01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Publishing the evidence behind data-quality claims in catalogs, knowledge graphs, and governed dataset releases.
- Limits
- DQV does not define universal quality metrics or decide fitness for use; projects must define and justify their own measurements and thresholds.
- Best for
- AI / ML and Cross-cutting teams working across Harmonize → Exchange → Learn + reuse.
- Maturity
- ScalingUsable now, but adoption or tooling is still developing. Pilot the exact stack first.
02
See it in the workflow
This view shows the input, the change the standard introduces, and the resulting output.
- InputWhat starts
AI / ML and Cross-cutting source data, metadata, and local mappings
- DQVWhat changes
Use DQV as a pinned quality vocabulary across Harmonize → Exchange → Learn + reuse
- OutputWhat becomes possible
A handoff the next system or team can validate against the same release
03
A concrete example
A governed dataset publishes completeness, validity, subgroup coverage, and drift measurements with metric definitions, thresholds, timestamps, and agents.
Why it matters: Makes quality evidence machine-readable, but cannot turn missing or inadequate measurements into proof of model fitness.
04
What it fits with
Extends dataset metadata such as DCAT; SHACL or domain tests produce validation results that DQV can describe.
- Data model / schemaCroissant
Both support AI / ML and Cross-cutting work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Validation standardSHACL
Both support Cross-cutting and AI / ML work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata vocabularyDPV
Both support AI / ML and Cross-cutting work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - StandardISO/IEC 5259
Both support AI / ML and Cross-cutting work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship
05
Implementation starter
Start with one bounded handoff. Pin, test, and review it before scaling.
Define one handoff, its accountable owner, and the decision DQV must support.
Pin the exact version and companion artifacts: W3C Working Group Note · 2016-12-15.
Map one representative input to the required quality vocabulary artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.
06
Test the main limitation
DQV does not define universal quality metrics or decide fitness for use; projects must define and justify their own measurements and thresholds.
Run one representative end-to-end pilot and record exactly where DQV loses context, needs an extension, or depends on another standard.
Machine-readable output may still be unfit for analysis or ML.
Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Makes quality evidence machine-readable, but cannot turn missing or inadequate measurements into proof of model fitness.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
W3C Data Quality Vocabulary
Official publisher or steward guidance for this quality vocabulary profile.
- Publisher
- W3C