The VALUE DRIVEN DATA Workbook: Practical exercises, templates, and tools for data value creation

Table of contents :
ENDORSEMENTS FOR VALUE DRIVEN DATA
ABOUT THE AUTHOR
ACKNOWLEDGEMENTS
INTRODUCTION
PART ONE
VISION: DISCOVERING AND CAPTURING DATA VALUE OPPORTUNITIES
Chapter 01
Enhancing Understanding of Data Vision
Exercise 1: Defining Data Value
Exercise 2: Interpreting Data Vision
Exercise 3: Differentiating Data Vision
Exercise 4: Macro Data Vision
Exercise 5: Separating Signal from Noise
Exercise 6: Signal from Noise Optimization Techniques
Chapter TWO
Capturing Data Visions
Exercise 1: Identifying Budget Challenges
Exercise 2: Reframing Budget Challenges
Exercise 3: Time Horizon and Budget Challenges
Exercise 4: Current State Assessments
Exercise 5: First Principle Thinking
Exercise 6: Vision Perspectives and Leadership Style
Chapter THREE
Why Data Visions of All Size Matter
Exercise 1: Understanding Data Accessibility Challenges
Exercise 2: Analysing Data Granularity and Timeliness
Exercise 3: Identifying Data Quality Issues
Exercise 4: Recognizing Foundational Data Analysis Challenges
Exercise 5: Exploring Data Vision Breakdown
Exercise 6: Clear Goals Analysis
Exercise 7: Tangible Purpose Exploration
Exercise 8: Enriching Data Vision Techniques
Exercise 9: Strategic Decision Enhancement
Exercise 10: Reflection and Application
Chapter FOUR
The Destructive Impact of Data Vision Misalignment
Exercise 1: Evaluating Current Data Capabilities
Exercise 2: Identifying Challenges with Data Vision Alignment
Exercise 3: Detecting and Defusing Data Vision Displacement
Exercise 4: Embracing Alternative Viewpoints
Exercise 5: Framework for Disruption Detection
Exercise 6: Unlocking the Power of Diversity
Exercise 7: Phenomenology and Alignment
CHAPTER FIVE
Simplifying Data Vision Misalignments
Exercise 1: Understanding the Three-Step Process for Data Vision Alignment
Exercise 2: Conceptualizing Data Vision Alignment
Exercise 3: Analysing the Streamlined Three-Step Process
Exercise 4: Identifying Obstacles to Data Vision Alignment
Exercise 5: Examining Speed as a Key Factor in Data Vision Alignment
Exercise 6: Uncovering Data Quality Matters in Data Vision Alignment
Exercise 7: Addressing Technology and Infrastructure Concerns
Exercise 8: Reflecting on Data Vision Alignment Challenges
Exercise 9: Applying the Streamlined Approach to Data Vision Alignment
PART TWO
OBSTACLES: THE THINGS THAT STAND BETWEEN DATA VISIONS AND DATA VALUE REALIZATION
Chapter SIX
Obstacles of the Past
Exercise 1: Reflection on Heritage and Legacy Data Platforms
Exercise 2: Exploring Data Use within a Legacy System Context
Exercise 3: Shifting from Obstacles to Opportunities
Exercise 4: Legacy Data for Decision-Making
Exercise 5: Heritage Skills and Capabilities
Exercise 6: Complacencies from Past Successes
Exercise 7: Data Quality Assessment
Exercise 8: Measuring Data Quality Impact
Exercise 9: The Value of Timeliness
Exercise 10: Overcoming Resistance to Change
Exercise 11: Evaluating Buy vs. Build Trade-offs
Chapter SEVEN
Enhancing Understanding of Obstacles of the Future
Exercise 1: Reflecting on Misunderstandings and Mistaken Assumptions
Exercise 2: Identifying Disconnects Resulting from Mistaken Assumptions
Exercise 3: Analysing Misplaced Assumptions Driving Inappropriate Solutions
Exercise 4: Addressing Unknown Obstacles
Exercise 5: Understanding Personal Data Protection
Exercise 6: Reflection and Analysis
Exercise 7: Case Study Analysis
Exercise 8: Applying Strategies
Exercise 9: Reflection and Action Plan
Chapter EIGHT
Obstacles of the Present
Exercise 1: Skills Matrix Analysis
Exercise 2: Leadership Competency Assessment
Exercise 3: Task Distribution Analysis
Exercise 4: Decision Leadership Assessment
Exercise 5: Reflection on Data Strategy
Exercise 6: Responsible Leadership for High-Performing Teams
