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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Fake Door Test |  Product Discovery Experiment Canvas |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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 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. | 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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 1-5 Tage | 30-60 min | 45-75 min |
Participantsdifferent | 1-4 | Nutzertraffic | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Click Data, Interest Signal, Learning Decision | Completed Experiment Canvas, Success Metric | Test Matrix, Test Plan |
Tags1 shared | Knowledge graphValidationSemantic | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryHypothesis | ExperimentsValidationDiscoveryOptions |