In Episode 24 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by David Castro-Gavino, Executive Director, Head of Data Deployment at AstraZeneca, and joined by his co-author Boyan Angelov, Principal Strategist at Exxeta, where they discuss their new book, Enabling Data, and why the data industry is still stuck in Groundhog Day. The same three arguments – who owns that number, is it right, and why does it take so long – have been repeating for thirty years, and rather than fixing them, the industry keeps renaming the problem.
The conversation covers why most complexity in data is self-inflicted, why maturity models are "data theatre" compared to diagnosing friction, and why AI hasn't solved any of this – it has poured fuel on the fire.
They also discuss:
- Why the industry has a short collective memory and keeps rediscovering problems solved twenty years ago.
- What the three recurring arguments are that every data organisation keeps having.
- Why renaming the symptom – big data, data mesh, platforms – never fixes the underlying problem.
- How a simple pizza business becomes a data nightmare the moment it goes digital.
- Why most complexity in data is self-inflicted, and why that is good news.
- Why "technology is not the problem, you are" is deliberately provocative.
- What four questions to ask before going back to the market for a new tool.
- Why fixing the system, not the tool, is the maxim that matters.
- Who the "merchants of complexity" are, and why consultants are usually the culprits.
- Why making things simple is the hardest job in data.
- What's wrong with using maturity scores as the objective.
- Why measuring the wrong things promotes the wrong behaviours.
- How to practically find friction by refusing to accept the first answer.
- Why friction looks different for an analyst, an engineer and a business leader.
- Why not every foundational problem needs to be solved, and how to avoid spending forever in the basement.
- Why data teams that don't understand the business are missing the biggest opportunity.
- What the cargo cult is, and why copying Spotify's operating model won't make you Spotify.
- How the enabling model's four pillars – people, governance, technology and enablement – fit together.
- Why the fragility of senior data leadership is structural rather than personal.
- Why data doesn't create friction in an organisation, it reveals it – and gets blamed for it.
- Why a clear mandate matters more than who the CDO reports to.
- Why the industry needs to stop hiring data leaders on a shopping list of technical skills.
- How AI has exposed how little progress most companies have actually made on the fundamentals.
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