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| Criterion |  Knowledge Modeling Shape-First Modeling |  Architecture Risk Storming |  Product Discovery Smoke 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. | Helps clarify architecture context, options, and consequences in concrete terms. It organizes technical context, alternatives, and consequences. The result is captured as an Annotated Diagram, a Prioritized Risk List, and an Action Backlog. | 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 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. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 60-90 min | 1-5 Tage | 1-5 Tage |
Participantsdifferent | 1-4 | 4-12 | Nutzertraffic | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Annotated Diagram, Prioritized Risk List, Action Backlog | Interest Metrics, Conversion Signal, Learning Note | Click Data, Interest Signal, Learning Decision |
Tags1 shared | Knowledge graphValidationSemantic | RiskArchitectureWorkshopValidation | ValidationExperimentsDemandGrowth | ValidationExperimentsDemandDiscovery |