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DTSTART;TZID=America/New_York:20230316T162000
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DESCRIPTION:The proliferation of data acquisition and sensor technology has
  made enormous amounts of data available to System Engineers (SE) which ca
 n facilitate real-time monitoring and enable the creation of reliability m
 odels improve operational and maintenance decisions. Gartner’s Analytics
  Ascendancy Model (AAM) identifies four types of statistical tools in an i
 ncreasing order. Starting from descriptive statistics\, it progresses to d
 iagnostic methods to predictive methods and finally prescriptive analytics
 . Implementing Knowledge Management (KM) programs can help the SE convert 
 the information from AAM into knowledge and use it to improve system perfo
 rmance. This may lead to a sustainable Machine Learning (ML) environment a
 nd autonomous system reliability adjustments.\n\nSpeaker(s): Dr. Ashraf Sa
 dek\, \n\n2000 Simcoe Street North\, Oshawa\, Ontario\, Canada
LOCATION:2000 Simcoe Street North\, Oshawa\, Ontario\, Canada
ORGANIZER:_anonimized_@_20241125_116c3eed-d082-4305-a604-10a9f339a5dc_.com
SEQUENCE:3
SUMMARY:The Use of Gartner’s Analytics Ascendancy Model to Enhance System
  Reliability: A Convergence of Data Analytics and Knowledge Management Tec
 hniques
URL;VALUE=URI:https://events.vtools.ieee.org/m/352190
X-ALT-DESC:Description: <br /><p><span class="S1PPyQ">The proliferation of 
 data acquisition and sensor technology has made enormous amounts of data a
 vailable to System Engineers (SE) which can facilitate real-time monitorin
 g and enable the creation of reliability models improve operational and ma
 intenance decisions. Gartner&rsquo\;s Analytics Ascendancy Model (AAM) ide
 ntifies four types of statistical tools in an increasing order. Starting f
 rom descriptive statistics\, it progresses to diagnostic methods to predic
 tive methods and finally prescriptive analytics. Implementing Knowledge Ma
 nagement (KM) programs can help the SE convert the information from AAM in
 to knowledge and use it to improve system performance. This may lead to a 
 sustainable Machine Learning (ML) environment and autonomous system reliab
 ility adjustments.</span></p>
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