Lexicon · Ontology as economic infrastructure
Data quality
Fully specified name: Data quality (concept)
The fitness of data for a use, assessed against explicit criteria.
- Scope note
- Completeness, consistency, and accuracy answer different questions.
- Synonyms
- None recorded as true synonyms in this context.
- Monograph
- Semantic interoperability (planned; brief only)
- External mappings
- None recorded. In W0 no mapping to SNOMED CT® or any external authority is asserted for this entry. A future mapping record will carry source system, identifier, version, relation type, evidence, author, confidence where meaningful, and review state.
- Formal status
- Editorial entry only. It is not part of any released ontology and a prose edit here changes no formal definition.
Draft. This entry is an unreviewed draft. Its sources have not been checked by a named person and no domain reviewer has approved it. Treat every claim as provisional.
Notes
Data quality is not a single property. It is a judgment about whether data are fit for a particular use, made against criteria that have to be named. The three most cited dimensions ask different questions. Completeness asks whether the values that should be present are present: is the administered activity recorded for every therapy? Consistency asks whether values agree with each other and with their constraints: does the recorded activity fall within the product's range, and does the administration date precede the follow-up scan? Accuracy asks whether a value matches the truth it claims to represent, which usually requires comparison with an independent source. Data can be complete and consistent and wrong; they can be accurate and sparse.
The scope note warns against collapsing these into one score. A dashboard that reports a single percentage has hidden which dimension was measured and against which criteria. For an informatician the dimensions map onto different checks; for a clinician they map onto different risks.
In this section data quality appears as a cost driver: poor quality generates reconciliation work, and a controlled vocabulary is one of several ways to reduce it. Any claim about how much time poor data quality consumes must come with its numerator, denominator, setting, and method. The related entries on Provenance and Uncertainty cover what must accompany a value for its quality to be assessable at all.