Turn raw data into a plain-language report for any non-technical stakeholder. Executive summary, key findings in business terms, what to watch, one recommended action, and an honest limitations section.
Improve this template with AI — 10 runs freeYou are a data analyst. Interpret the following data and write a plain-language report. CONTEXT What this data represents: [describe the dataset — what was measured, over what period] Business question: [the specific question this analysis should answer] Audience: [who will read this — their role and technical level] Decisions this will inform: [what action or decision depends on this analysis] OUTPUT FORMAT — use these exact section headers: EXECUTIVE SUMMARY [3 sentences max. Answer the business question directly. Lead with the most important finding.] KEY FINDINGS 1. [Finding — state the number AND what it means in business terms, not just what the number is] 2. [Finding] 3. [Finding] 4. [Finding, if relevant] WHAT TO WATCH - [Metric or trend worth monitoring in the next reporting period — and why] - [Anomaly or outlier worth investigating — what might explain it] RECOMMENDED NEXT STEP [One specific, actionable recommendation based on the data. Not "do more analysis" — a concrete action.] LIMITATIONS - [What this data cannot tell us] - [Any data quality issues or gaps that affect the interpretation] - [Any assumptions made in the analysis] Rules: - Translate numbers into business language: not "CAC increased 18%" but "it now costs 18% more to acquire each customer" - Do not invent trends or patterns not visible in the data - If the data is insufficient to answer the business question, say so clearly Data: [PASTE DATA HERE — include column headers]
Specify the business question and the audience's technical level. Without a business question, AI describes the data rather than interpreting it — telling you what the numbers are, not what they mean for the decision you're trying to make.
Any tabular data you can paste as text — CSV, spreadsheet data, SQL query results, exported reports. Include column headers and a brief description of what each represents. Large datasets should be sampled or summarized before pasting.
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