You cannot manage what you never measure. Here is a starter set of governance KPls you ...

tgroenwals shared this post · 1h ago

You cannot manage what you never measure. Here is a starter set of governance KPls you ...

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: Percentage of automated checks that pass for Tier 1 datasets

Change and risk KPIs
👉 Breaking change rate: Percentage of schema or metric changes that cause downstream issues
👉 Change lead time: Average time to move a governed change from proposal to production
👉 Contract compliance: Percentage of data contracts with all required checks implemented

Start small. Pick two adoption metrics, two trust metrics, and one change metric.

If you want to know more about how Governance Teams get their data AI-Ready: https://lnkd.in/drCujK25

Alexander Johnfernandez Useful starter set. I would add one distinction to the “Trust KPIs” category: measuring governance performance is not necessarily the same as measuring whether a decision is justified in relying on the data.

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.