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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |
|---|---|---|---|
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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. |
Complexitydifferent | Medium | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 60-90 min | 1-2 h |
Participantsdifferent | 1-4 | 3-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Knowledge graphValidationSemantic | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |
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