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| Criterion |  Knowledge Modeling Property Graph Schema Design |  Growth A/B Testing |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | Property Graph Schema Design turns business and technical requirements into a robust graph schema. It fits when query patterns, constraints, and migrations need to be thought about together. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | High | Medium | Low |
Timedifferent | 4-8 h initial, dann iterativ | 1-4 Wochen | Halber Tag pro Domain Slice | 30-60 min |
Participantsdifferent | 1-6 | 1-6 | 1-4 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | Schema Diagram, Node and Relationship Catalog, Index Plan, Migration Scripts, Sample Queries | Experiment results, Decision log, Learning summary | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Knowledge graphModelingSemantic | ExperimentsGrowthAnalyticsValidation | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis |