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DESCRIPTION:Click for Latest Location Information: http://edw2023fall.datav
 ersity.net/sessionPop.cfm?confid=157&proposalid=14531\nAt one level, Analyt
 ics is just another Data Consumer and should follow all the same DM rules a
 nd requirements as any other Data Consumer in your organization. There
  are, however, some very special factors to consider when managing data for
  advanced analytics. In this overview, we’ll talk about:\n\n
 How the need for aggregation, anonymization, and obfuscation imposes specia
 l data quality rules.\n
 How Technology and DM need extra degrees of integration for analytics.
  Are Model Explainability and Model Management the purview of technolo
 gy or DM…or both? How do we define version in the context of ma
 chine learning? Is it a form of software management or data management
 ? \n	How is bias managed? \n	How about ethics? \n
 What other special questions do we need to ask about our data management pr
 actices before we send our data to the analytics team?\n\nIn short, when di
 scussing analytics, the very boundaries of what Data Management means can b
 ecome a bit fuzzy. When we consider the various factors that go into d
 efining these boundaries, it is less important where we draw those boundari
 es than that we are clear that all the factors have been accounted for.&nbs
 p; \n
DTSTART:20230920T144500
SUMMARY:What's So Special About Analytics?
DTEND:20230920T152959
LOCATION: See Description
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