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| Criterion |  Knowledge Modeling Shape-First Modeling |  Delivery MoSCoW |  Decision Making Decision Matrix |  Decision Making Trade-off Analysis |
|---|---|---|---|---|
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 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. | When several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | With limited resources, quality, speed, cost, and risk almost always compete with each other. Trade-off Analysis makes these tensions explicit and prevents decisions from producing hidden side effects. |
Complexitydifferent | Medium | Low | Medium | Medium |
Timedifferent | Halber Tag pro Domain Slice | 30-90 min | 45-90 min | 45-120 min |
Participantsdifferent | 1-4 | 3-12 | 2-8 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Prioritized Backlog, Release Scope, Tradeoff Notes | Decision Matrix, Scoring Rationale, Selected Option | Trade-off Matrix, Criteria List, Decision Rationale, Accepted Downsides |
Tagsno overlap | Knowledge graphValidationSemantic | PrioritizationScopeDecision | DecisionCriteriaScoringTradeoffs | TradeoffsDecisionCriteriaOptions |