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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16802\nAchieving a trust
 ed golden record requires far more than implementing an entity resolution e
 ngine. Modern Master Data Management succeeds at the operational layer, whe
 re real-time integration, message validation, match tuning, exception handl
 ing, observability, and human stewardship must work together under producti
 on pressure. As enterprises modernize their data ecosystems, MDM platforms 
 must support faster onboarding, stronger data quality controls, resilient i
 ntegration patterns, and safer automation across complex source and downstr
 eam systems.\n\nThis product-neutral session presents a practical blueprint
  for operational MDM in real enterprise environments. The focus spans dupli
 cate prevention before ingestion, cross-system message tracing, match engin
 e tuning, bad merge and overlay recovery, stewardship queue automation, and
  the responsible use of rules, machine learning, and Generative AI. It emph
 asizes architectures that make MDM operations more observable, explainable,
  and resilient while helping data stewards move from manual review toward a
 ccelerated, evidence-based investigation.\n\n
 Design real-time search-before-create workflows to prevent duplicate creati
 on.\n
 Compare deterministic, probabilistic, and referential matching approaches.\
 n	Automate stewardship queues using rules, ML, and Generative AI.\n
 Detect, quarantine, and recover from overlays and corrupted records.\n
 Use LLMs to summarize audit trails and explain large entity clusters.\n
 Apply governance controls for explainable, auditable, human-approved decisi
 ons.\n\n
DTSTART:20261117T120000
SUMMARY:Operational MDM in the AI Era: A Real-Time Blueprint for Trusted Go
 lden Records
DTEND:20261117T124459
LOCATION: See Description
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