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| Criterion |  Knowledge Modeling Shape-First Modeling |  Innovation Lean Startup |  Knowledge Modeling Competency Questions |  Product Strategy Lean Canvas |
|---|---|---|---|---|
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. | 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. | 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 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Halber Tag pro Domain Slice | Wochen bis Monate je Lernzyklus | 2-4 h | 45-90 min |
Participantsdifferent | 1-4 | 2-8 | 2-8 | 1-6 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Lean Canvas, Core Assumptions, Experiment Backlog |
Tagsno overlap | Knowledge graphValidationSemantic | LeanStartupValidationMVP | OntologyKnowledge graphScopeRequirementsSemantic | StartupLeanAssumptionsBusiness model |