Be attentive to your users and empathically communicate with your intended audience, even after your project has been released. Begin your project fully committed and attentive to seeing it through. Middle Dashboards need to be maintained over time. This means continual maintenance to keep up with enhanced technology, changing styles, and new data. Generally, the dashboard’s designated owner will evaluate the dashboard’s ongoing needs and utility and maintain its functionality. End All good things come to an end. This includes the need for a dashboard. There will come a time when your dashboard is obsolete. Perhaps a new, improved dashboard has been developed, or perhaps your dashboard’s function is simply no longer necessary. It is important to recognize when your dashboard’s usefulness has ended. A dashboard’s owner must be willing and ready to discontinue a project when the time is right. Reviewing dashboard use data is the best way to understand your dashboard’s current utility. If this sort of data is unavailable or inaccessible, examine the amount of people using your dashboard. If your audience has significantly diminished since release, it might be time to retire your dashboard. Another method is to directly ask for user feedback. If your audience does not engage with your dashboard frequently, once again, it might be time to scrap the project. Ultimately, your audience’s reaction, or lack thereof, to your dashboard’s removal will say all you need to know in regard to the state of the project.

DASHBOARDS AND STORYTELLING This chapter we have spent a lot of time covering dashboards and best practices. Dashboards are an important form of communication in the modern workplace. As a data translator and storyteller, you

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might be called upon to create dashboards to deliver information to others in a digestible way. You may also need to interpret other people’s dashboards and relay this information, context, and meaning to your audience. This prompts an interesting question: are dashboards an example of data storytelling? In Chapter 7, we learned that a data story required a narrative, a story arc, and a hook. Using this definition, most dashboards are not in fact data stories. In fact, the difference between dashboards and data stories becomes even more blatant when you consider each item’s different function. Data stories exist to shift narratives and spur action. Most generic dashboards, even if well done, do not change minds or spur strong action without more context and storytelling. Dashboards are best used in conjunction with audio, images, text, or video. A presentation format would also allow you the time to expand upon and orient the information contained within a dashboard.

  Section Challenge Activity ACT

Identify an example where a dashboard did a good job at telling a data story. What did it use to do this? For example, an executive director of a nonprofit may record a video to be included as an element of your dashboard. The executive director might discuss the organization’s progress that year and highlight a couple of individuals who benefited from patrons’ donations. We are used to seeing such storytelling in glossy colored reports, but dashboards can provide this same luster on top of leveraging more interactive components. Still, even though most dashboards are merely tools for storytelling, they should still give the user worthwhile insight. Company-wide dashboards should reflect your corporation’s goals and priorities. A dashboard shared by a nonprofit with its donors should demonstrate the organization’s service, progress, and success. As a data storyteller and translator, you will need to be competent in leveraging and interpreting dashboards. Use the practices identified in this chapter as a foundation and keep finding new avenues to communicate.

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REFERENCES 1. [Madrian_1] Brigette Madrian, “Making It Easy: How Defaults and Design Can Improve Retirement Savings Outcomes,” Georgetown University McCourt School of Public Policy Center for Retirement Initiatives, https://cri.georgetown.edu/making-iteasy-how-defaults-and-design-can-improve-retirement-savingsoutcomes/ 2. [Meriam-Webster_1] “dashboard,” Merriam-Webster, September 4, 2022, https://www.merriam-webster.com/dictionary/dashboard

CHAPTER

10

Communicating Your Data Story

A

ll your hard work is fruitless unless you effectively communicate your data story to your audience. By this point, you have honed in on the problem, your audience, and your purpose. You have laid out your data story systematically via a Data Story Canvas, and you have produced your data visuals. The final step lies in execution; the way you go about communicating your data story is pivotal. Your data story might be communicated in a variety of ways. It might be used during a presentation. It could be used to nurture your audience’s interest before your lecture; it could be included in an email or posted on a website. As a data translator, you need to be well versed in communicating data stories through all these many avenues. You will need to alter your approach according to the venue, the audience, and the purpose. This chapter will cover a Data Story Checklist —a tool to follow to help you successfully and efficiently deliver your data story. After covering the Data Story Checklist, the chapter will dive into different strategies to develop and improve your ability to present and leverage your data visualizations.

AN INTRODUCTION TO THE DATA STORY CHECKLIST Like any good checklist, the Data Story Checklists can and should be reviewed regularly to ensure your data story is effective and compelling. For more complicated stories, your Data Story

