Explains data quality as fitness for purpose
Data are of high quality to the degree that they meet the expectations and needs of their consumers; quality is relative to use, and the cost of poor quality (scrap, rework, lost opportunity) is the case for managing it.
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 | 58% | Not shown |
| gemini-3.5-flash-lite | Low cost | 49% | Not shown |
| gpt-6-luna | Low cost | 58% | Not shown |
| claude-haiku-5-5 | Low cost | 58% | 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: Data are of high quality to the degree that they meet the expectations and needs of their consumers; quality is relative to use, and the cost of poor quality (scrap, rework, lost opportunity) is the case for managing it.
How it is assessed: Answers a CDMP-style multiple-choice question on the data quality
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-3