Explains traceability from data rule to check

Q-352 · Knowledge knobit · Activity: Derive data quality rules · Competence: Data quality

Every check cites the rule or requirement it implements, and the check follows the rule's lifecycle.

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.

* 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: Every check cites the rule or requirement it implements, and the check follows the rule's lifecycle

How it is assessed: Traces sample checks to their rules and finds the untraced ones

Related knobits

Needed for

• Q-307 Recognises a check with no rule behind it
• Q-308 Recognises a check outliving its rule
• Q-310 Recognises a check on the wrong object
• Q-311 Recognises a check weaker than its rule
• Q-312 Recognises a check stronger than its rule
• Q-319 Recognises duplicate checks for one rule

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-752