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| Criterion |  Product Discovery Context Canvas |  Knowledge Modeling Shape-First Modeling |  Product Discovery Assumption Mapping |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
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. | 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. | 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 | Low | Medium | Medium | Medium |
Timedifferent | 45-90 min | Halber Tag pro Domain Slice | 45-60 min | 2-4 h |
Participantsdifferent | 3-10 | 1-4 | 2-8 | 2-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Context canvas, Open questions, Shared context notes | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Assumption map, Test backlog, Risk ranking | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | ContextFramingRequirementsCanvas | Knowledge graphValidationSemantic | AssumptionsRiskExperimentsValidation | OntologyKnowledge graphScopeRequirementsSemantic |