Explains resolution targets and escalation of data quality issues
The policy sets a resolution target per severity level; an open issue past its target is escalated along the route the policy names, up to the data governance council (DMBOK2 ch. 13, 2.7.3).
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 98% | Mastered |
| gemini-3.5-flash-lite | Low cost | 51% | Not shown |
| gpt-6-luna | Low cost | 98% | Mastered |
| claude-haiku-5-5 | Low cost | 99% | Mastered |
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 policy sets a resolution target per severity level; an open issue past its target is escalated along the route the policy names, up to the data governance council (DMBOK2 ch. 13, 2.7.3).
How it is assessed: Answers a written question on the data quality issue escalation correctly
Related knobits
Builds on
Needed for
All 9 knobits of Manage data quality issues.
Source: Practice the stage 6 reference and review checklist rest on