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DTSTAMP:20260722T235820Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16729\nMost organization
 s measure data quality. Fewer design metrics that consistently drive action
 .\n\nFrom completeness scores to accuracy percentages, data quality program
 s often focus on reporting metrics rather than enabling decisions. While da
 shboards and scorecards provide visibility, they frequently fall short in i
 nfluencing behavior, ownership, and meaningful improvement.\n\nThis session
  focuses on how to design data quality metrics that move beyond passive rep
 orting to actively driving accountability and outcomes. We will explore how
  to distinguish between informational and actionable metrics, align measure
 s to ownership and escalation paths, and integrate data quality signals int
 o operational workflows.\n\nAttendees will learn practical design principle
 s for creating metrics that trigger action, support governance objectives, 
 and scale across distributed teams. The session will emphasize repeatable p
 atterns that can be applied regardless of organizational size or tooling ma
 turity.\n\nLearning Objectives \n\n
 Understand the characteristics of data quality metrics that drive action ve
 rsus those that only provide visibility\n
 Identify and redesign common “vanity metrics” into actionable, 
 decision-supporting measures\n
 Learn how to align data quality metrics with ownership, accountability, and
  escalation processes\n
 Apply design approaches that integrate data quality metrics into operationa
 l workflows rather than static reporting\n
DTSTART:20261117T110000
SUMMARY:Your Data Quality Metrics Are Lying to You: Designing Metrics That 
 Actually Drive Action
DTEND:20261117T234459
LOCATION: See Description
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