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DTSTAMP:20260521T142724Z
DESCRIPTION:Click for Latest Location Information: http://dgiq2024east.data
 versity.net/sessionPop.cfm?confid=162&proposalid=15746\n"I can't d
 o data QA because my team hasn't defined what data quality looks like&q
 uot; – if this sentence sounds familiar, you're not alone.\n
 \nHigh-quality data is AI's secret sauce, yet defining what that means 
 can seem impossible. Who defines data quality, how does it differ for each 
 data domain, when should you update those standards post-production, and wh
 at moves your data governance from a 60-page Word document updated last dec
 ade to a clear set of standards that serve as your data architecture's 
 backbone?\n\nThis talk covers the basics of great data governance: The peop
 le, processes, and tools needed to automate data quality at scale. You'
 ll learn how to define data domains, organize your data architecture, creat
 e data QA, and build more transparency into your algorithms.\n
DTSTART:20241210T111500
SUMMARY:From Paper to Production: Automating Data Quality for AI
DTEND:20241210T115959
LOCATION: See Description
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