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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Strategy Lean Canvas |  Knowledge Modeling Competency Questions |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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 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. | 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 2-4 h | 45-60 min |
Participantsdifferent | 1-4 | 1-6 | 2-8 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Lean Canvas, Core Assumptions, Experiment Backlog | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | Knowledge graphValidationSemantic | StartupLeanAssumptionsBusiness model | OntologyKnowledge graphScopeRequirementsSemantic | AssumptionsRiskExperimentsValidation |