Explains impact-based severity of data quality issues
Severity levels are defined by impact in the issue policy before any issue is rated (a misstated regulatory return or customer payout, a misstated board KPI, an operational inconvenience); each issue records its impact in words and, where possible, numbers (DMBOK2 ch. 13, 2.7.3).
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | 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: Severity levels are defined by impact in the issue policy before any issue is rated (a misstated regulatory return or customer payout, a misstated board KPI, an operational inconvenience); each issue records its impact in words and, where possible, numbers (DMBOK2 ch. 13, 2.7.3).
How it is assessed: Answers a written question on the data quality issue severity correctly
Related knobits
Needed for
- Q-601 Recognises a data quality issue severity set by the reporter
- Q-605 Recognises an issue on a critical data element with no impact stated
- Q-653 Explains resolution targets and escalation of data quality issues
All 9 knobits of Manage data quality issues.
Source: Practice the stage 6 reference and review checklist rest on