Data Hero Playbook

MALCOLM HAWKER is a data strategy and management leader with over 25 years’ experience in the industry. He has authored industry-defining research and has consulted to some of the world’s largest businesses on their data and analytics strategies. He’s a frequent public speaker on data and analytics best practices.

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