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| Criterion |  Knowledge Modeling Shape-First Modeling |  Operations Causal Factor Analysis |  Knowledge Modeling Competency Questions |  Operations 5 Whys |
|---|---|---|---|---|
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. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | 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. | For a single, hard-to-explain deviation, the method exposes the causal chain behind the visible symptom. It keeps the cause open until a controllable condition emerges instead of a mere description. |
Complexitydifferent | Medium | High | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 2-6 h | 2-4 h | 15-30 min |
Participantsdifferent | 1-4 | 3-10 | 2-8 | 2-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Root cause notes, Countermeasures |
Tagsno overlap | Knowledge graphValidationSemantic | CausalityIncidentRoot causeTimeline | OntologyKnowledge graphScopeRequirementsSemantic | Root causeIncidentLeanProblem solving |