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H35DATA

Decision-support and scenario modeling

When a leader is weighing a decision — should we open a new region, raise prices on this tier, hire ahead of plan, kill this product line — the pattern builds a structured model of the decision and lets the leader explore scenarios: what assumptions would have to be true for this to work, what does sensitivity look like, what's the expected vs. downside-case impact. Doesn't make the decision; structures the thinking. Different from generic forecasting because the model is constructed per decision rather than from a fixed template, and integrates with the firm's actual data so scenarios start from reality. The pattern's value is replacing the slow consultant-style analysis cycle with something a leader can iterate on in an afternoon.

WHERE THIS FITS
BUSINESS SHAPES
B2B servicesProduct company
VOLUME THRESHOLD
Below 3 significant strategic decisions per quarter a month, the payback rarely earns the build. Patterns this shape reliably pay back at 15+.
REQUIREMENTS · 4 REQUIRED, 2 OPTIONAL

Requirements describe capabilities the pattern needs in your environment, not the vendors you must buy. Any system that fills a requirement satisfies it — that’s what makes the catalog portable across the long tail of SMB tooling.

  1. decision_intake
    REQUIREDREADhuman

    Where the leader frames the decision and asks for the model.

    DATA SHAPE
    Decision description: what's being decided, what options are on the table, what success looks like, what concerns motivate the analysis.
    COMMONLY FILLED BY
    • structured intake form for the analyst team
    • conversation with the pattern that captures the framing
    • decision document the leader is drafting
  2. operational_data_access
    REQUIREDREADrequest

    The firm's operational data the model needs to ground in reality.

    DATA SHAPE
    Tables and metrics relevant to the decision: financials, customer data, operational metrics, market data.
    COMMONLY FILLED BY
    • data warehouse with relevant fact and dimension tables
    • BI tool with curated marts
    • financial system data for cost and revenue grounding
  3. external_benchmark_data
    RECOMMENDEDREADcorpus

    Comparable data from outside the firm: industry benchmarks, market sizing, peer performance.

    DATA SHAPE
    Per-domain benchmarks with sources, applicability, freshness.
    IF MISSING
    Models are grounded in firm-only data and can't compare to broader patterns. Strongly recommend for strategic decisions where external context matters.
    COMMONLY FILLED BY
    • benchmark databases the firm subscribes to
    • industry reports library
    • structured external data feed maintained by strategy team
  4. model_construction_surface
    REQUIREDREAD + WRITErequest

    Where the model gets built and shared with the leader, with structured assumptions and scenario controls.

    DATA SHAPE
    Structured model with inputs (assumptions), formulas, outputs, scenarios. Interactive: leader can adjust assumptions and see results.
    COMMONLY FILLED BY
    • interactive model in a spreadsheet or modeling tool
    • purpose-built scenario UI
    • structured model document with computable cells
  5. analyst_collaboration_route
    REQUIREDREAD + WRITErequest

    How the firm's analyst or finance team participates in shaping the model. Critical because the pattern produces drafts, not final analyses.

    DATA SHAPE
    Model with annotations from the analyst: assumption challenges, methodology refinements, sensitivity additions.
    COMMONLY FILLED BY
    • shared model the analyst can edit
    • review and comment workflow
    • structured analyst session before model goes to the leader
  6. decision_archive_destination
    RECOMMENDEDWRITEcorpus

    Where final models and the decision made get archived for organizational learning.

    DATA SHAPE
    Per-decision: framing, model, scenarios considered, decision made, rationale, prediction vs. actual over time.
    IF MISSING
    Patterns of decision-making and prediction quality stay invisible. Strongly recommend for any firm wanting to learn from its decisions over time.
    COMMONLY FILLED BY
    • decision archive (potentially fed by C9)
    • strategic decision library
    • structured retrospective archive
RUNTIME FLOW · 8 STEPS
  1. 01
    Leader frames the decision through the intake
    decision_intake
  2. 02
    Identify what data and assumptions the model needs
  3. 03
    Pull relevant operational data to ground the model in current reality
    operational_data_access
  4. 04
    Pull external benchmarks where useful
    external_benchmark_data
    DECISION Skip if external_benchmark_data not filled or not relevant.
  5. 05
    Construct draft model with explicit assumptions, computational logic, and key outputs
    model_construction_surface
  6. 06
    Route to analyst for review and refinement
    analyst_collaboration_route
  7. 07
    Iteratively present scenarios to the leader, let them adjust assumptions and explore
    model_construction_surface
  8. 08
    Archive the final model and decision for future reference and prediction-vs-outcome tracking
    decision_archive_destination
EMISSIONS · 2

Structured outputs this pattern produces. Other patterns and client systems can subscribe to them, which is how the catalog composes over time.

  • decision_history_corpus

    Library of structured decisions over time, with assumptions and outcomes, the most valuable strategic asset this pattern builds.

    CONSUMED BY
    • leadership pattern recognition
    • annual strategic reviews
    • future decision-making context
  • prediction_calibration_signal

    How well the firm's strategic predictions held up over time, useful for improving confidence calibration.

    CONSUMED BY
    • executive learning
    • strategy team retrospectives
    • decision-quality improvement