You wouldn’t ship a software product without an owner, a roadmap, or a release process. Why do you treat your data differently?
Think about how your engineering team ships software. There’s an owner. A backlog. A definition of done. Someone who decides what goes into the next release and what doesn’t.
Now think about how your data team ships datasets. Probably nothing like that.
Most data “products” just… exist. They grew organically. Someone built a pipeline two years ago, someone else added a transformation, a third person built a dashboard on top. Nobody planned it. Nobody maintains it. When it breaks, the Slack channel explodes.
I’ve watched teams treat their data platform like the kitchen of a student house. Everyone uses it, nobody cleans it, and by Friday you can’t find a single clean pan.
The good news: you’ve solved this before. Pick one critical dataset. Give it the same treatment you’d give a software product: one owner, a documented schema, freshness SLAs, a changelog. Run it for a month. Watch what happens to the support requests.
You already know how to build products. You’re just not applying it to data yet.
What’s the one dataset in your org that everyone depends on but nobody officially owns?