In this episode, Ramon Chen sits down with industry veteran Dan Power to explore the evolution of data governance, quality, and management in the age of AI. From foundational lessons at Dun & Bradstreet to regulatory pressures in financial services, Dan shares what it takes to build trusted, scalable data practices—and where AI fits into the picture.
Key Takeaways:
- Data Products Need Discipline: Creating reliable data products requires manufacturing-like thinkingclear inputs, measurable outputs, and trust scores to drive business value.
- AI Is Not a Silver Bullet—But a Power Tool: GenAI and agentic systems can automate manual data work, but only if foundational data observability is in place.
- Regulations Demand Operational Readiness: Regulatory compliance increasingly depends on fast, accurate lineage, quality metrics, and the ability to prove control—not just documentation.
Data Forward: AI and Data Management News