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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Delphi Method |  Knowledge Modeling Ontology Design Patterns |  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. | Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles. | 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 | 1-3 h pro Pattern | 1-2 h |
Participantsdifferent | 1-4 | 6-30 Experten | 1-6 | 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 | Selected Patterns, Adapted Schema Fragments, Pattern Documentation | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Knowledge graphValidationSemantic | ForecastingExpertsDecisionStrategy | OntologyPatternsReuseSemanticModeling | StrategyDecisionAssumptionsHypothesis |