Scores a data model against the Data Model Scorecard
A score per scorecard category with evidence, a total against the threshold and a decision that follows it
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
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: A score per scorecard category with evidence, a total against the threshold and a decision that follows it
How it is assessed: Scores a retail payments logical model in all ten categories with evidence for each (definitions checked, validator errors cited), totals it, and approves or rejects by the agreed threshold
Related knobits
Builds on
- K-915 Explains data model review and the Data Model Scorecard
- K-920 Explains database design principles
- K-901 Explains what data models are and why organisations build them
All 8 knobits of Review the data model against the scorecard.
Source: DAMA-DMBOK2 chapter 5, element(s) DM-29, DM-40, DM-42: a performance at CDMP Practitioner level