Explains centralised, replicated and federated operating models
Centralised, replicated and federated (hybrid) models and when each fits.
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 | 74% | Not shown |
| gemini-3.5-flash-lite | Low cost | 45% | 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: Centralised, replicated and federated (hybrid) models and when each fits.
How it is assessed: Answers a CDMP-style multiple-choice question on the data governance operating model types
All knobits of Data governance.
Source: DAMA-DMBOK2 chapter 3, element GV-5