View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Innovation Lean Startup |  Knowledge Modeling Competency Questions |  Product Discovery Assumption Mapping |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | Wochen bis Monate je Lernzyklus | 2-4 h | 45-60 min | Halber Tag pro Domain Slice |
Participantsdifferent | 2-8 | 2-8 | 2-8 | 1-4 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Assumption map, Test backlog, Risk ranking | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | LeanStartupValidationMVP | OntologyKnowledge graphScopeRequirementsSemantic | AssumptionsRiskExperimentsValidation | Knowledge graphValidationSemantic |