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Checklist should be used in tandem with the Data Story Canvas you generated in Chapter 7, “Painting Your Data Story.” The Data Story Checklist is composed of nine distinct but equally important items. In the following sections, each element of the list will be broken down in more detail. Be Authentically You One of the most important parts of being a successful communicator is allowing yourself to be authentic. Especially for beginners, it can be tempting to try to become someone you are not—someone that you think your audience wants to see. It is important to empathize with and understand your audience, but this does not mean you should put on a performance. Rather, it means delivering your data story using a method that is authentic to and effective for you. Finding your authentic style involves self-reflection. Think of different times when you had a serious conversation with friends, family, or long-time colleagues, and consider what tactics you found most natural and effective for informing and persuading. Read emails or letters that you have written and try to identify what elements and tactics remain consistent throughout. This will help you determine what strategies are an instinctive fit for you and will give you a natural foundation to build up your style. Maybe you use a lot of hand gestures. Maybe you tend to weave in targeted jokes. Maybe you improvise or draw as you present. Consider your own habits, and list tics or strategies that you think represent your authentic communication style. Forming a cohesive style can also involve some experimentation. Research successful data story communicators, such as Malcolm Gladwell, and try emulating them. Test which strategies work for you and which ones don’t align with your style. Over time, your communication style can and should change and develop. With more experience, you will gain both a better understanding of yourself and more confidence. Both these things will help you become more comfortable and authentic. Your audience will see that confidence and be more inclined to believe you. Conversely, when your audience senses that you are uncertain or artificial, they will lose trust in what you are saying. Above all, be genuine in your

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communication. Everyone has a communication style. Embrace and leverage your own.

  Section Challenge Activity ACT

Being self-aware is an important starting point when communicating authentically. Take 10 minutes to describe yourself. In one column, create a list of words that describe your communication style. In the second column, list words that do not describe your communication style. Using a Johari-Window-like approach, ask others that are familiar with your communication style professionally to create a similar list of your strengths and weaknesses. Look at the similarities and differences between your list and theirs. Test and Verify Standup comedians have one of the most difficult jobs in the performance industry; they must get in front of an audience of diverse people whom they know nothing about, and somehow get their listeners to genuinely laugh. Think of Gabriel Iglesias, Jerry Seinfeld, Tina Fey, or Dave Chappelle. By the time they perform in front of thousands of people, they have tweaked and tested each word of their joke and each second of their delivery. They have written and rewritten each quip; they have tested the order of their set and the speed of their delivery in front of friends, other standups, and perhaps even small club audiences. All this hard work is put in for a couple seconds of laughter. Standup comedians are fearless in their approach; they test the efficiency of their jokes by directly putting them on display and seeing the response. Similarly, you should be unafraid of mistakes. When formulating how to communicate data stories, try emulating their approach. Getting genuine feedback from trusted representatives of your audience is one of the best ways to practice with and verify your strategies and methods. When testing your communication’s effectiveness, there are two valuable sources of feedback to consider. One important source of critique is your colleagues. Identify a couple of knowledgeable

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coworkers that you can trust to give you worthwhile, genuine advice, and let them look over your communications and offer advice. The second source of feedback can be more elusive; it is found within your audience itself. It can be invaluable to find a representative within your target audience to bounce ideas off. For example, you may be a business analyst that communicates regularly with the finance department. In this situation, it would be beneficial on multiple levels to build relationships with your finance colleagues and get their perspective on your work. In the beginning, this might be as simple as sending questionnaires or asking simple questions in conversation. By the end, you might be comfortable enough with one another to get their input on your data stories early on in their lifecycle. In general, especially when you are new to an organization or early in your career, you should focus on spending time establishing and nurturing diverse connections within different departments in this way. Most importantly, receive advice and feedback from others graciously and thankfully. Be thoughtful when asking clarifying questions. If you are combative or aggressive when receiving feedback, eventually people will stop giving you honest advice. In short, as you begin to develop and nurture your style of communication, remember the importance of outside feedback. Having a proxy audience to preview important communications prior to their delivery can be invaluable. Be Vulnerable As we discussed in Chapter 1, “The Age of the Data Translator,” one of the most important traits a data translator and storyteller can possess is trustworthiness. One way to build trust in your audience is, a bit paradoxically, to be vulnerable to them. Being vulnerable shows humility and gives your audience something human to relate to. It also helps your audience to feel that you reciprocate their trust and regard—people generally don’t share their vulnerabilities with those they dislike or doubt. Often, a good way of expressing vulnerability to an audience is by bringing up a past mistake that you learned from and rectified. For example, you might bring up an instance where you made a mistake in survey data collection and talk about how you managed

Communicating Your Data Story • 169

to mitigate the error. This story not only subtly shows vulnerability, but it also allows you to show adaptability and strength. It shows that you grow from your mistakes and gives your audience the chance to learn with you. It is important to be vulnerable strategically. Being vulnerable can be a double-edged sword, so understanding your audience and not being vulnerable in a way that may be used against you by detractors is important. Further, it is unwise to be too unreserved too fast; naturally ease into showing vulnerability after a relationship has had time to develop. Finally, keep in mind that vulnerability requires balance. If you show too much vulnerability, you risk coming across as incapable or unconfident. If you show too little vulnerability, you might come across as unapproachable and cold. In the end, find an equilibrium, and let yourself connect with your audience by showing vulnerability. Eliminate Roadblocks in Advance Despite your best intentions, you will ultimately encounter obstacles and setbacks in your work as a data storyteller and translator. The greater the scope of your project, the more likely you are to encounter problems and detractors. As discussed in Chapter 7, detractors are simply people that have a narrative that is not aligned with your own. They desire a different outcome than that which you are suggesting and are often entrenched in their position. When planning your data story, try to identify your detractors. Consider their motivations and try to uncover some common ground. This will make it easier to determine what it might take to persuade your detractors and will give you the ability to build a convincing counterargument. Your organization’s history can also become a roadblock to overcome. Previous missteps can result in unexpected hesitancy or resistance in your audience. Early in the planning process, take the time to research and understand your company’s previous endeavors. When you present your proposal, emphasize what makes your project different from past projects. Highlight the ways that your proposal is innovative. Framing your project as a step forward will make people less likely to dwell on the past.

