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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Strategy Lean Canvas |  Product Discovery Smoke Test |  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 an early product bet still has too many open points, it brings target group, problem, and assumptions into a single view. It condenses the idea so core risks become nameable. | 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. | 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 | Medium | Low | High |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-4 | 1-6 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Lean Canvas, Core Assumptions, Experiment Backlog | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Knowledge graphValidationSemantic | StartupLeanAssumptionsBusiness model | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |