The Data Hero Playbook

For thirty years, "best practices" in data have delivered bigger platforms, thicker slide decks, and disappointing outcomes. The problem isn't technology. It's mindset.
The Data Hero Playbook reveals how limiting beliefs-learned helplessness, all-or-nothing thinking, externalized blame-have kept data from becoming truly transformational. The cure is a growth mindset, put into action through practical steps any data professional can take.
You'll discover how to:

• Put customers at the center of every decision.
• Apply product management principles to data work.
• Quantify value in revenue, cost savings, and risk reduction.
• Run data as if it were a P&L.
• Deliver fast wins with a Data Strategy MVP in weeks, not months.

Unflinching about what hasn't worked yet unapologetically hopeful about what's next, this book is a call to arms for data leaders ready to escape the status quo.
If you want to stop explaining why data should matter and start proving it with measurable results, The Data Hero Playbook is your guide. Become the data hero your company actually needs.

Introduction xv
Chapter 1: The Data Hero Origin Story 1
Chapter 2: The Data Hero Superpower: A Positive Mindset 17
What's a Mindset? 17
Mindset and Corporate Culture 21
Traits of a Positive Mindset and Acts of Data Heroism 24
Adaptability and Willingness to Change 25
Resiliency 27
Innovation and Risk-Taking, Reduced Fear of Failure 30
Open to Feedback and Criticism 34
Seeks Opportunities to Collaborate 36
Chapter 3: The Anti-hero: Limiting Mindsets 41
All-or-Nothing Thinking 42
Lack of Accountability 45
Blaming Others 49
Avoid Challenges, Reluctance to Take Risks 52
Embrace the Status Quo, Resist Change 56
Failure to See Positive Intent 59
Chapter 4: The Wrath of the Anti-hero in Data and Analytics 63
The Unwillingness to Quantify the Value of Data 64
Data Literacy and Blaming Customers for Product Failures 69
Extreme Forms of "Data First" or "Data Driven" 76
Data Culture Is a Dependency to Deliver Value and Is Somebody Else's Problem 80
Garbage In, Garbage Out 83
Seeing Negative Intentions in Others 88
Deterministic, "All-or-Nothing" Thinking in a Probabilistic World 96
Chapter 5: Reinforcement Mechanisms in Data and Analytics 103
Market Realities 105
Information Technology Ecosystem Feedback Loop 105
Analyst Influences 112
Consultant Influences 120
Vendor Influences 128
Social Media Influences 133
Technology Influences 140
Chapter 6: Putting Your Customer at the Center of Everything You Do 147
Become Customer Driven, Not Data Driven 149
Focus on Customers and Their Business Processes, Not Technology 152
Assume Positive Intentions, Have Empathy 153
Better Aligned Incentives and Success Metrics 155
Proactive Engagement and Feedback Loops 157
Revisit Organizational Structures, Roles, and Responsibilities 158
Chapter 7: Integrating Product Management as a Discipline Within Data and Analytics Teams 163
The P&L North Star 165
Hire a Product Manager 167
Embrace User- and Customer-Centric Design Methodologies 169
Hire a Value Engineer and Measure the Cost and Benefit of Everything 172
Implement a "Go to Market" Function; Repackage Governance and Literacy 178
Changing Your Data Governance Function to a Customer Enablement Function 179
Changing a Data Literacy Focus to a Customer Training Function 183
Separate Data Management from Data Product Management (and GTM) 185
Evolve Your Organization Toward Customer and Product Centricity 187
Data Supply Chain Management 189
Data Product Manufacturing (or Development) 189
Data Product Management and PMO 190
Finance, Planning, and Analysis 192
Chapter 8: Embrace Agility and a Relentless Focus on Value Delivery 195
The Data Strategy MVP 196
Success Metrics/Business Cases 199
Scope, Approach, and Roadmap 201
The Data Governance Model 203
The Data and Analytics Organizational Model 205
D&A Product Management 206
Technology and Infrastructure 208
Wash, Rinse, and Repeat 210
Chapter 9: Look Inward Before Looking Outward 215
Be Humble 216
Embrace Critical Thinking 219
Lead by Example 221
Make Room for Failure 228
Be Practical 232
Chapter 10: Looking Forward 235
Natively Digital 235
Data and AI Haves and Have-Nots 240
DataOps and the Convergence of Data and Product Functions 241
Data Monetization and Widespread Data Sharing 243
Data Consortiums and Governance Networks 246
Data Sustainability 249
Data as an Asset 254
In Closing 256
Index 259