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In short, address known roadblocks—whether they be in the form of detractors or organizational baggage—as early in the process as possible to minimize backlash against your project. Engage Often and Early Another way to mitigate obstacles is to engage with your audience throughout the process. As you send updates about the project, establish a two-way communication channel, and encourage your audience to give input and feedback. The frequency and style of this communication is going to vary based on the scenario at hand, and you should consciously think about the needed communication early on. Determining the form and frequency of communication might involve investigating company norms. It might also involve getting input from the people you are communicating with directly on what might be beneficial for them. One simple, low-touch way to keep people engaged is to send a periodic email or group message update. In addition to this written approach, it might also be effective to attach a short video of the written update to differentiate the update from a typical email communication. Allowing your audience to engage with your project not only helps you catch potential mistakes, but it also helps them get more invested. They will have had a hand in the project and thus will have developed some sense of ownership of it. Then, your data stories become follow-up conversations about something you both care about. Be Transparent and Ethical Being consistently transparent and ethical is crucial to building trust. Still, it can be challenging to communicate all the nuances of data collection, processing, and analysis to an audience in an engaging manner. Overly nuanced communication will generally not keep an audience’s attention, as discussed in Chapter 7. As a result, it is important to consider the tradeoffs involved in excluding or including certain pieces of information. An alternative could be to include footnotes or an appendix to give the viewer valuable context while not overwhelming the viewer or disrupting the flow. While you weigh the value of certain information, consider your audience and their cognitive biases and craft your data stories responsibly.

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Being persuasive in your storytelling does not mean obscuring data; rather, it means presenting the data and explaining how it supports your proposal. In the end, a data translator’s ultimate goal should be to inform an audience and help them make logical decisions. Unfortunately, many modern institutions frequently misuse data science for their own ends. More or Less, a podcast available on BBC Radio 4, investigates and presents the accuracy of numbers and statistics in the public domain [More or Less_1]. Using this and other resources will help you become more comfortable recognizing and avoiding improper data usage.

  Section Challenge Activity ACT

One way to become an even better communicator is to get in the habit of arguing why you are wrong. Genuinely think about all the reasons you might be incorrect. Take a recent project and write down ten reasons you might be wrong. Now take three of those reasons and develop counterarguments. After the exercise, consider whether your argument is phrased in a compelling manner. Doing this will ensure you know the gaps in your data story, and it will help you prepare for your audience and their objections. It might also be worthwhile to practice debating against an opposing side. Be Confident and Humble When delivering your data story, it is important to balance being confident with being humble. Appearing confident gives your viewers the impression that you are competent, which in turn can help you to gain their trust and respect. On the opposite end of the spectrum, appearing humble allows you to be approachable and likable, which enables you to build genuine, long-lasting relationships. Thus, leveraging both these traits is necessary for being an effective communicator. Still, it can be challenging. Certain individuals find it difficult to exude confidence, while others find it challenging to appear approachable. It is important to be authentic to yourself, but this is

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not an excuse to refuse to improve yourself. If you find it challenging to seem humble, try to show more vulnerability, as discussed previously in this chapter. To appear confident, consider how you carry yourself and the way you communicate information. 1. Posture: Sitting or standing upright can help evoke a sense of competence and reliability. Try not to slouch or fidget, especially during video and in-person presentations. This will help you to appear more confident and in control. 2. Eye Contact: Confident people don’t shy away from eye contact. Do not stare to the point of discomfort but do try to periodically look at your audience members in the eyes. Especially when you are responding to a person’s question, establish that connection and make eye contact. Not only does this help you engage with your audience, but it also helps you appear confident. 3. Volume: When presenting, make sure that you can be comfortably heard. Account for the setting and the ambient noise. Your voice needs to be heard. It is incredibly hard for your audience to engage with you when they must strain to hear your voice. In addition, speaking too softly also makes you come across as inexperienced. Make sure to be conscious of your volume during a presentation. 4. Movement: Using contained, natural hand gestures can be a good way to make your audience present and engaged. Pointless gestures—fidgeting, nervous pacing, overexaggerated hand movements, and so on—can have the opposite effect. Be aware of the way that you move during your presentation, and make a conscious effort to be deliberate with your motions. Much of appearing confident lies in how you hold yourself and the way you present your ideas. This holds true even in writing. Writing a concise opening and a compelling conclusion helps the content within the text itself hold weight. Be conscious of the way you hold yourself; let yourself be confident and humble as needed.

