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| Criterion |  Knowledge Modeling Shape-First Modeling |  Product Discovery Concierge MVP |  Product Strategy DIBB |
|---|---|---|---|
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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. |
Complexitydifferent | Medium | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 1-4 Wochen | 1-2 h |
Participantsdifferent | 1-4 | 3-10 Kunden | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Concierge Learnings, Service Blueprint, MVP Risks | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Knowledge graphValidationSemantic | MVPValidationServiceDiscovery | StrategyDecisionAssumptionsHypothesis |
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