AI Prompts for Data Analysis & Reports

Analyze this dataset and provide insights: [paste data or describe the dataset — columns, row count, time period]. Identify: the 3 most significant patterns or trends, any anomalies or outliers worth investigating, correlations between variables, segments that behave differently from the average, and 3 actionable recommendations based on the data. Present findings in order of business impact, not statistical significance.

Write a SQL query for [database type — PostgreSQL/MySQL/SQLite] that [describe what you need]. The relevant tables are: [list tables with key columns]. The query should: handle NULL values appropriately, use proper JOIN types with reasoning, include WHERE clauses for [filtering criteria], aggregate with [GROUP BY requirements], and order results by [sort preference]. Add comments explaining the logic and suggest an index that would improve performance.

Write a professional narrative description for a chart showing [describe the chart — type, axes, data represented, time period]. Include: a one-line summary of the main takeaway, description of the primary trend, notable inflection points or changes with possible explanations, comparison to benchmarks or previous periods, and a forward-looking statement about what this trend suggests. Write as if presenting to [audience — executives/team/board].

Perform a trend analysis on this data: [paste time-series data or describe the dataset]. Identify: the overall direction (upward/downward/stable/cyclical), rate of change and whether it is accelerating or decelerating, seasonal patterns if applicable, the 3 most significant turning points and possible causes, how the current trend compares to [industry benchmark or historical average], and a forecast for the next [time period] with stated assumptions and confidence level.

Design an executive dashboard for [department/business unit] that monitors [business objective]. Recommend: 6-8 KPIs with definitions and data sources, the ideal chart type for each KPI and why, dashboard layout with hierarchy (what goes top-left for maximum visibility), alert thresholds for each metric (green/yellow/red), one drill-down view for the most critical metric, and the refresh frequency for each data source.

Help me investigate this data anomaly: [describe the anomaly — what metric, when it occurred, how it deviates from normal]. Suggest: 5 possible root causes ranked by likelihood, specific data checks to confirm or rule out each cause, which additional data sources to examine, a troubleshooting decision tree to follow, and how to determine if this is a data quality issue vs. a real business event. Include the SQL or formula needed for each diagnostic check.

Analyze these survey results: [paste survey data or key statistics]. The survey had [number] respondents from [population]. Provide: key findings ranked by significance, demographic breakdowns if available, sentiment analysis of open-ended responses, comparison with [benchmark or previous survey], statistically notable differences between segments, and 5 specific action items based on the findings. Present the analysis in a format ready for stakeholder review.

Help me build a forecast for [metric] over the next [time period]. Historical data: [paste or describe historical data]. Define: which forecasting method is most appropriate for this data and why (moving average, exponential smoothing, regression, etc.), the variables that should be included as predictors, assumptions that need to be stated, how to calculate confidence intervals, a formula or code implementation in [Excel/Python/R], and how to measure the forecast's accuracy after the fact.