Compute Contribution to Explain Where the Change Came From

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1From “Sales Are Down” to a Testable Analytics Question 2Lock the Brief: Objective → Decision → Metric → Time Window → Baseline 3Define Metrics So AI Can’t Guess: Numerator, Denominator, Unit 4Choose Segments and Grain That Match the Decision 5Use AI to Draft Clarifying Questions—Then Rewrite Them Into Measurable Tests 6Request the Minimum Necessary Data—and Document AI Use and Limits

7Metric vs Dimension: Stop AI From Mixing Them Up 8Grain First: Validate What One Row Means Before You Aggregate 9Duplicates: Detect, Decide, and Document a Dedup Rule 10Missingness That Changes the Story: Unknown vs Not Applicable vs Zero 11Inconsistent Categories: Build a Mapping You Can Reuse and Audit 12Outliers With Consequences: Investigate, Cap, Exclude, or Keep 13Cleaning Log + Metrics Table: The Audit Trail That Makes AI Safe to Use

14Turn an AI Draft Into a Verifiable Summary Plan 15Choose the Right Aggregation: Sum, Count, Average, and Distinct Count 16Reconcile Totals Before You Trust Any Pivot 17Rates and Denominators: Catch the ‘Wrong Denominator’ Trap 18Trends You Can Defend: Time Windows, Buckets, and Baselines 19Don’t Get Fooled by Missing Data and Partial Periods 20Ship the Descriptive Summary Pack: Charts, Sanity Checks, and Traceable Narratives

21Turn “Why?” Into Diagnostic Sub-Questions You Can Actually Test 22Use AI to Suggest Drivers—Then Remove What the Data Can’t Support

23Compute Contribution to Explain Where the Change Came From

24Verify the Slice: Recompute the Highlighted Segment From Filtered Data 25Protect Against False Signals: Small Samples, Volatile Rates, and Window Instability 26Write the Diagnostic Driver Hypothesis Memo With Evidence, Limits, and Next Steps

27Risk Framing for AI-Assisted Analytics: What Can Go Wrong and Where 28A Standard Prompt Pattern That Produces Checkable Outputs 29Verification Gates for AI-Generated Numbers: Recalculate, Reconcile, Resolve 30Verification Gates for AI-Generated Charts: Inputs, Axes, and Hidden Filters 31Operate Responsibly at Work: Privacy Hygiene, Bias Guardrails, Logging, and Escalation

32Executive Summary That Stays Inside the Evidence 33Methods Section: Grain, Filters, Metrics, and Cleaning Rules in Plain English 34From Diagnostic Memo to Recommendations With Evidence Links 35Limitations That Match Your Cleaning Log and Non-Causality Boundaries 36AI Editing With a Final Traceability and Reproducibility Pass 37Package the Report: Responsible-Use Disclosure, Stakeholder Q&A, and Artifact Bundle

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Compute Contribution to Explain Where the Change Came From