Explains data quality dimensions
Completeness, uniqueness, validity, accuracy, consistency, timeliness and integrity, and what each measures.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 59% | Not shown |
| 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 | 58% | Not shown |
| gpt-6-luna | Low cost | 86% | Not shown |
| claude-haiku-5-5 | Low cost | 62% | Not shown |
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
All 31 knobits of Derive data quality rules.
Source: Data-management practice used by the stage 7 reference and review checklist