insight-synthesis

name	insight-synthesis
description	Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights for stakeholder communication.

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