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Criterion
Paper illustration of Shape-first Modeling with its method-specific working model.
Knowledge Modeling
Shape-First Modeling
Paper illustration of a calm four-quadrant Assumption Map with hypothesis cards and emphasis on important assumptions with little evidence.
Product Discovery
Assumption Mapping
Four paper stations connect system purpose, quality goals, risk cards and focused investigation with a target and gap profile.
Architecture
LASR (Lightweight Approach for Software Reviews)
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
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.When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first.LASR helps development teams identify where their software falls short of important quality goals and which improvements to consider. A small group with existing system knowledge can make an initial assessment and investigate open questions in more detail.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.
Complexitydifferent
MediumMediumMediumHigh
Timedifferent
Halber Tag pro Domain Slice45-60 min3–4 h for initial assessment; preparation and deeper analysis additional1-4 Wochen
Participantsdifferent
1-42-81 or a small team; particularly suitable for 3–4 people1-6
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape DocumentationAssumption map, Test backlog, Risk rankingLean Mission Statement describing the software’s purpose, Prioritized quality goals and evaluation criteria, Risks and estimated gaps against quality goals, More closely examined findings and open checksExperiment results, Decision log, Learning summary
Tagsno overlap
Knowledge graphValidationSemantic
AssumptionsRiskExperimentsValidation
ArchitectureQualityRiskAssessmentLean
ExperimentsGrowthAnalyticsValidation
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