Recognises duplicate checks for one rule
Two DQ rules implementing the same data rule on the same columns with different expressions, thresholds or severities, or two rules with the same expression. The report counts the defect twice, and the two results contradict each other.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 35% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 40% | Not shown |
| gpt-6-astra* | Flagship | 80% | Not shown |
| claude-fable-5-1* | Flagship | 41% | Not shown |
| gemini-3.8-flash* | Mid tier | 40% | Not shown |
| gpt-6.1-sol* | Mid tier | 80% | Not shown |
| claude-sonnet-5-5* | Mid tier | 78% | Not shown |
| gemini-3.5-flash-lite | Low cost | 34% | Not shown |
| gpt-6-luna | Low cost | 83% | Not shown |
| claude-haiku-5-5 | Low cost | 80% | Not shown |
* 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: The checks to merge
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: DQ-006 at 100 %, error, and DQ-019 at 95 %, warning, both citing BR-003 on loan_maturity_date; one is red while the other is green Acceptable: One check per rule per population; a second only for a different population or target, saying so in its name (DQR-008)
Also known as: One rule, two checks that disagree (data quality rule)
Related knobits
Builds on
All 30 knobits of Derive data quality rules.
Source: Review checklist 03-dq-rules (data-quality), row 19; DMBOK2; Loshin 2011; formerly data-modelling K-719