Why do organisations still struggle to realise meaningful value from data and artificial intelligence, despite decades of technological progress?
Despite decades of investment in data platforms, analytics tools and artificial intelligence, many organisations still struggle to turn data into real business impact. Too often, data initiatives become trapped in cycles of pilots, fragmented platforms and growing technical complexity, while the promised value remains just out of reach. This book explores why that happens and what organisations must do differently to unlock the true potential of data and AI.
At the centre of the book is the Enabling Data Model, a new mental model for designing organisations where data, analytics and AI can scale. Rather than focusing solely on technology or isolated innovation projects, the model highlights the organisational foundations required for data work to succeed, aligning strategy, governance, technology and people around the shared goal of generating value from data.
Accompanied by a Data Manifesto that articulates the first principles of enabling data, the book helps leaders rethink how data initiatives should be structured, governed and sustained in complex organisations. Readers will gain:
- A clear explanation of why many data and AI initiatives fail to deliver impact
- A new mental model for aligning data capabilities with business strategy and innovation
- Practical tools including diagnostic rubrics, data personas, use case templates and the Platform Fit Canvas to translate ideas into action
Combining strategic insight with practical guidance, this book equips senior data leaders, CDOs, product leaders and analysts with the perspectives and tools needed to move beyond experimentation and build the foundations required for data, analytics and AI to generate lasting business value.
Table of Contents:
Foreword - Thomas C. Redman
Chapter 1: Groundhog Day
Chapter 2: The Promise of AI
Chapter 3: The HelloFresh Data Story
Chapter 4: The Dark Side of the Role
Chapter 5: The Data Manifesto
Chapter 6: A Model in Practice
Conclusion
Appendix 1: Appying the Enabling Data Model
Appendix 2: Model in practice: metadata as product
About the Author :
David Castro-Gavino is a data and AI executive, author and advisor whose career spans five continents and nearly three decades of international leadership. He has built and led data functions at some of the world's most recognised organisations, including AstraZeneca, HelloFresh, Booking.com and dunnhumby, working across some of the most complex and regulated industries in the world. He speaks, writes and lectures on data and AI, and advises boards and executive teams on strategy. He is based in London, UK.
Boyan Angelov is a Principal Consultant at Exxeta, an innovative technology and consultancy firm, and a lecturer on data and AI. His previous books include Elements of Data Strategy and Python and R for the Modern Data Scientist, which he co-authored, and his writing has appeared in outlets such as Handelsblatt and CDO Magazine. Throughout his career he has advised organisations of all sizes on data and AI-powered transformation, in roles ranging from data strategist to CTO. He is based in Berlin, Germany.
Review :
Enabling Data is a timely and important contribution to the growing conversation on data and AI transformation. Rather than adding another framework to an already crowded field, the authors offer a compelling model grounded in real-world experience, showing how trust, governance, enablement, and technology must work together to create lasting value. A thoughtful and highly practical guide for modern organizations.
“Enabling data” has become one of those phrases the industry loves to repeat without ever stopping to define. David Castro-Gavino and Boyan Angelov do both: they cut through the buzzword to show what enabling data actually requires in practice, and more usefully, what trips organizations up along the way. From the modern data stack to data mesh to AI, they name the traps, debunk the misconceptions, and offer practical guidance rooted in first principles. A thorough read, and definitely required reading for anyone working in data.
This book is therapeutic for any data leader who has navigated vendor and consultancy hell. David and Boyan write from the inside, you feel as if you are sitting with them, living the experience. The manifesto and enabling data model are not theoretical constructs; they are forged through real experience leading data in large, complex organisations. Required reading for anyone serious about making data work.
Best book out there to understand what AI-ready data really is all about and how to get there. It tackles the AI revolution at exactly the messy, unglamorous point where organizations are stuck and at the same time offers a new, sharp new lens on building data foundations that moves beyond “just build a big data platform” advice. Highly recommended reading for anyone who has more AI ambition than running a few ChatGPT prompts.
David Castro-Gavino and Boyan Angelov have written an exceptional book that cuts through the hype surrounding data and AI to address what truly drives organisational success. Drawing on deep practical experience, David and Boyan show that lasting impact comes not from chasing the latest technology, but from building the right foundations, culture and capabilities. Rich with real-world examples and clear insights, this is an essential read for executives and anyone determined to turn data into sustained competitive advantage.
Most data leadership books present a framework. This one doesn't. Castro-Gavino and Angelov focus on the root cause of our industry's problems; that data initiatives often fail to deliver what was expected of them, not because of talent, data or technology, but because of flawed assumptions about what data leadership is and what it could be delivering. Those assumptions create structural problems that prevent good work from ever delivering value. This book frames that pattern clearly, then offers a genuinely practical way through to challenge and change those assumptions, backed by real-life examples. A must read for people trying to turn data activity into tangible business outcomes.