Exercise 7: Overcoming Complexity and Complications
Exercise 8: Seeing Beyond the Challenges
Exercise 9: Fixing a Flying Plane - Transition and Migration
Exercise 10: Reflection on Growth Limiting Factors
Exercise 11: Analysing Obstacles for Future Growth
Exercise 12: Critical Steps for Ensuring the "Right" Speed of Execution
Exercise 13: Reducing Defensiveness for Collaborative Efforts
Exercise 14: Addressing Budgetary and Funding Issues
Exercise 15: Utilising the VOV Model for Commercial Value Connectivity
Exercise 16: Understanding Minimum and Maximum Viability
PART THREE
VALUE: IDENTIFYING, CAPTURING AND COMMUNICATING DATA VALUE
Chapter NINE
Capturing Data Value Propositions
Exercise 1: Understanding Data Value Propositions
Exercise 2: Bottom-Line Value (BLV) Optimization
Exercise 3: Top-Line Value (TLV) Optimization
Exercise 4: Cost Avoidance Value (CAV)
Exercise 5: Understanding Data Costs
Exercise 6: A Business Stakeholder Perspective of Data Value Capture
Exercise 7: RTB and CTB Optimization
Exercise 8: Reflecting on Data Value Propositions
Exercise 9: Applying Data Strategies
Exercise 10: Evaluating Data Analytics Initiatives
Exercise 11: Case Study Analysis
Chapter TEN
Measuring Data Value for Business Case and Operational Assurance
Exercise 1: Macro vs. Micro Data Value Measurement
Exercise 2: Understanding Business Stakeholder Perspectives
Exercise 3: Assessing Data Value in a Multifaceted Operation
Exercise 4: Articulating Data Value Propositions
Exercise 5: Addressing Cost-Avoidance through Data Value
Exercise 6: Macro-Level Data Value Measurement
Exercise 7: Generating a Data Value Business Case
Exercise 8: Reflection and Application
Exercise 9: Macro and Micro Approaches to Data Value Measurement
Exercise 10: Stakeholder Perspectives on Data Value Measurement
Exercise 11: Generating a Data Value Business Case
Exercise 12: Data Value for Different Departments
Chapter ELEVEN
Understanding the Data Value Measurement Lifecycle
Exercise 1: Estimation Phase
Exercise 2: Delivery Phase
Exercise 3: Operations Phase
Exercise 4: The Triple BAT Model for Data Value Measurement
Exercise 5: The Application of the Triple BAT Model
Exercise 6: Milestones of the Data Value Measurement Lifecycle
Exercise 7: Challenges in Data Value Estimation
Exercise 8: Challenges in Data Value Validation
Exercise 9: Challenges in Data Value Monitoring
Chapter TWELVE
Enhancing Understanding of Data Value Profits and Losses
Exercise 1: Vision and Value Proposition
Exercise 2: Understanding the Impact of Returns
Exercise 3: Estimating Value Returns on Investment
Exercise 4: Identifying Challenges for Data Value P&L
Exercise 5: Reflecting on the Challenges for a Data Value P&L
Exercise 6: Simplifying Data Value Assessment
Exercise 7: Increasing Resource Autonomy
Exercise 8: Reducing Interdependencies
Exercise 9: Overcoming Traditional Obstacles with Silos
Exercise 10: Case Study Analysis
Exercise 11: Essential Preconditions for a Data Value P&L
Exercise 12: Reflection and Application
Exercise 13: Group Discussion
Exercise 14: Action Plan
Chapter THIRTEEN
Presenting Data Value to Executives and the Board
Exercise 1: Presentation Structure Analysis
Exercise 2: Unexpected Findings
Exercise 3: Identifying Obstacles
Exercise 4: Focusing on Ambitious Visions and Associated Value
Exercise 5: Transforming Data through Connected Organisational Silos
Exercise 6: Role Analysis and Reflection
Exercise 7: Technology Platforms and Data Transformation
Exercise 8: People and Culture in Data Transformation
Exercise 9: Decoupled Data Value Framework
Exercise 10: Unpacking Data Value Presentation Slides
CONCLUSION: BRINGING IT ALL TOGETHER
YOUR JOURNEY CONTINUES: BUILDING ON "VALUE DRIVEN DATA"
Empower Yourself
Empower Others

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