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…mehr
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.
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.
Inhaltsangabe
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
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
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