BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//hacksw/handcal//NONSGML v1.0//EN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260422T090838Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2025.data
 versity.net/sessionPop.cfm?confid=164&proposalid=15914\nSupporting analytic
 s and data science in an enterprise involves more than building a data lake
  or using cloud services. Too often, the focus is on technology when it sho
 uld be on data practices and management. This session discusses architectur
 e principles, the relationship between governance and architecture, and the
  data architecture and data management needed to support multiple competing
  enterprise workloads.\n\nThe focus in our market has been on acquiring tec
 hnology, and that ignores the more important part: the ecosystem within whi
 ch this technology exists and the data architecture that lies at its core. 
 If one expects longevity from data infrastructure, then it should be a desi
 gned rather than accidental architecture.\n\nArchitecture starts with uses 
 and includes the data, technology, methods of building and maintaining, gov
 ernance, and organization of people. What are the design principles that le
 ad to good design and data architecture? What assumptions limit older appro
 aches? How can one modernize an existing data environment? How will this af
 fect data management? This session will help you answer these question
 s.\n\nYou will learn:   \n\n
 How data architecture constrains both governance and technology architectur
 e\n
 Analytic workloads and their impact on data architecture and technology cho
 ices\n
 How to separate infrastructure from application in your data systems\n
 Tradeoffs and considerations in multi-use data infrastructure\n
 When to standardize data, how to curate it, and govern its delivery and use
  over time\n
DTSTART:20250508T083000
SUMMARY:W4: Data Architecture to Modernize Analytics and Data Science
DTEND:20250508T155959
LOCATION: See Description
END:VEVENT
END:VCALENDAR