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DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16634\nAI systems are only
  as trustworthy as the data that powers them. Most organizations racing to 
 deploy AI have inherited pipelines built for reporting – not for the 
 precision and traceability that AI demands. The result: models that halluci
 nate, agents that drift, and governance teams accountable for failures they
  couldn't see coming.\n\nThe root causes are well understood but chroni
 cally under-addressed – source data variability, pipeline failures, s
 chema drift, missed business rules, undetected anomalies, and the absence o
 f observability. This session examines each failure pattern and the enginee
 ring disciplines required to address them systematically, including agentic
  data testing and continuous monitoring. Whether you are an engineer design
 ing pipelines or a governance leader setting standards, you will leave with
  a practical framework for making reliable data the non-negotiable foundati
 on of enterprise AI.\nTakeaways\n\n
 Recognize the data failure patterns that undermine AI initiatives.\n
 Understand how to engineer AI-ready data and pipelines.\n
 Learn how agentic-first approaches can scale data reliability engineering.\
 n
 Create a shared framework that aligns technical, governance, and business t
 eams.\n\n
DTSTART:20260506T104500
SUMMARY:Engineering Reliable Data for Reliable AI
DTEND:20260506T111459
LOCATION: See Description
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