AI-Assisted Analytics for Non-Technical Teams
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 QuestionLock the Brief: Objective → Decision → Metric → Time Window → BaselineDefine Metrics So AI Can’t Guess: Numerator, Denominator, UnitChoose Segments and Grain That Match the DecisionUse AI to Draft Clarifying Questions—Then Rewrite Them Into Measurable TestsRequest the Minimum Necessary Data—and Document AI Use and Limits
Metric vs Dimension: Stop AI From Mixing Them UpGrain First: Validate What One Row Means Before You AggregateDuplicates: Detect, Decide, and Document a Dedup RuleMissingness That Changes the Story: Unknown vs Not Applicable vs ZeroInconsistent Categories: Build a Mapping You Can Reuse and AuditOutliers With Consequences: Investigate, Cap, Exclude, or KeepCleaning Log + Metrics Table: The Audit Trail That Makes AI Safe to Use
Turn an AI Draft Into a Verifiable Summary PlanChoose the Right Aggregation: Sum, Count, Average, and Distinct CountReconcile Totals Before You Trust Any PivotRates and Denominators: Catch the ‘Wrong Denominator’ TrapTrends You Can Defend: Time Windows, Buckets, and BaselinesDon’t Get Fooled by Missing Data and Partial PeriodsShip the Descriptive Summary Pack: Charts, Sanity Checks, and Traceable Narratives
Turn “Why?” Into Diagnostic Sub-Questions You Can Actually TestUse AI to Suggest Drivers—Then Remove What the Data Can’t SupportCompute Contribution to Explain Where the Change Came FromVerify the Slice: Recompute the Highlighted Segment From Filtered DataProtect Against False Signals: Small Samples, Volatile Rates, and Window InstabilityWrite the Diagnostic Driver Hypothesis Memo With Evidence, Limits, and Next Steps
Risk Framing for AI-Assisted Analytics: What Can Go Wrong and WhereA Standard Prompt Pattern That Produces Checkable OutputsVerification Gates for AI-Generated Numbers: Recalculate, Reconcile, ResolveVerification Gates for AI-Generated Charts: Inputs, Axes, and Hidden FiltersOperate Responsibly at Work: Privacy Hygiene, Bias Guardrails, Logging, and Escalation
Executive Summary That Stays Inside the EvidenceMethods Section: Grain, Filters, Metrics, and Cleaning Rules in Plain EnglishFrom Diagnostic Memo to Recommendations With Evidence LinksLimitations That Match Your Cleaning Log and Non-Causality BoundariesAI Editing With a Final Traceability and Reproducibility PassPackage the Report: Responsible-Use Disclosure, Stakeholder Q&A, and Artifact Bundle