DQV
- Stage boundary
- No direct role is recorded for Plan, Acquire.
- Known limitation
- DQV does not define universal quality metrics or decide fitness for use; projects must define and justify their own measurements and thresholds.
Decision support
Place up to three profiles side by side. Focus on architectural role, evidence, and the first limitation to test—not on finding a single all-purpose standard.
Working set
1 of 3 selected
Decision lens
The useful question is not “Which standard wins?” It is “Which job must this part of the architecture perform, and what remains uncovered?”
Decide whether you need guidance, a domain payload, exchange, semantics, governance, or a reusable release.
Use the matrix to see where each profile has a direct role. A filled cell is coverage, not a quality score.
Read what each option leaves unresolved before judging maturity, confidence, or implementation fit.
Three-part assessment
Read left to right. Lifecycle reach comes first; maturity remains an editorial roll-up, not certification.
01 · Lifecycle reach
Coverage shows a recorded role at that readiness stage. It does not imply end-to-end implementation.
| Profile | Plan | Acquire | Harmonize | Exchange | Learn + reuse |
|---|---|---|---|---|---|
| DQVQuality vocabulary | W3C Data Quality Vocabulary has no direct role recorded in Plan. | W3C Data Quality Vocabulary has no direct role recorded in Acquire. | W3C Data Quality Vocabulary has a direct role in Harmonize. | W3C Data Quality Vocabulary has a direct role in Exchange. | W3C Data Quality Vocabulary has a direct role in Learn + reuse. |
02 · Boundaries
These are design boundaries, not faults. Use them to identify the companion layers your architecture still needs.
03 · Detailed assessment
Use the source, status, and limitation together. A higher maturity label does not erase a scope mismatch.
| Assessment | DQVW3C Data Quality Vocabulary |
|---|---|
| Purpose & coverage | RDF terms for quality dimensions, metrics, measurements, policies, certificates, and annotations. Best fitPublishing the evidence behind data-quality claims in catalogs, knowledge graphs, and governed dataset releases. |
| Readiness stages | HarmonizeExchangeLearn + reuse |
| AI-ready contribution | Makes quality evidence machine-readable, but cannot turn missing or inadequate measurements into proof of model fitness. |
| First limitation to test | DQV does not define universal quality metrics or decide fitness for use; projects must define and justify their own measurements and thresholds. |
| Evidence | E1 + E4 High confidence Formal statusW3C Working Group Note ReviewSource-checked |
| Maturity | Scaling Stable vocabulary note; adoption evidence varies |
| Sources & links | W3C Data Quality Vocabulary (opens in a new tab)Read full profile |