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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Pareto Analysis |  Operations Fishbone Diagram |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
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. | In many problem situations, effect is distributed unequally across many causes. A Pareto Analysis separates the few drivers with high leverage from the long remainder and focuses attention on what measurably moves the needle. | When a problem has several possible contributing factors, the method sorts causes by cause areas. It prevents an initial hunch from blocking the view of other plausible drivers. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | Halber Tag pro Domain Slice | 30-60 min | 30-60 min | 2-4 h |
Participantsdifferent | 1-4 | 1-6 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Pareto Chart, Top Causes, Focus List | Fishbone Diagram, Cause Categories, Investigation Backlog | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | Knowledge graphValidationSemantic | PrioritizationQualityFocusRoot cause | Root causeQualityOperations | OntologyKnowledge graphScopeRequirementsSemantic |