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DTSTAMP:20260411T012040Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16591\nAs organizations ra
 ce to adopt AI and machine learning, many overlook the silent threat that p
 oses the greatest risk to model accuracy, trustworthiness, and long-term pe
 rformance: digital debt. Decades of unstructured content, inconsistent meta
 data, duplicate records, legacy repositories, ungoverned file shares, and u
 nclear lineage accumulate into a hidden liability that directly shapes the 
 behavior and failures of modern AI systems.\n \nThis session explores 
 the compounding effect digital debt has on AI outcomes, from skewed trainin
 g sets and hallucinations to model drift, bias amplification, and unreliabl
 e decision-making. Drawing on real-world enterprise experiences and framewo
 rks, we’ll unpack how poor data hygiene degrades model quality over t
 ime and why traditional governance practices are no longer sufficient in an
  AI-driven world.\n \nParticipants will learn how to identify, measure
 , and remediate digital debt before it undermines AI initiatives; how 
 to modernize governance practices for ML ecosystems; and how to establ
 ish scalable controls that ensure accuracy, lineage, and trust. We will wal
 k through practical techniques for cleansing, restructuring, classifying, a
 nd labeling data to make it AI-ready, and highlight governance patterns tha
 t help organizations prevent future debt from accumulating.\n\nYou will lea
 ve with a roadmap for building AI that delivers reliable outcomes, reduced 
 risk, and a more defensible, future-proof data governance posture, turning 
 digital debt from a liability into a competitive advantage.\n
DTSTART:20260506T093000
SUMMARY:The Hidden Cost of Bad Data: How Digital Debt Undermines AI Outcome
 s
DTEND:20260506T101459
LOCATION: See Description
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