Day 1: Defining Quality Intelligence Requirements

Quality Decisions and Information Needs

• Validation checks for quality decisions
• Failure patterns needing deeper investigation
• Leading indicators of process drift
• Quality measures tied to outcomes

Critical Quality Data Sources

• Validation checks for critical quality
• Acceptance evidence retained for oversight
• Control limits highlighting unusual variation
• Measurable acceptance criteria for quality

Quality Taxonomy and Definitions

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

Day 2: Building AI-Enabled Quality Insight

Pattern and Trend Detection

• Practical tests for trend detection
• Sampling evidence informing quality decisions
• Defect patterns requiring root-cause analysis
• Prevention actions after recurring defects

Predictive Quality Indicators

• Evidence requirements for predictive quality indicators
• Visual outputs informing decision clarity
• Explanation limits communicated to decision-makers
• Validation checks prior to using results

Quality Knowledge and Search

• Operational risks around quality knowledge
• Data lineage supporting result traceability
• Common definitions for core measures
• Outliers reviewed prior to model use

Day 3: Operating the Quality Intelligence System

Insight Validation and Escalation

• Operational risks around insight validation
• Data quality before analytical interpretation
• Assumptions tested prior to final conclusions
• Business question linked to decisions

Dashboard and Review Cadence

• Decision points for review cadence
• Query logic checked against requirements
• Missing values requiring deliberate treatment
• Uncertainty limits around analytical conclusions

Quality Intelligence Roadmap

• Decision points for quality intelligence roadmap
• Governance roles throughout the lifecycle
• Human oversight at critical decisions
• Escalation routes for control failures

• Practical lab developing critical quality for operational use
• Decision simulation testing predictive quality indicators in context
• Hands-on workshop applying insight validation to realistic tasks
• Peer review challenging assumptions around quality intelligence roadmap