Explains master data acquisition, validation, standardisation and enrichment
Acquiring source data, validating and standardising it, enriching it from external sources before matching.
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 | 83% | Not shown |
| gemini-3.5-flash-lite | Low cost | 36% | Not shown |
| gpt-6-luna | Low cost | 36% | Not shown |
| claude-haiku-5-5 | Low cost | 36% | 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: Acquiring source data, validating and standardising it, enriching it from external sources before matching.
How it is assessed: Answers a CDMP-style multiple-choice question on the MDM processing steps
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-8