Data modelling
Data modelling is learned and done in 8 activities, made of 307 knobits.
Learning path
- Capture data requirements · 47 knobits
- Define business terms · 20 knobits · builds on Capture data requirements
- Document data rules as SBVR fact types and rule statements · 28 knobits · builds on Define business terms
- Build the conceptual model and taxonomies · 31 knobits · builds on Document data rules as SBVR fact types and rule statements
- Derive the logical data model · 56 knobits · builds on Build the conceptual model and taxonomies
- Design the physical data model · 40 knobits · builds on Derive the logical data model
- Derive data quality rules · 31 knobits · builds on Design the physical data model, Document data rules as SBVR fact types and rule statements
- Generate and review DDL · 33 knobits · builds on Derive data quality rules, Design the physical data model
Foundation knowledge
21 knobits used across the activities rather than in one of them.
- K-901 Explains what data models are and why organisations build them
- K-902 Explains the types of modelled data
- K-903 Explains data modelling notations
- K-904 Explains identifying and non-identifying relationships
- K-905 Explains dimensional modelling
- K-906 Explains UML class diagrams as a data modelling scheme
- K-907 Explains fact-based modelling
- K-908 Explains time-based modelling schemes
- K-909 Explains NoSQL data modelling schemes
- K-910 Explains canonical data models
- K-911 Explains views in physical design
- K-912 Explains the planning of data modelling work
- K-913 Explains how to choose a modelling scheme and notation
- K-914 Explains forward and reverse engineering of data models
- K-915 Explains data model review and the Data Model Scorecard
- K-916 Explains data model versioning and change management
- K-917 Explains the tools supporting data modelling
- K-918 Explains data model patterns and industry data models
- K-919 Explains naming conventions for data model objects
- K-920 Explains database design principles
- K-921 Explains data modelling and design standards
Which AI model is best at data modelling?
Best result for data modelling: gemini-4-argon (89% test score).
| Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | All |
|---|---|---|---|---|---|---|---|---|---|
| gemini-4-argon | 88% | 88% | 97% | 100% | 95% | 98% | 95% | 82% | 93% |
| gemini-3.1-pro-preview | 52% | 81% | 86% | 100% | 94% | 84% | 86% | 61% | 80% |
| gpt-6-astra | 78% | 94% | 94% | 87% | 91% | 97% | 98% | 81% | 90% |
| claude-fable-5-1 | 78% | 99% | 98% | 100% | 94% | 98% | 95% | 73% | 92% |
| gemini-3.8-flash | 67% | 94% | 93% | 100% | 94% | 92% | 94% | 66% | 88% |
| gpt-6.1-sol | 73% | 94% | 94% | 88% | 89% | 92% | 98% | 78% | 88% |
| claude-sonnet-5-5 | 75% | 98% | 100% | 94% | 95% | 94% | 92% | 76% | 91% |
| gemini-3.5-flash-lite | 53% | 68% | 53% | 92% | 66% | 68% | 66% | 56% | 65% |
| gpt-6-luna | 59% | 73% | 87% | 100% | 92% | 90% | 89% | 64% | 82% |
| claude-haiku-5-5 | 70% | 82% | 93% | 100% | 94% | 92% | 95% | 64% | 86% |
Expected share of each activity's knobits the model masters; columns follow the learning path above. Method and caveats.