AI for Data Management: Driving Trust, Quality, and Efficiency
1h 15mIntermediate2026-09-16
Authors
Jess Pomfret
Database Platform Architect and Microsoft MVP
Course details
AI is transforming how data managers work, from diagnosing failed pipelines to generating documentation and validating SQL. In this course, discover practical, hands-on techniques for using AI to investigate unfamiliar data systems, troubleshoot operational incidents, optimize queries, and maintain data quality at scale. Explore responsible AI practices—addressing bias, hallucinations, privacy, and human oversight—so you can use AI confidently without compromising trust or security. By the end of this course, you'll be equipped with cutting-edge strategies to incorporate AI into your current data lifecycle.
Learning objectives
Evaluate and prioritize at least three AI use cases for your data team using business impact, implementation effort, and operational risk.
Create a clear AI-assisted data system brief that documents dataset structure, schema relationships, and plain-language explanations for stakeholders.
Generate, validate, and optimize SQL or transformation logic with AI and verify correctness before implementation.
Troubleshoot common pipeline, integration, and operations issues by using AI to form and test evidence-based scenarios.
Design and apply responsible AI practices for data work that address privacy, security, bias, hallucinations, and human oversight.
Learning objectives
Evaluate and prioritize at least three AI use cases for your data team using business impact, implementation effort, and operational risk.
Create a clear AI-assisted data system brief that documents dataset structure, schema relationships, and plain-language explanations for stakeholders.
Generate, validate, and optimize SQL or transformation logic with AI and verify correctness before implementation.
Troubleshoot common pipeline, integration, and operations issues by using AI to form and test evidence-based scenarios.
Design and apply responsible AI practices for data work that address privacy, security, bias, hallucinations, and human oversight.
Concepts
Introduction
- The ever-changing role of AI
AI for Understanding Data Systems
- Exploring an unknown dataset with AI
- Asking better questions about schemas and relationships
- Generating a first-pass data dictionary with AI
- Explaining complex data models in plain language with AI
- Validating AI s understanding of your data
AI for Query Writing and Optimization
- Build repeatable AI-assisted query workflows
- Generating SQL from business questions
- Optimizing queries with AI suggestions
- Debugging broken SQL with AI
- Refactoring complex SQL for readability and maintainability
AI for Documentation and Knowledge Sharing
- Why data documentation breaks (and how AI can help)
- Creating data lineage narratives
- Writing operational runbooks with AI
- Explaining pipelines and jobs to stakeholders
AI for Data Reliability and Incident Response
- Moving from incidents to AI-assisted workflows for data reliability
- Diagnosing failed pipelines and jobs with AI
- Troubleshooting schema drift and breaking changes
- Detecting anomalies with AI-assisted monitoring
- Communicating and escalating data incidents effectively
Responsible AI for Data Managers
- Risks of using AI with enterprise data
- Bias and fairness in data-driven AI outputs
- Detecting and reducing hallucinations in technical work
- Designing human-in-the-loop decisions for AI systems
Conclusion
- Next steps in AI for data management