Data Governance Metrics: The 12 KPIs That Actually Matter

Data governance metrics are the quantitative KPIs that measure whether a governance program is working. The twelve metrics that actually matter in 2026: catalog coverage, policy coverage, incident count, mean time to resolution (MTTR), quality SLA compliance, audit-ready score, glossary adoption, access review completion, lineage coverage, AI agent compliance, cost of non-compliance, and business value delivered. Anything else is vanity.

This guide walks through each metric, how to compute it, target values, and why tracking these twelve gives you a complete picture of program health. We also cover the anti-metrics you should stop measuring.

The 12 Data Governance Metrics That Matter

How to Compute Each Metric

Catalog Coverage — Count production datasets, count cataloged ones, divide. Automated tools like Data Workers publish this continuously.

Policy Coverage — Every dataset should have at least one policy (retention, access, quality). Track the percentage with policies attached.

Incident Count — Any situation where a data consumer caught a problem the platform should have caught. Track quarterly; trend should be down.

MTTR — From detection to resolution. Best-in-class teams under 4 hours; median teams over 24 hours.

Quality SLA Compliance — For each quality rule with an SLA, percentage of checks that passed. Target 99%+.

Audit-Ready Score — Percentage of datasets where you can produce regulatory evidence on demand (lineage, access logs, policies, quality history).

Glossary Adoption — Business metrics and dimensions that have glossary entries. Stale glossaries should be flagged.

Access Review Completion — Did every quarterly access review get completed on time? Stalled reviews are a compliance red flag.

Lineage Coverage — Percentage of datasets with column-level lineage computed and current.

AI Agent Compliance — Every AI agent tool call should produce an audit log entry. Missing entries are a governance failure.

Cost of Non-Compliance — Fines, remediation costs, customer penalties. Should be $0.

Business Value Delivered — Dollar benefits directly traceable to governance (prevented incidents, faster decisions, regulatory savings).

Anti-Metrics: Stop Measuring These

• •Number of policies written (quantity without quality)
• •Committee meetings held (activity without outcome)
• •Slides produced (fortunately not measured at most orgs, but sometimes is)
• •Tool seats purchased (inputs not outcomes)
• •Manual tags added (use auto-classification instead)

How to Publish Data Governance Metrics

Publish monthly. Post the dashboard where executives see it. Show trends over time, not point-in-time snapshots. Red-flag any metric that is below target for two consecutive months. Review them at the monthly governance steering committee.

Data Workers auto-computes every metric above from its catalog, governance, and quality agents. Teams adopting the platform get a metrics dashboard on day one without manual instrumentation. See the governance agent docs for the dashboard layout.

Data governance metrics are the difference between a program you can defend to the board and one you can't. Track the twelve that matter; ignore the vanity metrics. Publish monthly, trend, and act on the red flags. Book a demo to see how Data Workers ships the metrics dashboard out of the box.