method

Five steps, one map

Every project follows the same discipline: the decision problem comes first, the data structure second, the technology choice last.

  1. Map the decision problem

    What happens
    Target decisions are defined: who decides, how often, with what information. An inventory of existing reports and tools is taken.
    Your input
    A 2-3 hour working session, access to existing reports, short interviews with decision owners.
    Output
    A decision map document: decisions, data sources, owners, priority order.

  2. Structure the data

    What happens
    Sources are connected, the data model and KPI dictionary are built, and quality issues are made visible.
    Your input
    System access, contact with data owners.
    Output
    A data model + KPI dictionary + data quality report.

  3. Build the intelligence layer

    What happens
    Analytics and AI components are built: a dashboard model, a forecast/pricing model, an agent or document pipeline. They are tested with sample cases and correct-answer sets.
    Your input
    Sample cases, correct-answer sets, feedback from domain expertise.
    Output
    A working system + test results. Quality is shown by measurement, not by claims.

  4. Design the decision interface

    What happens
    The output becomes an interface that fits the user's work: a dashboard, internal tool or app. Permissions and flow are designed.
    Your input
    1-2 test rounds with end users.
    Output
    A live interface + a short usage guide. The system passes the "the team can use it" test.

  5. Enable the team

    What happens
    Training, handover and documentation are done; a maintenance and development rhythm is defined.
    Your input
    Participation in workshops.
    Output
    Training sessions + a handover document + a 30/60/90-day plan.
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