You cannot manage what you never measure. Here is a starter set of governance KPls you can track from day one.
Adoption KPIs
👉 Catalog usage: Number of monthly active users in the catalog or governance portal
👉 Certified dataset coverage: Percentage of key reports that are built on certified datasets
👉 Owner coverage: Percentage of Tier 1 datasets with an assigned owner and steward
Trust KPIs
👉 Data incident rate: Number of high-impact data incidents per month or quarter
👉 Mean time to resolve (MTTR): Average time from detection to resolution for data incidents
👉 Policy compliance rate:…
For example, low incident rates, high policy compliance and fast MTTR can tell us that governance processes are functioning. They don't necessarily tell us whether a dataset remains fit to rely upon for a particular decision, purpose, scope and point in time.
I would therefore consider a further KPI family around Reliance:
→ % of critical decisions with an explicit reliance condition
→ % of critical datasets with current validation/revalidation status
→ % of downstream dependencies assessed after material change
→ time from a material change to withdrawal or revalidation of affected reliance
This moves the question from “Are we governing the data?” to “Can we demonstrate why the organisation is entitled to rely on this data for this decision?”
That distinction becomes increasingly important as data feeds AI systems and autonomous decision processes.