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DESCRIPTION:Click for Latest Location Information: http://edw2021.dataversi
 ty.net/sessionPop.cfm?confid=133&proposalid=12784\nDue to decentralized dat
 a storage systems, an organization may have to face disparate implemen
 tations governance processes and measures, for security and privacy protect
 ion. In many instances, data scientists must copy datasets to their computi
 ng environments in order to perform data analytics and statistical modeling
 . Clearly, this approach has led to many datasets and their copies that are
  managed in data silo environments. \nThis state of affairs presents c
 hallenges that can prevent:\n\n
 The orchestration and collaboration of multiple key transformational data m
 anagement initiatives\n
 The assurance of a consistent approach for data management strategy that in
 cludes data governance, secure access to data, data provisioning, metadata 
 standardization, and data discovery\n
 The establishment of an overall vision to leverage distributed computation 
 platforms based on advanced technologies such as Hadoop, Apache Spark, Data
 bricks, etc.\n\nIn this presentation, we will lay out an enterprise-wide fr
 amework and architecture for Data Management and Governance that covers the
  entire data lifecycle from acquisition to disposition. At the foundation, 
 is the standardized framework for metadata at two levels: an operation
 al level to support critical business operations such as system interface m
 anagement, and data lineage reporting and at a business level to 
 deliver cataloging, access authorization search, and discovery of datasets.
 \nIn terms of architecture, the core component is the Enterprise Data Lake,
  which is flexible, modular, and capable of evolving with the Bureau’
 s business needs such that:\n\n
 Best-fit technology for various architecture components serving the ne
 eds of the business operations can be utilized.\n
 Ingress and egress of data from the lake can be done seamlessly within a hy
 brid environment spanning cloud and traditional data centers\n
 Data analysis and statistical modeling utilizing advanced tools and technol
 ogies can be performed on-demand by users\n\nFinally, the approach includes
  an overarching mechanism that tracks the usage of sensitive data with audi
 t and reporting capabilities throughout the entire lifecycle of the data.&n
 bsp;
DTSTART:20210423T080000
SUMMARY:Ensuring Data Protection and Enabling Data Analytics
DTEND:20210423T084959
LOCATION: See Description
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