Artificial Intelligence
A practical field guide to the habits that separate teams who trust their data from teams who don't.
Good data management isn't a single project with an end date — it's a set of habits. This field guide covers the ones that consistently separate teams who trust their data from teams who quietly don't.
A dataset without notes on its source, limitations, and last update becomes a liability the moment its creator moves to a different project.
Permissions granted for a one-time project rarely get revoked. A quarterly review catches the access nobody remembers granting.
Old datasets and unused pipelines aren't free to keep around — they're additional surface area for something to break or leak.
None of these habits are complicated. What's hard is doing them consistently, which is exactly why the teams who do stand out.