Explains the business drivers of data quality management
Poor data costs money and trust: rework, failed payments and payouts, misstated regulatory returns, lost customers and risk decisions taken on wrong data; regulation and the value of data as an asset drive a formal programme.
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: Poor data costs money and trust: rework, failed payments and payouts, misstated regulatory returns, lost customers and risk decisions taken on wrong data; regulation and the value of data as an asset drive a formal programme.
How it is assessed: Answers a CDMP-style multiple-choice question on the drivers of data quality management
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-1