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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Assumption Mapping |  Innovation Lean Startup |  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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Halber Tag pro Domain Slice | 45-60 min | Wochen bis Monate je Lernzyklus | 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 | Assumption map, Test backlog, Risk ranking | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Lean Canvas, Core Assumptions, Experiment Backlog |
Tagsno overlap | Knowledge graphValidationSemantic | AssumptionsRiskExperimentsValidation | LeanStartupValidationMVP | StartupLeanAssumptionsBusiness model |