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| Criterion |  Knowledge Modeling Competency Questions |  Knowledge Modeling Shape-First Modeling |  Product Discovery Assumption Mapping |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 2-4 h | Halber Tag pro Domain Slice | 45-60 min | 30-60 min |
Participantsdifferent | 2-8 | 1-4 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Assumption map, Test backlog, Risk ranking | Completed Experiment Canvas, Success Metric |
Tagsno overlap | OntologyKnowledge graphScopeRequirementsSemantic | Knowledge graphValidationSemantic | AssumptionsRiskExperimentsValidation | ExperimentsValidationDiscoveryHypothesis |