Explains data quality goals tied to business drivers
A goal names the business driver it serves (a supervisory finding, a misstated KPI, failed payouts, the cost of rework), a measure that can be checked and a date; 'improve data quality' is not a goal (DMBOK2 ch. 13, 2.2 and 2.6).
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 | 83% | Not shown |
| gemini-3.5-flash-lite | Low cost | 80% | Not shown |
| gpt-6-luna | Low cost | 83% | Not shown |
| claude-haiku-5-5 | Low cost | 83% | 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: A goal names the business driver it serves (a supervisory finding, a misstated KPI, failed payouts, the cost of rework), a measure that can be checked and a date; 'improve data quality' is not a goal (DMBOK2 ch. 13, 2.2 and 2.6).
How it is assessed: Answers a written question on the data quality goal correctly
Related knobits
Needed for
- Q-001 Recognises a data quality goal with no business driver
- Q-005 Recognises a data quality goal with no measure or date
All 12 knobits of Set the data quality strategy and framework.
Source: Practice the stage 0 reference and review checklist rest on