IDUG NA 2026

Organizations that have optimized institutional data through governance, stewardship, and quality management are AI‑ready by design. Leveraging that institutional data both internally and externally for competitive advantage opens possibilities at scale never before imagined. This session will discuss:
Tool adoption without data maturity creates risk: Weak data foundations lead to unreliable results, reduced transparency, and diminished business value.AI builds on existing data foundations: Incremental enhancements—such as data scraping, enrichment, and feature refinement—are most effective when layered on curated data stores.Efficient access to data still matters: AI tools need immediate access to data.Data curation presents as a DBA responsibility: DBAs control data curation through standard operating procedures and best practices. Decision engines still matter: AI outputs are only as effective as the decision logic, controls, and context in which they operate.AI is an evolution of data expertise, not a replacement: The role of data professionals becomes more critical, installing guardrails in automation and scalable intelligence.This session reframes AI success as the amplification of long‑standing data discipline. Attendees will gain practical insight into what they are doing today, for better or worse, that is curating data and what approaches they can apply to improve AI positioning. Best practices in database management are not a preparatory step for AI, but translate directly into sustainable AI advantage.