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  Section Challenge Activity ACT

One exercise that can be useful in building confidence involves roleplaying as different high-ranking individuals, such as CEOs, professional athletes, or even the president. Consider how you would communicate about a project as one of the most powerful figures in the world and write out a message. Then, perform the exercise again, but this time roleplay as a lower-ranking individual. Afterward, consider the differences between the two messages. How did you communicate when you had no inhibitions? How did you communicate when you were being humble? Consider ways to find an equilibrium between these two extremes. Be Prepared to Improvise The key to improvising is to be in the moment. It is about listening to your audience and responding. It is about adapting in the face of unexpected challenges. When crafting your data communication, it is important to consider its flexibility. You’ll want to create something that can adapt with you. For example, consider a scenario where you must present a 45-minute project update in 15 minutes due to unexpected time constraints. Rising to the challenge would mean knowing what is essential versus what can be removed. It would involve eliminating noncrucial points of distraction to streamline the presentation. It might even require sending out a written communication to provide your audience with a fuller picture. All these adjustments require an adaptable presenter and a customizable presentation. Let yourself and your work be flexible so that it can survive real-world obstacles. Another important skill to develop is the ability to read your audience. Some audiences might want to explore a topic in detail, and others might prefer a broad summary. Paying attention to your audience’s reactions and gauging their level of engagement can help you know when to go in depth and when to pull back. If you can tell you are losing your audience, change tracks. Improvise. Ask questions to get the audience involved and take care to build off their responses so that they understand their input is worthwhile.

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Most of all, as you create and communicate your data story, be flexible. Improvise and control the flow and pace of your data story. Lead With a Story Backed by Data and Visuals Stories are powerful tools. They can motivate your audience where raw numbers will not, and they can give your audience something human to get invested in. As a result, an important step in crafting a presentation is finding a story to use as a hook. Once your audience connects to this story, they will more easily engage with and be invested in the data that underpins it. This means it’s essential to identify a true story that is relevant to and resonant with your audience. Maybe you include a story about the challenges a customer faced using your product, or you describe how a competitor’s actions led to bad press. You might even consider intertwining several into your presentations or reports. Some of the most effective stories spotlight a relatable customer or employee and describe how your project will impact them in a positive way. Keep in mind that this example story is not the centerpiece of your presentation. Rather, it is meant to highlight the importance of your data, and it is a tool to aid you in relaying your findings and conclusions. For this reason, it is obviously important that you choose well-supported, factual stories when crafting your data story. Attempts to fool your audience with a fake story will only result in distrust. Instead, leverage compelling, genuine stories to keep your audience. An important part of the any data communication is relaying the data itself and this is why it is important to weave this into your flow still. Leveraging data visualizations are a way that we can bring data to an audience but not bore them. Data visualization done well is easy for people to understand and capture takeaways as we discussed in Chapter 7. Consider the Right Person Data translation and storytelling necessarily require collaboration. The best data translators are adept at teamwork. They thrive on engaging with others and are willing to share the spotlight. Therefore, when considering how best to communicate a particular data story, make sure to leverage your team effectively. Certain team members, for example, might garner a more positive

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response from specific audiences due to previously established relationships or shared backgrounds. Further, some team members might be better suited toward crafting written communications, while others excel doing presentations. When working with new teams or introducing new team members, it is worthwhile to have open conversations about their individual strengths, weaknesses, and passions. Understanding your team members’ backgrounds will also help you utilize their talents more effectively. On occasion, utilizing your team might mean allowing someone else to take the lead. It is important to remember that just because you can present something does not mean you are the best person for the job. Don’t be afraid to delegate. Using unselfish and unselfconscious discernment to choose the presenter will result in a more effective presentation. In addition, it will also help you build trust and respect within your team. Allowing multiple data translators to be let into the project allows your work to reflect a more rounded, diverse perspective. In particular, it can be helpful to incorporate the perspective of someone who formerly disagreed with your stance. They can provide useful information on what convinced them to change their stance and can be a powerful voice for convincing other contrarians within your audience.

DATA STORY CHECKLIST TT

Be authentically you

TT

Test and verify

TT

Be vulnerable

TT

Eliminate roadblocks in advance

TT

Engage often and early

TT

Be transparent and ethical

TT

Be confident but humble

TT

Be ready to improvise

TT

Lead with a story back by data and visuals

TT

Consider the right person

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DEVELOPING YOUR COMMUNICATION SKILLS Practicing the concepts in the Data Story Checklist in real professional situations is the best way to improve your presentation skills. Be diligent in your preparation and your delivery. Still, there are some ways you can improve and grow outside of individual presentations. Here are some creative approaches to improve your skills. Meetup Groups / Professional Association Becoming a part of a couple MeetUps or other professional associations in your area of expertise is a good way to practice communicating with your peers. It is also a good opportunity to learn from others and to observe how like-minded professionals respond to your approach. On top of being a great resource for your development, these sorts of groups can also help you build relevant relationships in your field. Contributing Author Learning to communicate also involves being a proficient writer. To grow your skills in this area, consider becoming a contributing author for a website or report in your field of study. Submit your writing through these channels and be attentive to comments and other feedback you receive. Becoming a published writer can be a valuable learning experience, and it looks good on a resume. Improvisational Theater Improvisational theater, or improv, is not only fun, but it is also a good way to grow as a communicator. Improv enhances your ability to adapt and think on your feet. It can broaden the way you think and help you see scenarios from unique perspectives. Improv shows and classes are usually available at affordable rates and can be a fresh way of broadening and refining your communication skills. Toastmasters International Toastmasters International is a professional, nonprofit organization concerned with educating people to become better communicators and public speakers [Toastmasters_1]. Over the last century, they have developed a systematic approach to

