Model

72 posts filed under Model.

Most data teams know their monthly cloud bill. Far fewer can tell you which euro produced value and which one was pure waste.

Run your AI feature twice on the same input. If a different answer would be a problem, you've found a deterministic requirement.

An e-commerce client's reporting ran on a spreadsheet one person updated by hand every morning. It broke every quarter, always at month-end.

The team bought more tokens. The agent still gave confident, wrong answers. The bottleneck was never compute.

The junior left in March. In May, marketing's dashboard broke and nobody knew who to call.

Two teams sitting in different Slack channels build two pipelines that compute the same metric.

This company piloted Data Mesh with 2 domains in 6 months. Here's what worked (and what didn't).

AI is generating data quality tests now. Not replacing you. Multiplying what you can realistically cover.

Every governance rebuild I've seen starts the same way: a team that over-corrected once, then over-corrected the other way, and is now tired …

"Do nothing" is never free. One client's "zero cost" status quo was burning 200K a year in workarounds nobody tracked.

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