Explains data quality dimensions
Completeness, uniqueness, validity, accuracy, consistency, timeliness and integrity, and what each measures.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview* | Flagship | 61% | Not shown |
| gpt-6-astra* | Flagship | 87% | Not shown |
| claude-fable-5-1* | Flagship | 60% | Not shown |
| gemini-3.8-flash* | Mid tier | 61% | Not shown |
| gpt-6.1-sol* | Mid tier | 86% | Not shown |
| claude-sonnet-5-5* | Mid tier | 85% | Not shown |
| gemini-3.5-flash-lite | Low cost | 100% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
* From the earlier benchmark of 2026-10: 10 data-modelling test tasks, 3 runs per task, graded by claude-opus-5-5. These models have not taken the current 123 tasks yet; the two methods differ, so compare their figures with care. They are left out of the “most models” counts and the best-model picks.
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Completeness, uniqueness, validity, accuracy, consistency, timeliness and integrity, and what each measures
How it is assessed: Assigns sample checks to the dimension they measure
Related knobits
Needed for
- Q-318 Recognises a misfiled data quality dimension
- Q-451 Explains the ISO/IEC 25012 data quality characteristics
All 30 knobits of Derive data quality rules.
Source: Data-management practice used by the stage 3 reference and review checklist; formerly data-modelling K-751