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communication, public speaking, and leadership. For those who might benefit from a structured format, becoming a member of Toastmasters International could be tremendously profitable.

CONCLUSION At this point, you have been given all the tools necessary to develop great data stories and to put them into motion. As you continue your journey, remember that practice makes perfect. More specifically, practice with feedback makes perfect. Try the exercises provided and continue to practice your skills with intentionality. It is all about putting in the reps and getting better.

REFERENCES 1. [MoreOrLess_1] More or less (radio programme), last edited October 12, 2022, https://en.wikipedia.org/wiki/More_or_Less_ (radio_programme) 2. [Toastmasters_1] Toastmasters International, https:// en.wikipedia.org/wiki/Toastmasters_International https://www. toastmasters.org/

EPILOGUE

C

ongratulations! Each journey starts with one step. This book is one step in a lifelong journey on being a better data translator. From the start we talked about how curiosity is a vital part of being a great data translator. Not simply for the extrinsic benefits it holds but for the intrinsic benefits. Your gift for making it to the end of this book is the gift of more learning opportunities. This has broken down into podcasts and books knowing some people prefer one or the other. These are not what you might thought of as traditional “data” podcasts and books but as a data translator your curiosity is broad. Also, a quick reminder to download the interview videos and transcripts that accompany this book. Learn from experts being interviewed on their experience and advice around being successful data translators. Additionally, for using this as a textbook, please see the sample syllabus included in the files. All these items can be obtained by writing to the publisher at [email protected].

TOP 20 PODCASTS FOR DATA TRANSLATORS Podcasts are great in that they are fun to listen to in cars, during workouts, on walks, and in many other places. Here is a list of the top 20 podcasts for data translators in alphabetical order. Note you will be catching a podcast on an episode whenever you subscribe so if an episode at first doesn’t resonate give another episode a try before determining if it is right for you. Further, podcasts may go dormant

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but even if dormant the nature of the podcasts being recommended will be valuable to listen to past episodes whenever you find them. 1. Against the Rules by Michael Lewis. Michael Lewis is the famed author of Moneyball and Liar’s Poker, and in his podcast Against the Rules takes look at fairness in life observing through the lens of people who depend on public trust. 2. Akimbo: Seth Godin has authored many great books and has been authoring a well-known daily blog for thousands of days. In Akimbo, Seth takes his experience and wisdom and provides short, applied advice for everyday people for their personal and professional lives. 3. a16z This podcast hits on many different topics in emerging technology with rapid frank debates on topics. As a data translator there is a constant back and forth and this podcast provides examples to emulate. 4. Behavioral Grooves: Tim Houlihan and Kurt Nelson have deep interviews with many of the leading behavioral scientists out there today. 5. The Brainy Business: Melina Palmer hosts one of the best applied behavioral science podcasts covering many core topics and how you can leverage them in your personal and professional lives. 6. Conversations with Tyler: Tyler Cowen is one of the best people at asking tough and challenging questions of far-reaching guests. 7. Dan Carlin’s Hardcore History: The most popular podcast related to history but more importantly is the focus and depth that Dan Carlin puts into telling his stories. Dan is truly one of the great storytellers of our generation. 8. Econtalk: Running for over fifteen years, Econtalk is a podcast with the tagline “Conversations for the curious.” There are a vast number of topics covered by prominent guests in a way that gets at the “so what” in complicated things. 9. Freakonomics: Published nearly twenty years ago, Freakonomics pushed the field of applied economics in a way that is extremely

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relatable for the general public. The podcast continues down that front covering many topics over the years. 10. Getting to Yes, And: This podcast is hosted by Second City which is famed for its improv classes and performances. Improv has many great lessons for data translators as mentioned in this book and this podcast is a great avenue into a deeper understanding of some of those lessons. 11. Hidden Brain: This podcast hosted by Shankar Vedantam helps curious people understand the world and themselves better. 12. Honestly: Bari Weiss covers topics that are sometime intentionally not covered by others and has guests having contrarian opinions from a mainstream media perspective. 13. The Jordan Harbinger Show: Jordan Harbinger interviews a variety of guests covering nearly every topic imaginable. You learn from his interviewing technique how to get information from others but also learn from the stories of others. 14. Learning Bayesian Statistics: Great data translators are Bayesian people by nature meaning they make the best decision with the information at hand and update their priors. This podcast dives into different concepts around Bayesian statistics that will help deepen this concept and apply it to real problems as a data translator. 15. Lex Fridman: Lex Fridman is described as conversations about the nature of intelligence, consciousness, love, and power. The deep conversations he has with guests and bringing out their true selves is quite enjoyable. 16. More or Less: This podcast is one of the best for looking at statistics that are given and questioning their truth in a short but meaningful way. 17. No Stupid Questions: Stephen Dubner and Angela Duckworth have a conversation around a question one of them or their audience had. They then have a good back and forth on the question applying their knowledge with Stephen being a well-known journalist and author and Angela being a well-known research psychologist and author.

