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| Criterion |  Innovation Lean Startup |  Knowledge Modeling Shape-First Modeling |  Product Discovery Pretotyping |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
Purposedifferent | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | 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. | 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 | Medium | Low | Medium |
Timedifferent | Wochen bis Monate je Lernzyklus | Halber Tag pro Domain Slice | Stunden bis wenige Tage | 2-4 h |
Participantsdifferent | 2-8 | 1-4 | 1-4 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | LeanStartupValidationMVP | Knowledge graphValidationSemantic | ValidationDemandMVP | OntologyKnowledge graphScopeRequirementsSemantic |