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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Delphi Method |  Product Discovery Assumption Mapping |  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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | High | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 1-4 Wochen | 45-60 min | 1-2 h |
Participantsdifferent | 1-4 | 6-30 Experten | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Expert Forecast, Consensus Range, Assumption Notes | Assumption map, Test backlog, Risk ranking | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Knowledge graphValidationSemantic | ForecastingExpertsDecisionStrategy | AssumptionsRiskExperimentsValidation | StrategyDecisionAssumptionsHypothesis |