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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Decision Matrix |  Growth A/B Testing |  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 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. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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 | Medium | High | Medium |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 1-4 Wochen | 45-120 min |
Participantsdifferent | 1-4 | 2-8 | 1-6 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Decision Matrix, Scoring Rationale, Selected Option | Experiment results, Decision log, Learning summary | Trade-off Matrix, Criteria List, Decision Rationale, Accepted Downsides |
Tagsno overlap | Knowledge graphValidationSemantic | DecisionCriteriaScoringTradeoffs | ExperimentsGrowthAnalyticsValidation | TradeoffsDecisionCriteriaOptions |