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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Eisenhower Matrix |  Operations Root Cause Analysis |  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. | It separates true priorities from noise so it becomes clear what should be done now, scheduled, delegated, or dropped. | When a problem keeps recurring and the cause stays unclear, the method exposes the underlying mechanism. It separates symptom, guess, and robust explanation from one another. | 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 | Low | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 15-30 min | 1-4 h | 15-30 min |
Participantsdifferent | 1-4 | 1-6 | 3-8 | 2-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Eisenhower Matrix, Action List, Delegation Notes | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan | Root cause notes, Countermeasures |
Tagsno overlap | Knowledge graphValidationSemantic | PrioritizationFocusTasks | Root causeProblem solvingQualityIncident | Root causeIncidentLeanProblem solving |