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DTSTAMP:20260429T055152Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16646\nYour organization h
 as dashboards, scorecards, and governance processes for data quality. But w
 hen bad data slips into production, reaches decision-makers, or feeds AI sy
 stems the wrong context, those measurements alone do not stop the damage. T
 his session is about closing that gap.\nThe stakes are higher than most tea
 ms realize. In practice, analytics and AI often fail not because the models
  are weak, but because the data underneath them is untested, poorly describ
 ed, and insufficiently governed at the point where mistakes actually enter 
 the system. Our own survey of working data engineers found that the top rea
 son teams do not test their data is not tooling or skill. Nobody gives them
  the time or budget. Quality gets measured. It rarely gets enforced.\nChris
  Bergh shares six practical practices that move data quality from reporting
  to enforcement, helping teams catch problems earlier, reduce downstream bl
 ame, and make data more reliable for both people and AI. Drawing on work wi
 th enterprise data teams in pharma, health care, and manufacturing, this se
 ssion focuses on the operational habits that matter most: testing before pr
 oduction, stronger metadata, clearer ownership, broader quality coverage, a
 nd controls that turn standards into action.\nAttendees will leave with a s
 elf-assessment they can use with their teams right away, a framework for id
 entifying where their current program is strong or exposed, and clear langu
 age for explaining to leadership why data quality must move beyond dashboar
 ds if the organization wants trustworthy analytics and trustworthy AI.
DTSTART:20260505T101500
SUMMARY:Dashboards Don't Fix Data: Six Practices That Do, For Teams and AI 
 Agents Alike
DTEND:20260505T105959
LOCATION: See Description
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