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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |  Decision Making Assumption Surfacing |  Product Discovery Test Card |
|---|---|---|---|---|
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 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. | 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. | The Test Card turns a critical assumption into a testable claim and specifies in advance which result counts as success. |
Complexitydifferent | Medium | Low | Low | Low |
Timedifferent | Halber Tag pro Domain Slice | 30-60 min | 45-90 min | 20-35 min |
Participantsdifferent | 1-4 | 1-5 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Completed Experiment Canvas, Success Metric | Assumption List, Critical Assumptions, Learning Plan | Test Card with a pre-set threshold |
Tagsno overlap | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis | AssumptionsRiskDecisionDiscovery | ExperimentsValidationDiscoveryHypothesis |