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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Smoke Test |  Innovation Lean Startup |  Growth A/B Testing |
|---|---|---|---|---|
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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | Medium | Low | Medium | High |
Timedifferent | Halber Tag pro Domain Slice | 1-5 Tage | Wochen bis Monate je Lernzyklus | 1-4 Wochen |
Participantsdifferent | 1-4 | Nutzertraffic | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Interest Metrics, Conversion Signal, Learning Note | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Experiment results, Decision log, Learning summary |
Tags1 shared | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth | LeanStartupValidationMVP | ExperimentsGrowthAnalyticsValidation |