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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration of Shape-first Modeling with its method-specific working model.
Knowledge Modeling
Shape-First Modeling
Paper illustration of the Force Field Analysis working structure.
Decision Making
Force Field Analysis
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.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 change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving.DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report.
Complexitydifferent
HighMediumLowLow
Timedifferent
1-4 WochenHalber Tag pro Domain Slice45-90 min1-2 h
Participantsdifferent
6-30 Experten1-43-122-8
Formatdifferent
AsyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesSHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape DocumentationForce Field Map, Change Levers, Risk NotesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingExpertsDecisionStrategy
Knowledge graphValidationSemantic
ChangeDecisionStrategy
StrategyDecisionAssumptionsHypothesis
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