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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Strategy Lean Canvas |  Product Discovery Pretotyping |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
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. | 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 | Halber Tag pro Domain Slice | 45-90 min | Stunden bis wenige Tage | 2-4 h |
Participantsdifferent | 1-4 | 1-6 | 1-4 | 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 | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | Knowledge graphValidationSemantic | StartupLeanAssumptionsBusiness model | ValidationDemandMVP | OntologyKnowledge graphScopeRequirementsSemantic |