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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16774\nAs organizations 
 accelerate the adoption of artificial intelligence, one challenge becomes i
 mpossible to ignore: AI can only be as reliable as the data behind it. Poor
  data quality can amplify bias, weaken trust, increase risk, and undermine 
 the value of AI initiatives. Prioritizing limited resources becomes more re
 levant than ever. This session combines Marilu Lopez’s Data Strategy 
 PAC Method, from Data Strategies for Data Governance, with Danette McGilvra
 y’s proven Ten Steps methodology from Executing Data Quality Projects
 : Ten Steps to Quality Data and Trusted Information™, as a practical 
 approach to addressing data quality challenges in the age of AI.\n\nThe ses
 sion will connect business priorities to an effective data strategy and the
 n to core data quality principles aligned with today’s AI governance 
 needs. It will show how organizations must define business impact, assess d
 ata quality, identify root causes, improve processes, and build sustainable
  practices to achieve reliable AI outcomes. It will also celebrate the fort
 hcoming Spanish edition of Danette McGilvray’s book, translated by Ma
 rilu Lopez and expected in November 2026, which will expand access to this 
 important methodology for Spanish-speaking data professionals.\n\nMarilu Lo
 pez and Danette McGilvray will show a combined approach to:\n\n
 Explain why data quality is foundational to trustworthy AI.\n
 Describe what it takes to create an effective Data Strategy to drive Data Q
 uality priorities.\n
 Connect data quality practices with AI governance, risk management, and bus
 iness value.\n
 Identify common data quality issues that can affect AI outcomes.\n
 Build sustainable practices for quality data and trusted information\n\n
DTSTART:20261117T164500
SUMMARY:AI Data Quality Challenge: Strategy + Ten Steps to the Rescue
DTEND:20261117T172959
LOCATION: See Description
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