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| Criterion |  Product Discovery Smoke Test |  Knowledge Modeling Competency Questions |  Growth A/B Testing |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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. | 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. |
Complexitydifferent | Low | Medium | High | Medium |
Timedifferent | 1-5 Tage | 2-4 h | 1-4 Wochen | Halber Tag pro Domain Slice |
Participantsdifferent | Nutzertraffic | 2-8 | 1-6 | 1-4 |
Formatdifferent | Async | Workshop + async | Async | Workshop + async |
Outputdifferent | Interest Metrics, Conversion Signal, Learning Note | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Experiment results, Decision log, Learning summary | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | ValidationExperimentsDemandGrowth | OntologyKnowledge graphScopeRequirementsSemantic | ExperimentsGrowthAnalyticsValidation | Knowledge graphValidationSemantic |