Explains MDM implementation guidelines
Adhering to the master data architecture, monitoring data movement, managing reference data change, readiness and change.
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 90% | Mastered |
| gemini-3.5-flash-lite | Low cost | 90% | Not shown |
| gpt-6-luna | Low cost | 90% | Mastered |
| claude-haiku-5-5 | Low cost | 89% | 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: Adhering to the master data architecture, monitoring data movement, managing reference data change, readiness and change.
How it is assessed: Answers a CDMP-style multiple-choice question on the MDM implementation
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-11