Explains accountability in a data quality programme
Data owners are accountable for the quality of their data; the DQ team measures, analyses and supports; an escalation route leads to the data governance council (DMBOK2 ch. 13, 5; BCBS 239 Principle 1).
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 | 82% | Not shown |
| gemini-3.5-flash-lite | Low cost | 82% | Not shown |
| gpt-6-luna | Low cost | 82% | Not shown |
| claude-haiku-5-5 | Low cost | 82% | 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 are accountable for the quality of their data; the DQ team measures, analyses and supports; an escalation route leads to the data governance council (DMBOK2 ch. 13, 5; BCBS 239 Principle 1).
How it is assessed: Answers a written question on the data quality accountability correctly
Related knobits
Needed for
All 12 knobits of Set the data quality strategy and framework.
Source: Practice the stage 0 reference and review checklist rest on