Day 1: Framing Analytical Questions and Data Context

Business Question and Metric Logic

• Validation checks for business question
• Missing values needing deliberate treatment
• Data lineage supporting result traceability
• Common definitions for key measures

Schema and Data Relationships

• Ownership expectations for data relationships
• Visual outputs supporting decision clarity
• Explanation boundaries communicated to decision-makers
• Validation checks before using results

Data Access and Sensitivity

• Ownership expectations for data access
• Outliers reviewed prior to model use
• Data quality before analytical interpretation
• Assumptions tested prior to final conclusions

Day 2: Using Generative AI for SQL Work

Query Drafting and Explanation

• Evidence requirements for query drafting
• Uncertainty boundaries around analytical conclusions
• Query logic checked against requirements
• Business question linked to decisions

Debugging and Optimization

• Review criteria for debugging and optimization
• Acceptance evidence retained for review
• Control limits highlighting unusual variation
• Failure patterns requiring deeper investigation

Analytical Exploration and Summaries

• Decision points for analytical exploration
• Leading indicators of process drift
• Quality measures linked to outcomes
• Quantifiable acceptance criteria for quality

Day 3: Validating Analysis Before Decision Use

Query Testing and Reconciliation

• Review criteria for query testing
• Corrective actions tied to evidence
• Process variation prior to customer impact
• Verification checks before final release

Hallucination and Logic Checks

• Validation checks for logic checks
• Sampling evidence supporting quality decisions
• Defect patterns needing root-cause analysis
• Prevention actions after recurring defects

AI-Assisted Analysis Workflow

• Decision points for AI-assisted analysis workflow
• Ownership of approved AI outputs
• Oversight roles across the lifecycle
• Human oversight at high-impact decisions

• Simulation testing data relationships under operational pressure
• Guided exercise applying debugging and optimization to workplace cases
• Group review evaluating query testing against evidence
• Case analysis examining AI-assisted analysis workflow in workplace decisions