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DTSTAMP:20260520T223550Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16461\nAs organizations pr
 epare to integrate generative AI into their data ecosystems, many face a fu
 ndamental challenge: incomplete or inconsistent metadata. To enable intelli
 gent data discovery and interaction through large language model (LLM) tech
 nologies, our team implemented an automated approach to metadata remediatio
 n.\n\nBy applying AI models to analyze representative data samples, infer c
 lassifications, and propose business definitions, we developed an AI-assist
 ed process that accelerates curation, improves accuracy, and establishes a 
 sustainable foundation for metadata quality.\n\nSession Highlights:\n\n	\n
 How AI can automate metadata remediation and enrichment at scale\n	\n	\n
 The connection between metadata quality and AI success\n	\n	\n
 Practical lessons from using LLMs to generate and validate metadata\n	\n	\n
 A governance-driven approach to ensure trust, accountability, and sustainab
 ility\n	\n	\n
 How to evolve from manual stewardship to AI-augmented metadata management\n
 \n\nThis real-world case study bridges governance and innovation — sh
 owing how to make metadata truly “AI-ready.”\n
DTSTART:20260506T083000
SUMMARY:Metadata Remediation for AI Readiness: Enabling Intelligent Data Di
 scovery Through Governance and Automation
DTEND:20260506T091459
LOCATION: See Description
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