Weekly essays with actionable insights on what truly matters in data and AI and why they matter for your organization's long-term success. I write to clarify my own thinking, articulating ideas forces deeper understanding and broader perspective. My goal is for these reflections to offer practical value as you navigate your own data and AI journey.
Most governance frameworks grew by accumulation. The 'cut in half' question forces you to explain why each piece exists. What you'll find is which parts have a clear rationale and which parts exist purely because they seemed responsible at the time.
We built models to predict grocery baskets. The better they got, the smaller the baskets became. Google, Meta, and now OpenAI have tried to bolt shopping onto their platforms. The disruption never lands. The gap is not compute power. It is a misunderstanding of how people actually shop.
Data management has a predictable failure mode: lack of urgency at strategy level, lack of clarity in execution, lack of mandate with the fixers. AI is about to make that dysfunction impossible to survive. The answer is not more governance. It is fewer people in the loop.
In nearly twenty years working with data, I have yet to meet a business stakeholder who woke up wanting a data product. The shift we need is from describing what data is to articulating what data does. "Data-as-a-product" was the right vocabulary for the wrong conversation.
AI is removing the technical barriers that shaped data strategy. When every team can build, the bottleneck shifts from capability to direction. The data leader's most important job isn't enabling more use cases. It's translating business strategy into clear data priorities and saying no to the rest.
The vision of everyone vibe-coding their own tools sounds productive. Until you multiply it by a few thousand employees. Then you don't have innovation. You have chaos, security nightmares, and a fundamental misunderstanding of what makes people successful in organizations.