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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Assumption Mapping |  Decision Making Assumption Surfacing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it. | 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 | Low | Low |
Timedifferent | Halber Tag pro Domain Slice | 45-60 min | 45-90 min | 30-60 min |
Participantsdifferent | 1-4 | 2-8 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Assumption map, Test backlog, Risk ranking | Assumption List, Critical Assumptions, Learning Plan | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Knowledge graphValidationSemantic | AssumptionsRiskExperimentsValidation | AssumptionsRiskDecisionDiscovery | ExperimentsValidationDiscoveryHypothesis |