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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Pretotyping |  Product Discovery Assumption Mapping |  Product Discovery Smoke 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. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Medium | Low | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | Stunden bis wenige Tage | 45-60 min | 1-5 Tage |
Participantsdifferent | 1-4 | 1-4 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Assumption map, Test backlog, Risk ranking | Interest Metrics, Conversion Signal, Learning Note |
Tags1 shared | Knowledge graphValidationSemantic | ValidationDemandMVP | AssumptionsRiskExperimentsValidation | ValidationExperimentsDemandGrowth |