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| Criterion |  Decision Making Delphi Method |  Knowledge Modeling Shape-First Modeling |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | Halber Tag pro Domain Slice | 1-2 h | 30-60 min |
Participantsdifferent | 6-30 Experten | 1-4 | 2-8 | 1-5 |
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 | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ForecastingExpertsDecisionStrategy | Knowledge graphValidationSemantic | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |