BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//hacksw/handcal//NONSGML v1.0//EN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260722T235813Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16780\nOrganizations are
  eager to adopt AI, but many are still struggling to answer a more fundamen
 tal question: Can we trust the data? As AI initiatives accelerate, long-sta
 nding data quality issues such as missing data, inconsistent definitions, w
 eak lineage, fragmented ownership, and low trust in data become increasingl
 y visible. These challenges directly impact model performance, business con
 fidence, and an organization's ability to realize value from AI investm
 ents.\n\nDrawing on enterprise experience leading data quality programs in 
 highly regulated environments, this session explores practical approaches f
 or assessing AI data readiness, implementing quality controls across AI and
  machine learning pipelines, and strengthening trust through governance and
  accountability. Attendees will learn how to identify data quality risks be
 fore they impact outcomes, align data quality and AI governance efforts, an
 d demonstrate the business value of data quality investments. The session f
 ocuses on practical lessons learned from enterprise data quality programs, 
 implementation strategies, and actionable frameworks that organizations can
  immediately apply to support trustworthiness and scalable AI adoption.&nbs
 p;\n
DTSTART:20261118T133000
SUMMARY:Data Quality for AI/ML Pipelines
DTEND:20261118T141459
LOCATION: See Description
END:VEVENT
END:VCALENDAR