Topics
AI data analysis prompts for trends, outliers, segments, surveys, forecasts, charts, and executive summaries with explicit definitions and checks
A polished analysis can still be wrong when metrics, denominators, missing values, or time periods are unclear. These prompts start from the data contract and require reconciliation before interpretation.
Explain what a chart shows, what it does not show, and what decision it can reasonably support
Convert verified metrics into decisions, changes, risks, and requests without overstating evidence
Aggregate ratings and comments into privacy-safe themes with denominators, variation, counterevidence, and limits
Group comparable ticket evidence while preserving denominators, uncertainty, duplicates, and severity
Turn a dataset and decision into questions its fields can actually answer
Flag unusual values with a method suited to the distribution, then investigate before removal
Audit expense rows with explicit duplicate, completeness, range, sequence, and policy tests without alleging misconduct
Extract invoice headers, lines, tax, payment terms, and anomalies with source locations and arithmetic checks
Measure and visualize association while retaining time order, outliers, confounders, clustering, and alternative explanations
Reconcile line items, dates, rates, credits, taxes, prior balances, and payments while preserving unresolved discrepancies
Turn a forecasting request into a testable analysis plan with baselines, time splits, leakage controls, uncertainty, and decision limits
Map a decision question to rows, columns, values, filters, and validation totals
