Data Governance Metrics: The 12 KPIs That Actually Matter
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
| Metric | Target | Formula |
|---|---|---|
| Catalog Coverage | ≥90% | cataloged datasets / total datasets |
| Policy Coverage | ≥95% | datasets with policy / total datasets |
| Incident Count | Trend down | Quarterly data incidents |
| MTTR | <4 hours | Mean time to resolution per incident |
| Quality SLA Compliance | ≥99% | passing SLA checks / total checks |
| Audit-Ready Score | 100% | % of datasets with audit evidence |
| Glossary Adoption | ≥80% | metrics with glossary entry / total |
| Access Review Completion | 100% | quarterly reviews completed |
| Lineage Coverage | ≥95% | datasets with lineage / total |
| AI Agent Compliance | 100% | agent calls with audit log / total |
| Cost of Non-Compliance | $0 | fines + remediation costs |
| Business Value Delivered | Positive | $ benefits traced to governance |
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.
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Explore Topic Clusters
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- Data Lineage: The Complete Guide — Column-level lineage, impact analysis, and observability.
- Data Quality: The Complete Guide — Tests, SLAs, anomaly detection, and data reliability engineering.
- AI Data Engineering: The Complete Guide — LLMs, agents, and autonomous workflows across the data stack.
- MCP for Data: The Complete Guide — Model Context Protocol servers, tools, and agent integration.
- Data Mesh & Data Fabric: The Complete Guide — Federated ownership, domain-oriented architecture, and interop.
- Open-Source Data Stack: The Complete Guide — dbt, Airflow, Iceberg, DuckDB, and the modern OSS toolkit.