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18. People I Mostly Admire: Hosted by Steven Leavitt who was one of the original authors of Freakonomics. Steven Levitt has conversations with other highly accomplished people covering a range of areas. He does a good job of humbly interviewing them and getting into revealing discussions. 19. The Rework Podcast: This podcast is described as a podcast on how to run and work your business, but it is more expansive on that. It really is about delivering work you can be proud about. 20. The Vance Crowe Podcast: Vance does a great job interviewing people from all walks of life and really getting them to share their story. It covers a lot of interesting and nontraditional topics.

TOP 20 BOOKS FOR DATA TRANSLATORS There are so many great books for data translators. Here is a list of the top 20 books for data translators in alphabetical order. If you prefer audio format, then most of these have audio versions as well. 1. The 4 Disciplines of Execution by Chris McChesney, Sean Covey, Jim Hurling, Scott Thele, and Beverly Walker (New York: Simon & Schuster, 2021). This book is one of the best books around defining and leveraging metrics in a way that drives desired change. 2. The 7 Habits of Highly Effective People by Stephen Covey (New York: Simon and Schuster, 1989). This book is a classic book that still sells well because it covers core concepts of fairness, integrity, honesty, and human dignity that are important for all people but especially data translators. 3. Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian (New York: Henry Holt and Company, 2016). This book covers a variety of computer algorithms that can be applied in your everyday life and does so with some interesting stories. 4. Antifragile by Nicholas Taleb (New York: Random House, 2014). This book is about gaining from disorder and chaos while at the same time being protected from fragilities and adverse events.

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5. The Black Swan by Nicholas Taleb (New York: Random House, 2007). This book is about why we are bad at predicting the future, especially for highly unlikely events. It also examines ways to become better at predicting the future. 6. The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie (New York: Basic Books, 2018). This book covers the timeless concept of causation versus causality and how understanding this is important to understanding human thought and artificial intelligence. 7. Effective Data Storytelling by Brent Dykes (Hoboken, NJ: Wiley, 2020): This is a great book focused on data storytelling in which Brent covers on a lot of important concepts in an engaging and easy to read manner. 8. How to Lie with Statistics by Darrell Huff (New York: W.W. Norton & Company, 1954): First published in 1954, it is a classic that is still highly relevant and covers a variety of statistical concepts leveraging interesting examples and stories. 9. Influence, New and Expanded: The Psychology of Persuasion by Robert Cialdini (New York: HarperCollins, 1984). As a data translator, influence is vital and this book covers seven core principles of persuasion that every data translator should be aware of and know how to leverage. 10. Nudge by Richard Thaler and Cass Sunstein (New York: Random House, 2008). This book is about how to unconsciously make better decisions by creating environments and systems that do so. 11. The Power of Moments by Chip Heath and Dan Heath (New York: Simon & Schuster, 2017). This book does a great job exploring the power of moments and their impact on others. Chip and Dan Heath intertwine great stories to cover concepts just as they always do. 12. Predictably Irrational by Dan Ariely (New York: HarperCollins, 2008): This book covers a variety of behavioral science concepts in bite-sized chunks that will help you expand your knowledge and ability to leverage behavioral science concepts as a data translator.

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13. Principles: Life and Work by Ray Dalio (New York: Simon & Schuster, 2017). Making better decisions is a core passion for all data translators, and Principles is focused on a variety of techniques that Ray Dalio has applied in both his life and at work. 14. The Quick Fix: Why Fad Psychology Can’t Cure Our Social Ills by Jesse Singal (New York: Farrar, Straus and Giroux, 2021). Good data translators are good contrarians. This book does a great job of being a contrarian to some popular psychology concepts while at the same time explaining things in a relatable and engaging manner. 15. The Signal and the Noise by Nate Silver (New York: Penguin Books, 2012). This book covers the world of prediction and how to be better at detecting signals to help you make better decisions from the massive amount of noise around those signals that exists in the world. 16. Talking to Strangers by Malcolm Gladwell (New York: Little, Brown and Company, 2019). This book covers a variety of stories that exhibit why well-intentioned people from different perspectives often struggle communicating and understanding others. 17. Thinking in Bets by Annie Duke (New York: Penguin, 2018). This book dives into why being a Bayesian thinker is a valuable approach to looking at the world, and offers options explaining how to be better at it. 18. Thinking in Systems by Donella Meadows (White River Junction, VT: Chelsea Green Publishing, 2008). This book focuses on systems thinking and the interdependence of many things, which is an especially important topic of understanding of data translators. 19. What Every Body Is Saying by Joe Navarro (New York: HarperCollins, 2008). This book does a great job helping the reader better understand body language, and how to utilize it to be a better listener and communicator. 20. What Your Customer Wants and Can’t Tell You: Unlocking Consumer Decisions with the Science of Behavioral Economics by Melina Palmer (Coral Gables, FL: Mango Publishing, 2021).

