Recognises a check with no rule behind it
A DQ rule with no source_rules and no source_requirements. It enforces a threshold nobody agreed to, and the first argument about it happens when it rejects production data.
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 | 62% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 98% | Mastered |
| gpt-6-astra* | Flagship | 99% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 99% | Mastered |
| gpt-6.1-sol* | Mid tier | 99% | Mastered |
| claude-sonnet-5-5* | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 48% | Not shown |
| gpt-6-luna | Low cost | 50% | Not shown |
| claude-haiku-5-5 | Low cost | 56% | 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 check and the rule or requirement it needs, or its retirement
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: DQ-030: account identifier is 20 characters, no source; the feed also carries non-Estonian IBANs Acceptable: A data rule for the IBAN format, scoped to Estonian IBANs, cited by the check; or the check removed (DQR-006)
Also known as: Check with no rule behind it (data quality rule)
Related knobits
Builds on
All 30 knobits of Derive data quality rules.
Source: Review checklist 03-dq-rules (data-quality), row 7; DMBOK2; Loshin 2011; formerly data-modelling K-707