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DESCRIPTION:Click for Latest Location Information: http://dgiq2023west.data
 versity.net/sessionPop.cfm?confid=152&proposalid=14325\nWith the unceasing 
 advancement in analytics, machine learning, and artificial intelligence, ge
 tting data quality right is increasingly the secret sauce in achieving the&
 nbsp;insights that can give organizations and businesses the competitive cu
 tting edge to outperform. This session aims to:\n\n
 Provide a background in data quality and the key dimensions that underpin h
 ow data quality can be measured both qualitatively and quantitatively\
 n
 Highlight how relevant data quality issues can be identified and addressed 
 conceptually and technically\n
 Provide a step-by-step analysis and resolution of these issues using a work
 ed example as part of an end-to-end analytics workflow\n
 Evaluate the differences in results using a confusion matrix after res
 olving the data quality issues\n
 Tie up all of the above to make data quality sustainable, systema
 tic, and scalable at the enterprise-wide level \n\nParticipants w
 ill take away clear and actionable steps that they can implement on dataset
 s to improve the data quality and achieve better insights and outcomes from
  their analytics and machine learning initiatives. They will also have a go
 od high-level conceptual understanding in tackling data quality issues to l
 ead and direct efforts in enterprise and business analytics programs.\
 n
DTSTART:20230605T133000
SUMMARY:T13: Getting Data Quality Right for Analytics and Machine Learning
DTEND:20230605T164459
LOCATION: See Description
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