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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16790\nData is the lifeb
 lood of AI productivity. By its very nature, the rapid ramp-up of AI promis
 es to manufacture more data, move it faster through more shadow pipelines, 
 and create extraordinary demands on data quality efforts that are already u
 nder stress. AI blurs the boundaries between data producer, engineer, and a
 nalyst, promising access to new capabilities, but all too often demanding a
 doption at the risk of declining quality standards. There has to be a bette
 r way.\n\nAI redefines the purview, reach, and scalability of data quality 
 efforts, but it doesn't change their principles. This session focuses o
 n how to apply proven best practices in what may seem like a completely new
  landscape. We will:\n\n
 Demarcate the infrastructure of AI as an information environment\n
 Show how AI "context" functions as data, code, and documentation 
 all at the same time, and how these functions can be disentangled, assessed
 , and monitored \n
 Highlight real-world examples for managing context in an AI-driven data pip
 eline in tools like Claude Code, Databricks Genie Spaces, and Snowflak
 e Cortex\n
 Teach how to leverage proactive, practical data quality strategies and tech
 niques as an accelerator for successful AI implementation\n\nThe goal of th
 is course is to show you how to improve AI implementation and results by ca
 pitalizing on your data quality expertise and the established methods mappe
 d out in the book Executing Data Quality Projects: Ten Steps to Quality Dat
 a and Trusted Information™, 2nd Ed. (Elsevier/Academic Press, 2021) b
 y Danette McGilvray. We'll also show you how to wield innovative AI tec
 hniques within a clear, controlled framework to empower your own data quali
 ty initiatives.\n\nChip Bloche and Danette McGilvray combine their expertis
 e (his technical/AI with Danette's data quality) to show how to effecti
 vely apply data quality principles and AI techniques to ensure the effectiv
 eness of AI outcomes. At the end of this session, you will be primed to nav
 igate new territory with new ideas and your own trusty compass.
DTSTART:20261116T134500
SUMMARY:Quality Data and Trusted AI: Turning Data Quality Expertise Into an
  AI Accelerator
DTEND:20261116T165959
LOCATION: See Description
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