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| Criterion |  Knowledge Modeling Shape-First Modeling |  Innovation Lean Startup |  Product Discovery Riskiest Assumption Test |  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. | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Halber Tag pro Domain Slice | Wochen bis Monate je Lernzyklus | 1-2 Wochen pro Iteration | 1-5 Tage |
Participantsdifferent | 1-4 | 2-8 | 2-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Prioritized Assumption List, Test Plan, Results Report | Click Data, Interest Signal, Learning Decision |
Tags1 shared | Knowledge graphValidationSemantic | LeanStartupValidationMVP | ExperimentsValidationDiscoveryAssumptions | ValidationExperimentsDemandDiscovery |