Two dashboards, two definitions of 'active user', two different numbers in the same meeting. Without a shared source of truth, analytics stops being evidence and starts being ammunition.
A metrics layer fixes this by defining each metric once — the exact events, filters and grain — in version-controlled code that every tool reads from. 'Active user' means one thing, everywhere, because it's defined in one place.
Model the events cleanly upstream first: consistent names, a stable identity, and the handful of properties you'll actually slice by. A metrics layer built on messy events just gives you consistent nonsense.
With definitions centralised, self-serve becomes safe. Analysts and PMs can explore without quietly reinventing a metric, and a change to a definition updates every dashboard at once instead of drifting apart over months.
The goal isn't more charts — it's fewer arguments. When everyone trusts that the number means what it says, the conversation moves from 'is this right?' to 'what do we do about it?'