Recognises data quality accountability given to the DQ team
The DQ team made accountable for data it does not own.
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 | 65% | Not shown |
| gemini-3.5-flash-lite | Low cost | 65% | Not shown |
| gpt-6-luna | Low cost | 65% | Not shown |
| claude-haiku-5-5 | Low cost | 65% | 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: Data owners accountable, the DQ team measuring and supporting, and the escalation route
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "DQ team is accountable for the quality of all data" Acceptable: Data owners accountable; DQ team measures and supports; escalation to the governance council
Also known as: Accountability given to the DQ team
Related knobits
Builds on
All 12 knobits of Set the data quality strategy and framework.
Source: Review checklist 00-dq-strategy, row 3 (DMBOK §5; BCBS 239 P1)