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This book does a great job covering many behavioral science concepts related to better unlocking desired consumer decisions. In addition to books and podcasts there are many great videos, courses, and websites that you will find online and that will further your success as a data translator. Now go enjoy your journey and help others along the way, and remember to be curious, customer focused, and data informed!

Index

Symbols 5 Whys, 85

A Advertising, 74 Amazon, 68, 147 Analytics as a service (AaaS), 68 Anscombe’s quartet, 123 Artificial general intelligence (AGI), 66 Artificial intelligence, 68 Axis gymnastics, 150

B Black panther, 150

C Central tendency, 55–57 Mean, 56 Median, 56 Mode, 56 Chief financial officer (CFO), 100 Chief marketing officer (CMO), 100 Cholera, 122 Classic story arcs Cinderella story, 112 Double man in a hole, 112 Icarus story, 113 Main in a hole, 112 Oedipus story, 113 Rags to riches, 111–113 Clean vs. Messy data Duplicate, 53 Inconsistent, 53 Incorrect, 52 Missing, 52 Outdated, 53 Coblis-color, 136

Cobra effect, 90 Color, 131–136 Categorical vs. Continuous, 135–136 Consistency, 134 Context correct, 133–134 Different, 135 Intensity, 135 Usage, 132–133 Color brewer, 136 Combination of reasons, 73–74 Didn’t know, 73 Don’t want the pain, 74 Miscommunication-frustration loop, 73 Wrong issue, 73–74 Communicating your data story, 165–178 Communication Data storytelling, 2 Data translation, 2 Initial, 97–98 Ongoing, 98–99 Communication skills, 1, 176–177 Meetups, 176 Coolors, 136 Corporate headquarter, 74 Correlation, 59–62 Curiosity, 5–6 Customer segmentation, 20–21 Customer understanding, 21–22

D Dashboard, 154 Analytical, 155 Best practices, 155–161 Away from text, 160 Balance interactivity, 159 Consistent, 158

188 • Index

Data source, 160, 160–161 Defaults matter, 161 What, why, and now what?, 156, 156–157 Checklist Confident and humble, 171, 171–172 Engage often and early, 170 Transparent and ethical, 170–171 Lifecycle, 161–162 Beginning, 161 End, 162 Middle, 162 Operational, 155 Storytelling, 162–163 Strategic, 154, 154–155 Data Heap, 151 Shuffle, 151 Data analysis, 1 Data as a service (DaaS), 68 Data communication Learning tools, 2 Visualization, 2 Data communicators Curiosity, 5 Empathy, 5 Trustworthiness, 5 Data modality Bimodal, 57 Multimodal, 57 Unimodal, 57 Data story, 105–120 Audience, 107–109 Existing narrative, 107–108 Canvas example, 116–120 Canvas introduction, 106–107 Checklist, 165–175 Authentically, 166–167 Eliminate roadblocks, 169–170 Improvise, 173–174, 174 Right person, 174 Story backed, 174 Test and verify, 167 Vulnerable, 168–169 Confidence, 115

Hook, 110–114 Source, 114 Topic, 106–107 Data storytelling, 2, 3 Data translation, 2 Data translator, 1–10 Data visualization, 2 Data visualization, 122–126 Annotations, 139 Axis labels, 139 Consistency, 140 Data labels, 139 Eight common Comparison, 127 Data over time, 127 Distribution, 127 Hierarchy, 127 Location, 128 Parts, 128 Pattern, 128 Relationships, 128 Exploratory, 122–125 Format, 140 Legends, 138 Moving beyond design and communicating, 144–151 Engage, 145 Layer, 148 Opinions, 146 Prioritize, 145 Trust, 149 Principles, 126–136 Purpose, 122–125 Source, 140 Storytelling, 125–126 Summaries, 137 Text, 136–144 Titles, 137 Trends and references, 141 Data visualization catalogue, 128 Dave chappelle, 167 Dave mathias user manual Communication, 17 Environment, 17 Feedback, 17 Fun and interesting facts, 17

Index • 189

Learn, 17 Meeting, 17 Overview and goals, 17 Strengths and opportunities, 17 Things that delight and annoy, 17 Dear data, 12 Decisions start with people, 11–28 Default effect, 161 Deshboard Best practices Z-pattern, 159

Fictional data, 54–55 Foundational analytics, 66–67 Artificial intelligence, 66 Classification vs. Regression, 67 Machine learning, 66 Specific vs. General artificial intelligence, 66, 66–67 Supervised vs. Unsupervised learning, 67

