AI-Powered BI Delivery: Metrics, Governance, and Pipelines
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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Classify Data for AI Use: A Privacy-Risk Lens for BI WorkMinimum Access Controls for AI-Assisted BI: Least Privilege and Separation of DutiesPrompt and Output Handling Rules: Redaction, Storage, and Internal-Only BoundariesHuman-in-the-Loop Review Gates for KPI, SQL, Semantic, and Narrative ArtifactsIncident Response for AI Mishaps: Contain, Document, Notify, and Adjust ControlsThe BI-Team AI Governance Mini-Policy: A Reusable Template You Can Adopt
From “Looks Right” to “Verified”: The BI Prompt→Output→Verify→Revise LoopConstraint-First Prompts That Prevent BI AmbiguityFew-Shot Conventions for Metric Definitions That Stay ConsistentA Lightweight Evaluation Loop: Two Drafts, One Decision, Written EvidenceThe Evaluation Log: Making AI Assistance Auditable in BI DeliveryRed-Teaming BI Artifacts: Drift, Leakage, and Narrative OverreachProvider Guidance to BI Checks: Turning Eval Advice Into Your Team Rubric
From Vague Ask to Decision Statement and Requirements BriefDraft KPI Candidates With AI, Then De-Duplicate and Assign OwnershipApply the KPI Definition Rubric: Grain, Logic, Dimensions, and FreshnessTie KPIs to Likely Systems of Record and Document AssumptionsDesign KPI Acceptance Tests Before Writing SQLRed-Team KPI Definitions to Eliminate Ambiguity and Prevent Drift
Turn KPI Language Into a Verified Glossary MapFind Candidate Tables and Join Keys Without Falling for Plausible AI GuessesWrite a Lineage Narrative That Flags Uncertainty Instead of Hiding ItSet Freshness Expectations and Define What “Late Data” MeansDocument Caveats and Package the Discovery for Future Governed Q&A
From KPI Card to SQL Prompt: Constraining the DraftJoin-Risk Audits: Catching Duplication Before It ShipsTime Logic Proofs: Time Zones, Windows, and Boundary DatesReconciliation Queries: Proving the KPI With Tie-OutsEdge-Case Tests: Late Data, Null Keys, Refunds, and NegativesThe Validation Log: Turning Checks Into an Audit TrailPerformance Tweaks Without Semantic Drift: Optimize, Then Re-Prove
Why a Semantic Layer Exists (and Why Ad-Hoc Measures Drift)From KPI Card to Certified Metric SpecAI-Drafted Documentation and Change Notes That Reduce AmbiguityProving Semantic Equivalence to the Validated SQLChange Control for Metrics: Propose, Review, Test, Certify, Communicate, Roll BackDrift-Prevention Checks and Ownership of Failures
Reproducible Pipelines: The Difference Between “It Ran” and “It Matches”Make the Environment Verifiable: Dependencies, Credentials, and Run ContextDraft a DAG Skeleton With AI: Tasks, Dependencies, and Publish GatesState and Late Data: Idempotency, Partitioning, and Backfill RulesAI-Assisted Refactors: What Can Change Semantics Without Anyone NoticingEquivalence Checks: Proving a Refactor Preserved the KPITolerances and Escalation: When a Diff Is a Bug vs an Intended ChangeTests as Pipeline Steps: Designing the “Test Before Publish” Spine
Turn a KPI Into Data Quality Contracts You Can EnforceImplement KPI Tests With Great Expectations or dbt PatternsSpecify Anomaly Monitoring That Distinguishes Data Issues From Real MovementEnforce the Evidence Rule in AI-Assisted NarrativesRed-Team Narratives for Overreach, Drift, and Privacy LeakageDesign Governed Self-Service and AI Q&A BoundariesOperate AI-Assisted BI: Review Gates, Usage Monitoring, and Cost ControlsBuild a Phased AI Adoption Roadmap for Your BI Team