Every data clean-up has the same ending if you let it: six months later there are 400 new duplicates and three fields nobody can define.
That’s not a discipline problem. It’s a design problem. Data goes bad because there’s no agreed definition of a record, no validation on the way in, no owner for a field, and no way to notice decay until someone runs a report and gets a number that’s obviously wrong.
Governance sounds heavy, but at your scale it’s a small number of concrete things:
- Definitions. What counts as an active client, a qualified lead, a current member — written down and agreed once, so two teams don’t report different totals.
- Ownership. A named person for each core object, responsible for its quality. Not a committee.
- Validation at the point of entry. Required fields, picklists instead of free text, deduplication on create, and address and email verification.
- Monitoring. A quality dashboard that tracks duplicates, blank required fields and stale records so problems surface in weeks, not years.
- Access and retention. Who can see what, what gets archived, and what you’re obliged to delete.
We’ll also be blunt about what to skip. Most small and mid-sized organisations don’t need a data governance framework; they need five validation rules and someone to own the contact record. We’ll tell you which category you’re in.