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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making OODA Loop |  Product Discovery MVP Test Matrix |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 | In dynamic situations, decisions become outdated faster than they can be prepared. The OODA Loop holds observing, orienting, deciding, and acting together as a recurring rhythm so reaction does not slide into inertia. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | Medium | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 15-60 min je Zyklus | 45-75 min | 30-60 min |
Participantsdifferent | 1-4 | 1-8 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Situation Assessment, Decision Loop, Action Updates | Test Matrix, Test Plan | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Knowledge graphValidationSemantic | DecisionChangeLearningStrategy | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryHypothesis |