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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Strategy DIBB |  Knowledge Modeling Competency Questions |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | 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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Low | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 1-2 h | 2-4 h | 30-60 min |
Participantsdifferent | 1-4 | 2-8 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | DIBB document, Belief list, Bet list, Learning report | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Knowledge graphValidationSemantic | StrategyDecisionAssumptionsHypothesis | OntologyKnowledge graphScopeRequirementsSemantic | ExperimentsValidationDiscoveryHypothesis |