Explains the readiness and risk assessment for a data quality programme
Management commitment, knowledge of the current state of the data, stakeholder analysis, accountability for data, existing measurement, and the business risks of poor data the programme is to reduce.
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 | 67% | Not shown |
| gemini-3.5-flash-lite | Low cost | 47% | Not shown |
| gpt-6-luna | Low cost | 47% | Not shown |
| claude-haiku-5-5 | Low cost | 47% | 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: Management commitment, knowledge of the current state of the data, stakeholder analysis, accountability for data, existing measurement, and the business risks of poor data the programme is to reduce.
How it is assessed: Answers a CDMP-style multiple-choice question on the data quality readiness assessment
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-30