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DESCRIPTION:Click for Latest Location Information: http://dgiq2024east.data
 versity.net/sessionPop.cfm?confid=162&proposalid=15772\nAs organizations be
 gin to move toward AI-powered applications, data quality has never been mor
 e important. AI-powered applications have raised the bar for data quality e
 ven higher. Simply addressing basic data syntax issues such as consistent r
 epresentation, validation, timeliness, and missing values is no longer adeq
 uate to assure that your AI applications will be successful. Quality issues
  such as accuracy, objectivity, reputation, believability, and relevance al
 so need to be addressed. This tutorial is primarily aimed at helping attend
 ees understand how these dimensions impact AI applications, and how these i
 ssues can be detected, measured, and remediated in your data quality progra
 m through broader involvement with other parts of the organization. Partici
 pants will learn\n\n
 A more comprehensive, academically validated, 16-dimensional definition of 
 data quality\n
 The often-overlooked data quality dimensions that are important for AI appl
 ications, and how to detect and remediate them\n
 The benefits of extending data quality management practices upstream to sou
 rcing and downstream to data products\n
 How to adapt the ISO 8000-61 best practices for data quality management to 
 be AI-ready\n
 Implementing data quality observability using the Data Quality Assessment F
 ramework (DQAF)\n\n
DTSTART:20241209T083000
SUMMARY:T6: Is Your Data AI Ready? A Roadmap for Building a Data Quality Ma
 nagement Program to Get You There!
DTEND:20241209T114459
LOCATION: See Description
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