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    nimrodfisher

    insight-synthesis

    nimrodfisher/insight-synthesis
    Data & Analytics
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    About

    SKILL.md

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    About

    Transform data findings into compelling insights...

    SKILL.md

    Insight Synthesis

    When to use

    • An analysis has produced many statistics but no clear "so what"
    • The team has findings but is struggling to prioritise which ones to act on
    • Stakeholders are asking "what does this mean for us?" rather than "what did you find?"
    • Multiple analyses need to be synthesised into a unified set of recommendations
    • Preparing an insight briefing for a team that doesn't have time to review the full analysis

    Process

    1. List all findings — enumerate every statistically meaningful finding: trends, comparisons, correlations, anomalies, surprises. Write each as a factual statement. Don't interpret yet.
    2. Apply So What → Why → Now What to each finding — convert each fact into an insight by answering: So what (why does this matter to the business?), Why (what is the most likely explanation?), Now what (what specific action should follow?). See references/insight_framework.md.
    3. Quantify business impact — for each insight, estimate the financial, customer, or operational magnitude. An insight without a number is an observation. Use order-of-magnitude estimates if precise data is not available.
    4. Prioritise by impact × confidence × actionability — score each insight on these three dimensions (1–3 scale). Insights that score high on all three are the ones to lead with. Deprioritise insights that are high-impact but low-confidence until validated.
    5. Group and resolve conflicts — cluster related insights and check for contradictions. If two findings point in opposite directions, document the tension and state what additional data would resolve it.
    6. Produce the insight brief — present the top 3–5 insights in priority order, each with the finding, So What / Why / Now What, business impact, and confidence level. Use assets/insight_brief_template.md.

    Inputs the skill needs

    • All analysis findings (statistics, charts, model outputs, anomalies)
    • Business context: current goals, OKRs, strategic priorities
    • Audience who will act on the insights (role and decision authority)
    • Confidence levels for the findings (based on sample size, method, data quality)
    • Known constraints on action (budget, timeline, team capacity)

    Output

    • references/insight_framework.md — So What / Why / Now What pattern, insight quality rubric, prioritisation matrix
    • references/prioritization_guide.md — scoring insights by impact, confidence, and actionability; how to present trade-offs
    • assets/insight_brief_template.md — structured brief: top insights in priority order, each with impact, explanation, recommendation, and confidence level
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    Repository
    nimrodfisher/data-analytics-skills
    Files