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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Smoke Test |  Product Discovery Pretotyping |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
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. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | 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. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 1-5 Tage | Stunden bis wenige Tage | 2-4 h |
Participantsdifferent | 1-4 | Nutzertraffic | 1-4 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Interest Metrics, Conversion Signal, Learning Note | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | Knowledge graphValidationSemantic | ValidationExperimentsDemandGrowth | ValidationDemandMVP | OntologyKnowledge graphScopeRequirementsSemantic |