# Metrics for data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition →: lineage, access, and stewardship health
A state labor agency's data catalogdata catalogA centralized inventory of an organization's data assets, enriched with metadata, that helps people find, understand, and trust the data they need.View full definition → lists 340 datasets. Ask who owns "unemployment claims 2023" and the answer is a Slack thread from 2021. Ask how long it takes to get access to that dataset for a fraud audit and the honest answer is "six to eight weeks, sometimes." That agency has a data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → policy. It does not have data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition →. This lesson shows you how to tell the difference using three measurable signals: lineage, stewardship, and access turnaround.
Data governanceData governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → is the set of rules, roles, and processes that decide who can access, change, and be accountable for data. Most public sector agencies have a governance charter. Far fewer can produce numbers proving it operates day to day.
This gap matters because public sector data feeds decisions with legal weight: benefit eligibility, tax assessments, policing, child welfare. Bad or unaccountable data does not just cost money, it produces wrongful denials and audit failures. The US Government Accountability Office (GAO) has repeatedly flagged federal agencies for incomplete data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition →, including inconsistent data inventories under the Federal Data Strategy and OMB's Evidence Act.
Three metrics let you audit governance like an outsider: lineage coverage, stewardship assignment rate, and access-request turnaround time.
Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.View full definition → is the documented trail showing where a data element originated, what transformed it, and where it flows downstream, from source system to report to dashboard.
Why it matters in the public sector: when a state Medicaid agency reports an eligibility error rate, auditors need to trace that number back through every join, filter, and recode to the original claims record. Without lineage, you cannot answer "why does this number look wrong" or "what breaks if we retire this legacy mainframe field."
How to measure it:
Mature private-sector data organizations often target 80 to 90% lineage coverage for tier-1 (regulatory or decision-critical) datasets. Public sector agencies frequently sit far below that; GAO and inspector general reports across federal agencies commonly cite lineage and metadata gaps as a recurring audit finding (estimate, based on repeated GAO IT management reports, exact percentages not centrally published).
Worked example:
An agency has 50 datasets flagged as "critical" (used in official statistics or benefits decisions). A catalog audit finds 14 have full lineage documented in the tool (e.g., Collibra, Alation, or an open-source option like OpenMetadata).
Lineage coverage = 14 / 50 = 28%.
That is a governance program that exists on paper but not in practice. A credible target for year one remediation is 60% coverage of tier-1 datasets.
A data stewarddata stewardA business-side owner responsible for the quality, consistency and appropriate use of data in their domain.View full definition → is a named individual (not a team, not a job title, an actual person) accountable for a dataset's definition, quality rules, and access decisions. Data stewardship health measures whether stewardship is real accountability or a checkbox in a RACI chart (Responsible, Accountable, Consulted, Informed, a common governance role matrix).
Key metrics:
A common failure pattern: an agency assigns stewards during a governance rollout, then never updates the roster. Eighteen months later, 40% of listed stewards have left the agency. The catalog says "owned." Reality says "abandoned."
Benchmark to apply: if your orphan rate exceeds 10% of critical datasets in any quarter, stewardship is decorative, not functional. This threshold is a practical rule of thumb from data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → practice, not a regulatory standard, treat it as a working benchmark rather than a cited figure.
This is the metric that most directly affects service delivery and public trust. Access-request turnaround time measures how long it takes from a legitimate request for data access to actual, provisioned access.
This matters enormously for the public sector because of layered legal regimes:
Slow turnaround is not just inefficiency, it is a governance signal: it usually means access approval workflows are manual, undocumented, or routed through people who no longer understand the system.
How to measure it:
Worked example:
An agency logs 120 access requests for restricted datasets last quarter. Sorted turnaround times in days: the median (60th value when ranked) is 34 days. Their published SLA target is 10 business days.
Compliance rate = requests resolved within SLA / total requests. If only 22 of 120 met the 10-day target, that is an 18% SLA compliance rate, a strong signal the access process is broken regardless of what the policy document promises.
Knowledge check
1. A state agency has a written data governance charter approved by leadership. What does the lesson argue is missing if the agency cannot produce measurable numbers on lineage, stewardship, and access turnaround?
2. An auditor questions why a reported Medicaid eligibility error rate seems off. Which metric most directly enables tracing that number back through every transformation to its source record?
3. Why does the lesson treat 'six to eight weeks, sometimes' as evidence of a governance problem rather than just an operational inconvenience?
4. Select ALL correct answers about why public sector data governance carries higher stakes than in many private sector contexts.
Select all the correct answers.
5. Select ALL correct answers about what makes lineage coverage, stewardship assignment rate, and access turnaround useful as governance metrics.
Select all the correct answers.
A useful audit does not treat these three metrics separately. Combine them into a simple scorecard reviewed quarterly:
| Metric | Formula | Illustrative target (estimate, practice-based) |
|---|---|---|
| Lineage coverage (tier-1) | documented lineage datasets / tier-1 datasets | ≥ 70% |
| Stewardship assignment | named current stewards / critical datasets | ≥ 90% |
| Orphaned dataset rate | orphaned datasets / critical datasets | ≤ 5% |
| Access SLA compliance | requests within SLA / total requests | ≥ 85% |
These targets are not from a single regulation, they reflect common data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → maturity benchmarks used across public and private sector practice. Treat them as a starting point for internal target-setting, not a compliance requirement.
A simple way to compute the scorecard programmatically from a catalog export:
import pandas as pd
df = pd.read_csv("catalog_export.csv")
tier1 = df[df.tier == "critical"]
lineage_coverage = (tier1.has_lineage.sum() / len(tier1)) * 100
steward_rate = (tier1.has_current_steward.sum() / len(tier1)) * 100
orphan_rate = (tier1.steward_orphaned.sum() / len(tier1)) * 100
print(f"Lineage coverage: {lineage_coverage:.1f}%")
print(f"Stewardship assignment: {steward_rate:.1f}%")
print(f"Orphan rate: {orphan_rate:.1f}%")This kind of script, run quarterly against the same catalog fields, turns governance from a narrative ("we take data seriously") into a trend line leadership can act on.
For a deeper reference model, the DAMA-DMBOK (Data Management Body of Knowledge) is the standard practitioner framework covering stewardship, lineage, and metadata management in detail.