Newsletter

"Organizing data" is commonly presented as a choice among frameworks. That suits vendors and tidies slides—but it does nothing for profit and loss.

Few debates are as solemn as the one about data architecture. The agenda fills with technical nouns—lake, mesh, fabric—and soon everyone has forgotten that the results will … Read full article

A very expensive lie in data today? "Ready-to-serve data for every use case."

The promises are seductive: universal definitions that work for everyone, self-service analytics without context switching, numbers that align across the company without argument.

But meaning doesn't work that way. Data is always recorded in a specific context … Read full article

Most companies are either worrying they aren't talking enough about AI—or talking way too much about it. Both miss the point.

The error is mistaking the method for the outcome. While executives fret about falling behind in the AI race or scatter effort across unfocused initiatives, the crucial question remains: … Read full article

Let’s retire the "AI onion" and consider a more useful framework: "Machine learning mode" vs "AI mode".

We've all seen the “AI onion”: nested circles where AI contains machine learning, machine learning contains deep learning, and so on. It's been copied countless times and serves a pedagogical purpose. But upon … Read full article

Instead of building data systems optimized for stability, we need systems architected for change.

The conventional wisdom about modern data governance sounds reassuringly methodical: treat data like a product, build for reuse, establish clear ownership, and watch costs fall as adoption rises. It's the kind of advice that feels both … Read full article