Explains ownership of data quality failures
A failure is owned by someone able to fix its cause.
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 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 | 62% | Not shown |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 100% | Mastered |
| gpt-6.1-sol* | Mid tier | 100% | Mastered |
| claude-sonnet-5-5* | Mid tier | 63% | Not shown |
| gemini-3.5-flash-lite | Low cost | 92% | Not shown |
| gpt-6-luna | Low cost | 92% | Not shown |
| 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: A failure is owned by someone able to fix its cause
How it is assessed: Names a capable owner for sample failures
Related knobits
Needed for
- Q-323 Recognises a check failure with no capable owner
- Q-602 Recognises a data quality issue owner unable to fix its cause
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-758