AI-Assisted Analytics Workflow for Non-Technical Professionals (From Messy Ask to Verified Report)

A guided, multi-course journey that builds this skill from the ground up. Work through the chapters in order, or jump to any lesson.

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From “Sales Are Down” to a Testable Analytics Question Lock the Brief: Objective → Decision → Metric → Time Window → Baseline Define Metrics So AI Can’t Guess: Numerator, Denominator, Unit Choose Segments and Grain That Match the Decision Use AI to Draft Clarifying Questions—Then Rewrite Them Into Measurable Tests Request the Minimum Necessary Data—and Document AI Use and Limits

Metric vs Dimension: Stop AI From Mixing Them Up Grain First: Validate What One Row Means Before You Aggregate Duplicates: Detect, Decide, and Document a Dedup Rule Missingness That Changes the Story: Unknown vs Not Applicable vs Zero Inconsistent Categories: Build a Mapping You Can Reuse and Audit Outliers With Consequences: Investigate, Cap, Exclude, or Keep Cleaning Log + Metrics Table: The Audit Trail That Makes AI Safe to Use

Turn an AI Draft Into a Verifiable Summary Plan Choose the Right Aggregation: Sum, Count, Average, and Distinct Count Reconcile Totals Before You Trust Any Pivot Rates and Denominators: Catch the ‘Wrong Denominator’ Trap Trends You Can Defend: Time Windows, Buckets, and Baselines Don’t Get Fooled by Missing Data and Partial Periods Ship the Descriptive Summary Pack: Charts, Sanity Checks, and Traceable Narratives

Turn “Why?” Into Diagnostic Sub-Questions You Can Actually Test Use AI to Suggest Drivers—Then Remove What the Data Can’t Support Compute Contribution to Explain Where the Change Came From Verify the Slice: Recompute the Highlighted Segment From Filtered Data Protect Against False Signals: Small Samples, Volatile Rates, and Window Instability Write the Diagnostic Driver Hypothesis Memo With Evidence, Limits, and Next Steps

Risk Framing for AI-Assisted Analytics: What Can Go Wrong and Where A Standard Prompt Pattern That Produces Checkable Outputs Verification Gates for AI-Generated Numbers: Recalculate, Reconcile, Resolve Verification Gates for AI-Generated Charts: Inputs, Axes, and Hidden Filters Operate Responsibly at Work: Privacy Hygiene, Bias Guardrails, Logging, and Escalation

Executive Summary That Stays Inside the Evidence Methods Section: Grain, Filters, Metrics, and Cleaning Rules in Plain English From Diagnostic Memo to Recommendations With Evidence Links Limitations That Match Your Cleaning Log and Non-Causality Boundaries AI Editing With a Final Traceability and Reproducibility Pass Package the Report: Responsible-Use Disclosure, Stakeholder Q&A, and Artifact Bundle