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| Criterion |  Knowledge Modeling Shape-First Modeling |  Knowledge Modeling Competency Questions |  Product Strategy ICE Scoring |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | When ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | Medium | Medium | Low | High |
Timedifferent | Halber Tag pro Domain Slice | 2-4 h | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-4 | 2-8 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | ICE Table, Top Idea List | Experiment results, Decision log, Learning summary |
Tagsno overlap | Knowledge graphValidationSemantic | OntologyKnowledge graphScopeRequirementsSemantic | PrioritizationScoringGrowthDecision | ExperimentsGrowthAnalyticsValidation |