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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |  Product Discovery Learning Card |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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. | The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 30-60 min | 25-40 min | 45-75 min |
Participantsdifferent | 1-4 | 1-5 | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Completed Experiment Canvas, Success Metric | Learning Card with evidence and next action | Test Matrix, Test Plan |
Tags1 shared | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis | ExperimentsValidationDiscoveryLearning | ExperimentsValidationDiscoveryOptions |