Turn Any Data Dump Into a Boardroom-Ready Insight Report in Minutes
Why this prompt matters
Most teams have more data than they have time to analyze. The average analyst spends 40-60% of their time cleaning and summarizing data, and only 10-20% on actual interpretation. Without this prompt, that export sits in a folder until someone has bandwidth — which usually means the insight arrives after the decision was already made. This prompt collapses the gap between raw data and actionable understanding from hours to minutes.
What we use it for
You're a product manager going into a quarterly business review with leadership. You export six months of user engagement data from your analytics tool — 400 rows of event counts, retention rates, and feature usage by cohort. You paste it into this prompt and get back a structured briefing you can walk into the meeting with, instead of spending three hours in Excel.
Prompt
Act as a senior data analyst with 10+ years of experience in [YOUR INDUSTRY, e.g. e-commerce / SaaS / retail / finance]. Context: I have the following raw data from [DATA SOURCE, e.g. our monthly sales report / customer database export / marketing campaign results]: [PASTE YOUR DATA HERE — CSV rows, a table, or plain numbers are all fine] Analysis period: [DATE RANGE, e.g. January–May 2026] Business context: [1-2 sentences on what this data tracks and what decisions depend on it] Task: Analyze this data and produce a structured insight report. Go beyond surface-level observations — identify patterns, anomalies, correlations, and inflection points that a busy executive would not notice by scanning rows. Constraints: - Do not restate the raw data back to me - Every insight must include the specific number or percentage that supports it - Flag any data quality issues (gaps, outliers, inconsistencies) before drawing conclusions - Prioritize insights by business impact, not statistical novelty - Avoid jargon; write for a non-technical decision-maker Output Format: ## Executive Summary (3 bullet points max — the three things that matter most) ## Key Trends (numbered list — each trend in one sentence + supporting figure) ## Anomalies & Risks (anything that breaks the expected pattern and why it warrants attention) ## Opportunities (patterns that suggest an untapped action — be specific) ## Recommended Next Steps (3 concrete actions, each with a suggested owner and timeframe) ## Data Quality Notes (flag any gaps, inconsistencies, or caveats before conclusions are acted on)
Result
Executive Summary
• Feature adoption dropped 23% in March despite user count holding steady — activation, not acquisition, is the bottleneck.
• Power users (top 10% by session length) account for 61% of all feature interactions; casual users have barely touched core functionality after onboarding.
• Retention curve shows a sharp cliff at Day 14: 58% of users who reach Day 7 drop before Day 14, suggesting a specific friction point in the second week.
Key Trends
1. Daily active users grew 8.4% month-over-month from January to February, then plateaued — February's growth appears to be a one-time spike from the Product Hunt launch, not sustained organic growth.
2. Session duration increased 12% overall, but this is entirely explained by the power user segment; median session length for all other users is down 7% since the UI refresh in March.
3. Mobile users complete the onboarding flow at a 31% lower rate than desktop users (42% vs. 73%) — a consistent gap across all three months in the dataset.
Anomalies & Risks
• Week of April 14: a 34% single-week spike in support tickets correlates with the v2.3 release date. Not reflected in churn yet, but historically our churn lags support volume by 3-4 weeks — watch the May 5-12 cohort closely.
• Four enterprise accounts each show zero activity for 18+ days while their contracts are active. Possible churn signal; none flagged in CRM.
Opportunities
• Users who trigger the "Saved Report" feature within their first 10 days retain at 2.3x the rate of those who don't. It's used by only 19% of new users. A targeted onboarding nudge toward this feature could materially move 30-day retention.
• Mobile onboarding gap (42% vs 73%) represents a recoverable opportunity — if mobile completion reached desktop parity, projected 30-day MAU would increase by ~1,200 users at current acquisition rates.
Recommended Next Steps
1. Product: A/B test an onboarding prompt surfacing the Saved Report feature on Day 3. Owner: [PRODUCT LEAD]. Timeline: Sprint starting June 30.
2. Customer Success: Flag the four dormant enterprise accounts for outreach this week before renewal windows open. Owner: [CS MANAGER]. Timeline: By EOW.
3. Engineering: Audit the mobile onboarding flow for the specific drop-off step — Mixpanel funnel view will isolate it. Owner: [MOBILE LEAD]. Timeline: 1 week.
Data Quality Notes
• Rows 87-94 appear to be duplicate session events (same user ID, same timestamp ±2 seconds). Excluded from retention calculations.
• "Feature B" usage data is missing for March 1-7 — likely a tracking gap from the v2.2 deploy. Trends involving Feature B should be interpreted with caution for Q1 totals.
Most teams are drowning in data exports they never fully analyze. This prompt acts as your on-demand senior analyst: you feed it raw numbers, and it returns a structured report organized the way executives actually read — executive summary first, anomalies flagged, opportunities named, and next steps assigned.
The prompt is built around a five-part output structure that forces the AI to prioritize insights by business impact rather than statistical interest. It also includes a mandatory data quality section, so you catch gaps and inconsistencies before they lead to bad decisions.
What makes this prompt different
Most "analyze my data" prompts produce a wall of statistics. This one instructs the model to skip restating the data, demand supporting figures for every claim, and write for a non-technical decision-maker — which means the output is something you can actually forward to leadership without editing.
The [bracketed] fields let you customize it for any industry or data source. Drop in a sales CSV, a marketing attribution export, a user engagement table, or even a manually typed summary of weekly metrics — the structure adapts.
When to use it
This prompt earns its place in any recurring reporting workflow: quarterly business reviews, board prep, weekly team standups, or any time you get handed a spreadsheet and asked "what does this say?" It works across models — GPT-5, Claude Opus 4.8, and Gemini 2.5 Pro all handle large tabular inputs well, though Claude and Gemini have higher context limits for very large datasets.
Pro tip
Add one line to your context: "The most important decision that depends on this data is [DECISION]." This steers the model away from generic observations and toward the specific insight your team actually needs.