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| Criterion |  Decision Making Delphi Method |  Knowledge Modeling Shape-First Modeling |  Delivery MoSCoW |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | 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. | 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 a release carries too many demands and priorities are only ever negotiated, MoSCoW creates clear boundaries for the next cut. Must, Should, Could, and Won't make commitment, room for maneuver, and trade-off logic visible to everyone involved. | 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 | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | Halber Tag pro Domain Slice | 30-90 min | 1-2 h |
Participantsdifferent | 6-30 Experten | 1-4 | 3-12 | 2-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Expert Forecast, Consensus Range, Assumption Notes | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Prioritized Backlog, Release Scope, Tradeoff Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingExpertsDecisionStrategy | Knowledge graphValidationSemantic | PrioritizationScopeDecision | StrategyDecisionAssumptionsHypothesis |