Explains the data quality issue lifecycle
Issues are logged, assessed, assigned, resolved and closed; duplicates are linked to one master issue; the incident is told from the problem behind it, and a problem is closed only when re-measurement over the affected population shows its cause removed (DMBOK2 ch. 13, 2.7.3; ITIL 4 problem management).
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 | 99% | Not shown |
| gemini-3.5-flash-lite | Low cost | 92% | Not shown |
| gpt-6-luna | Low cost | 92% | Not shown |
| claude-haiku-5-5 | Low cost | 92% | 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: Issues are logged, assessed, assigned, resolved and closed; duplicates are linked to one master issue; the incident is told from the problem behind it, and a problem is closed only when re-measurement over the affected population shows its cause removed (DMBOK2 ch. 13, 2.7.3; ITIL 4 problem management).
How it is assessed: Answers a written question on the data quality issue lifecycle correctly
Related knobits
Needed for
- Q-603 Recognises a data quality issue closed with no verification
- Q-604 Recognises duplicate data quality issues left unlinked
All 9 knobits of Manage data quality issues.
Source: Practice the stage 6 reference and review checklist rest on