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| Criterion |  Knowledge Modeling Competency Questions |  Product Discovery Assumption Mapping |  Product Strategy DIBB |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | 2-4 h | 45-60 min | 1-2 h | Halber Tag pro Domain Slice |
Participantsdifferent | 2-8 | 2-8 | 2-8 | 1-4 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Assumption map, Test backlog, Risk ranking | DIBB document, Belief list, Bet list, Learning report | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | OntologyKnowledge graphScopeRequirementsSemantic | AssumptionsRiskExperimentsValidation | StrategyDecisionAssumptionsHypothesis | Knowledge graphValidationSemantic |