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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |  Product Strategy DIBB |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | 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. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 30-60 min | 1-2 h | 45-60 min |
Participantsdifferent | 1-4 | 1-5 | 2-8 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation |