Measure whether consumers can find trusted data faster, whether quality issues are routed to a named owner and whether deprecated assets are being retired on schedule. Those signals show whether governance is changing behaviour, not just producing documentation.
Why This Matters for Security Teams
Data-as-a-product only works when governance changes how people use data, not when it simply adds another catalog entry. Teams need to know whether consumers can discover trusted datasets quickly, whether defects move to a named owner, and whether stale assets are actually being retired. Those are operational signals, not vanity metrics, and they line up with the broader discipline of measuring trust, access, and lifecycle control in the NIST Cybersecurity Framework 2.0.
That distinction matters because data teams often over-index on documentation completeness while missing adoption and accountability. NHI Management Group research shows that only 5.7% of organisations have full visibility into their service accounts, a useful reminder that asset visibility gaps are usually a governance problem before they are a tooling problem. The same pattern appears in data platforms when owners are unclear, lineage is partial, and retirement is informal. The Ultimate Guide to NHIs — Key Research and Survey Results shows how quickly operational blind spots become security and reliability issues.
In practice, many security teams encounter governance failure only after a stale dataset, broken owner mapping, or unresolved quality issue has already affected downstream decisions.
How It Works in Practice
Measurement should start with three questions: can consumers find the right data, can they trust it enough to use it, and does the organisation retire what no longer has a business purpose? If the answer to any of those is weak, the product is not functioning as a product. Current guidance suggests combining usage telemetry, quality signals, stewardship response times, and lifecycle milestones into a single operating view rather than tracking each in isolation.
A practical approach is to measure both demand-side and control-side outcomes. Demand-side metrics show whether the product is useful. Control-side metrics show whether governance is enforceable. Examples include time to discover a dataset, percentage of active consumers using certified assets, mean time to assign an issue owner, percentage of issues resolved within the SLA, and percentage of deprecated assets removed on schedule. For lifecycle control, align retirement metrics with the way asset owners actually decommission datasets, APIs, and pipelines. The Ultimate Guide to NHIs — The NHI Market is useful here because it reinforces the importance of ownership, visibility, and control boundaries across machine-operated assets.
- Measure discovery: search-to-open time, successful queries, and repeat usage of certified data product.
- Measure trust: data quality pass rates, freshness SLAs, and the percentage of incidents routed to a named owner.
- Measure retirement: deprecated asset inventory, percentage removed on schedule, and exceptions approved with an expiry date.
For governance maturity, map these metrics to NIST Cybersecurity Framework 2.0 functions and treat them as operational evidence, not reporting decoration. These controls tend to break down when ownership spans many domains and the platform cannot reliably link datasets, consumers, and retirement obligations.
Common Variations and Edge Cases
Tighter measurement often increases reporting overhead, requiring organisations to balance governance precision against analyst and engineering effort. That tradeoff is real, especially in federated data meshes, regulated environments, and teams with many semi-autonomous product owners. Best practice is evolving, and there is no universal standard for exactly which metrics every data product must carry.
In highly distributed environments, a single dashboard can hide the fact that one domain has excellent discovery performance while another has no cleanup discipline at all. In those cases, compare metrics by domain and product class rather than averaging everything together. For example, internal operational datasets may tolerate slower discovery but require stricter retirement controls, while customer-facing analytics products need stronger trust signals and clearer SLAs. The important point is consistency of measurement logic, not identical targets everywhere.
Teams should also avoid overcounting documentation proxies such as number of catalog entries, number of tags, or number of policies published. Those are leading indicators at best. The stronger signal is whether behaviour changes: fewer orphaned assets, faster issue routing, and higher reuse of trusted products. That is the difference between a data program that looks mature and one that actually operates maturely.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC-01 | Business context and product outcomes anchor what data teams should measure. |
| NIST CSF 2.0 | ID.AM-01 | Asset inventory and lifecycle visibility are core to retired-data measurement. |
| OWASP Non-Human Identity Top 10 | NHI-05 | Lifecycle and ownership discipline for machine-operated assets parallels data-product governance. |
Use ownership, visibility, and retirement metrics to prove governance is operational.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
