Essays for board members, executives and data and AI leaders. Each one takes a single question about data, AI or governance and works through what matters, why, and what it means for your decisions.
A few years ago, I watched a product recommender start behaving oddly. The model was fine. The pipelines were fine. What had changed was something nobody on the data science team knew about. That moment showed me something I now see everywhere: data visibility is AI's most overlooked bottleneck.
Someone asks: 'What data do we already have?' Within minutes, the room is problem-solving. Everyone wants to deliver. So the team works with what's available. And from that moment, the most expensive decision in the project has already been made.
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.