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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Eisenhower Matrix |  Operations 5 Whys |  Decision Making Pareto Analysis |
|---|---|---|---|---|
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. | 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. | 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. |
Complexitydifferent | Medium | Low | Low | Low |
Timedifferent | Halber Tag pro Domain Slice | 15-30 min | 15-30 min | 30-60 min |
Participantsdifferent | 1-4 | 1-6 | 2-6 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Eisenhower Matrix, Action List, Delegation Notes | Root cause notes, Countermeasures | Pareto Chart, Top Causes, Focus List |
Tagsno overlap | Knowledge graphValidationSemantic | PrioritizationFocusTasks | Root causeIncidentLeanProblem solving | PrioritizationQualityFocusRoot cause |