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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Force Field Analysis |  Product Discovery Assumption Mapping |  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. | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | 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. |
Complexitydifferent | Medium | Low | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 45-60 min | 1-2 h |
Participantsdifferent | 1-4 | 3-12 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Force Field Map, Change Levers, Risk Notes | Assumption map, Test backlog, Risk ranking | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Knowledge graphValidationSemantic | ChangeDecisionStrategy | AssumptionsRiskExperimentsValidation | StrategyDecisionAssumptionsHypothesis |