Continuity, 144 Proximity, 144 Similarity, 144 Symmetry, 144 Giorgia lupi, 12 Goliaths, 118 Goodhart’s law, 90 Good questions and great listening, 29–48 Definition, 30–33 How to ask, 33–39 Body language and tone, 36–38 Defuse with your questions, 35–36 Right customer, 34 Right setting, 34–35 Right time and place, 35 Importance, 29, 29–30 Google, 68 Google sheet, 54 Governmental data policies, 24 Great listener, 40–47 Focus, 41–43 Environment, 41 External distractions, 41 Getting off track, 41 Internal distractions, 41 Positive expectation, 41 Responsive listener Acknowledge, 45 Pause, 45 Understand, 43–45 Don’t interrupt, 43 Listen then respond, 44 Open mind, 43 Take notes, 44 Tone and body language signals, 43 Gregory treverton, 77

G

H

Gabriel iglesias, 167 Gestalt principles, 143 Gestalt’s design, 143 Closure, 144 Common fate, 144

Harnessing Angle, 129 Position, 130 Size, 129 Hellen keller, 5

E Elephant, 121 Empathy, 6–7 Employee net promoter score (ENPS), 145 Engage your customer, 23 Biases, blind spots, strengths, and weaknesses:, 24 Communication, 24 Creating customer personas, 24 History, 23 Problems and challenges, 23 Tradeoffs, 24 Excel sheet, 54

F

190 • Index

How to win friends and influence people, 40 Human resource, 74, 77

I Identify, understand, and frame problems, 73–88 Inferential statistics, 62–64 Calculating, 63 Confidence level, 63 Margin of error, 63 Population, 62 Sample size, 63

J Jefferson memorial, 84 Jerry seinfeld, 167 Job opening, 74 Johari window, 13 John snow, 122 Josef stalin, 12

K Key performance indicators (KPIS), 155

L Language data, 49–72 Structured vs. Unstructured, 50 Understanding, 50 Leading and lagging indicators Behavior, 94 Confidence, 94 Relation, 94 Time, 94 Leveraging dashboards, 153–164 Leveraging data, 1 Leveraging visuals, 121–152

M Malcolm gladwell, 166 Margin, 89 Marketing, 74

Merriam-webster, 153 Messiness, 11 Metrics Customer experience example, 96 Downward, 95 Effectiveness, 94 Efficiency, 94 Inventory, 101 Lifecycle, 101 Number, 101 Outcome, 94 Outcome redundancy, 101 Owner, 101 Return on investment (ROI), 100–101 Rollout, 101 Sales activity example, 95–96 Upward, 95 Microsoft, 68 Model complexity, 68 Model performance, 68 Model transparency, 68 Mozzarella cheese, 61

N Napoleon, 126 Napoleon’s russian campaign, 142 Navigate, 74 Net promoter score (NPS), 96 New york times, 126 Numerical data Continuous, 51 Discrete, 51 Nominal, 51 Ordinal, 51

O Operationalizing metrics, 100–102

P Perpetual model bias, 70–71 Problem-ask-value (PAV), 75 Profit, 89 Public speakers, 176

Index • 191

Purpose-audience-communicate-target (PACT), 91, 100 Purpose metric, 91–95 Align people, 92 Communicate priorities, 92 Define expectations, 93 Leading and lagging, 93 Motivate behavior, 92 Reduce uncertainty, 93 Show progress, 92

R Reframing problems, 81–86 Mapping why, 84 Path mapping, 82–83 Point of view, 81–82 Priorities view, 83–84 System mapping, 82 Timeframe view, 83 Responsive listener Ask, 46

S Simplifying insights through metrics and objectives, 89–104 Six sigma, 59 Specific purpose artificial intelligence (SPAI), 66 Statistics, 53–64 Descriptive, 54–62 Interpret, 53 Powerful tool, 53 Stefanie posavec, 12 Storyteller, 96 Storytellers, 74 Storytelling, 2 Superpower analytics and data science, 64–67

T Talent management, 22–26 Target, 99–102 Accuracy, 100 Precision, 100

Technical hiring, 26 Technology, 2 Tenzin gyatso, 40 The hitchhiker’s guide to the galaxy, 66 Thought experiment, 98 Tina fey, 167 Tradeoffs Audience-specific, 114 Data and analysis, 115 Translators, 74 Transparency and performance, 68–70 Trust, 7 Types of analytics, 64 Descriptive, 65 Diagnostic, 65 Predictive, 65 Prescriptive, 65

U Understand Problem, 76–78 Question, 78 Value, 79–80 Loss aversion, 80 Time discounting, 79–80 Understanding Engaging, and communicating with your customer, 20–26 Incentives and biases, 18–20 Understanding pain, 75 Understanding people, 12–17 User manual Communication, 15 Environment, 15 Fun and interesting facts, 16 Learning and feedback, 15 Meeting, 15 Overview and goals, 15 Strengths and opportunities, 15 Things that delight and annoy, 16

V Value framework, 75–81 Variability data Interquartile, 58

192 • Index

Range, 58 Standard deviation, 58 Variance, 58

W Wolves and huskies, 69 World focus wave (WFW), 116