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| Criterion |  Innovation Lean Startup |  Product Strategy Lean Canvas |  Knowledge Modeling Shape-First Modeling |  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. | When an early product bet still has too many open points, it brings target group, problem, and assumptions into a single view. It condenses the idea so core risks become nameable. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Wochen bis Monate je Lernzyklus | 45-90 min | Halber Tag pro Domain Slice | 2-4 h |
Participantsdifferent | 2-8 | 1-6 | 1-4 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Lean Canvas, Core Assumptions, Experiment Backlog | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | LeanStartupValidationMVP | StartupLeanAssumptionsBusiness model | Knowledge graphValidationSemantic | OntologyKnowledge graphScopeRequirementsSemantic |