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| Criterion |  Knowledge Modeling Shape-First Modeling |  Systems Thinking Leverage Points |  Product Strategy DIBB |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | Medium | High | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | Half day | 1-2 h | 45-60 min |
Participantsdifferent | 1-4 | 3-12 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Leverage Map, Action Strategy | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | Knowledge graphValidationSemantic | Systems thinkingChangeStrategy | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation |