Explains the drivers of reference and master data management
Meeting organisational data requirements, managing data quality, managing the cost of integration, reducing risk.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 75% | Not shown |
| gemini-3.5-flash-lite | Low cost | 75% | Not shown |
| gpt-6-luna | Low cost | 75% | Not shown |
| claude-haiku-5-5 | Low cost | 75% | 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: Meeting organisational data requirements, managing data quality, managing the cost of integration, reducing risk.
How it is assessed: Answers a CDMP-style multiple-choice question on the drivers of MDM
All knobits of Reference and master data.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 10: Reference and Master Data, element RV-6