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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Concierge MVP |  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 an idea can first fail or grow through genuine hands-on support, it relies on manual work instead of automation. It shows whether user value holds up even under manual execution. | 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 |
Timedifferent | Halber Tag pro Domain Slice | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-4 | 3-10 Kunden | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Concierge Learnings, Service Blueprint, MVP Risks | Completed Experiment Canvas, Success Metric |
Tags1 shared | Knowledge graphValidationSemantic | MVPValidationServiceDiscovery | ExperimentsValidationDiscoveryHypothesis |
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