insight-synthesis（洞察综合）

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