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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Force Field Analysis |  Product Discovery Experiment Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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 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. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-4 | 3-12 | 1-5 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Force Field Map, Change Levers, Risk Notes | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | Knowledge graphValidationSemantic | ChangeDecisionStrategy | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |