Beyond accuracy¶
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Quality Control
- Data quality: Data-validation pipelines (completeness, accuracy, consistency checks), schema compliance, duplicate detection, outlier flagging, reference-data validation, structured around the multi-dimensional data-quality framework Wang and Strong (1996) developed from a consumer perspective.
This sourceFoundational data-quality framework organizing 15 dimensions across intrinsic, contextual, representational, and accessibility categories; the canonical reference for data-validation pipeline design.
- Data quality: Data-validation pipelines (completeness, accuracy, consistency checks), schema compliance, duplicate detection, outlier flagging, reference-data validation, structured around the multi-dimensional data-quality framework Wang and Strong (1996) developed from a consumer perspective.
Domain-specific¶
- Phantom Inventory
- The deeper structural pattern phantom inventory instantiates — an information-layer record of some physical or real-world state diverges from that state, in the direction that overstates, and propagates into decisions that consume the record as authoritative, because no forcing function reconciles the two between audits — is genuinely substrate-neutral, and it recurs as real co-instances (case B), not mere resemblances: software registries with broken pointers or stale lockfiles serving the wrong artefact, surveillance databases with stale records, geographic information systems with outdated maps, public-health case registries with delayed-reporting bias, financial books carrying unreconciled positions, and government registries (voter rolls, property and vehicle records) with stale entries
This sourceStudy arguing that data-quality improvement efforts focus too narrowly on accuracy and developing a hierarchical framework of data-quality dimensions beyond it.
Supported in partVerified against a saved copy of the source
“Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy.”
- The deeper structural pattern phantom inventory instantiates — an information-layer record of some physical or real-world state diverges from that state, in the direction that overstates, and propagates into decisions that consume the record as authoritative, because no forcing function reconciles the two between audits — is genuinely substrate-neutral, and it recurs as real co-instances (case B), not mere resemblances: software registries with broken pointers or stale lockfiles serving the wrong artefact, surveillance databases with stale records, geographic information systems with outdated maps, public-health case registries with delayed-reporting bias, financial books carrying unreconciled positions, and government registries (voter rolls, property and vehicle records) with stale entries
Verification¶
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