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| Criterion |  Product Discovery Context Canvas |  Knowledge Modeling Competency Questions |  Product Discovery Assumption Mapping |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | When an environment of users, systems, and constraints is hard to grasp, it puts the context compactly on the table. It creates a clear reading of influence, boundary, and dependency. | 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. | 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. |
Complexitydifferent | Low | Medium | Medium | Medium |
Timedifferent | 45-90 min | 2-4 h | 45-60 min | Halber Tag pro Domain Slice |
Participantsdifferent | 3-10 | 2-8 | 2-8 | 1-4 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Context canvas, Open questions, Shared context notes | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Assumption map, Test backlog, Risk ranking | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | ContextFramingRequirementsCanvas | OntologyKnowledge graphScopeRequirementsSemantic | AssumptionsRiskExperimentsValidation | Knowledge graphValidationSemantic |