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| Criterion |  Knowledge Modeling Shape-First Modeling |  Knowledge Modeling Competency Questions |  Product Discovery Smoke Test |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 2-4 h | 1-5 Tage | 45-60 min |
Participantsdifferent | 1-4 | 2-8 | Nutzertraffic | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Interest Metrics, Conversion Signal, Learning Note | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | Knowledge graphValidationSemantic | OntologyKnowledge graphScopeRequirementsSemantic | ValidationExperimentsDemandGrowth | AssumptionsRiskExperimentsValidation |