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| Criterion |  Product Discovery Context Canvas |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |  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 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. | 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 | Low | Medium |
Timedifferent | 45-90 min | Halber Tag pro Domain Slice | 30-60 min | 2-4 h |
Participantsdifferent | 3-10 | 1-4 | 1-5 | 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 | Completed Experiment Canvas, Success Metric | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | ContextFramingRequirementsCanvas | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis | OntologyKnowledge graphScopeRequirementsSemantic |