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Lessons from history can inform data management and data governance professionals as well as those in the social sciences. Data governance must be able to
There are many sources of data in every organization, and many of these sources are valid. Therefore, challenges can exist about which source is the
Textual data comes in a variety of formats, which information technology (IT) specialists and end-users must be aware of and address when developing and using
Successful project managers present realistic expectations for completing any effort. Managing expectations for delivery is crucial to the perception of success, especially with business intelligence
Intelligent automation can reduce the instances and effects of automated cyber attacks As adversaries employ more complex tactics, techniques, and procedures (TTPs) to successfully evade
Is your business intelligence environment ready for safe, reliable, scalable accessibility via the Internet? How are you addressing this challenge? Typically, business intelligence (BI) environments
Master and reference data management is much more than a technical solution. It is a combination of data integration, metadata management, data governance, and enterprise
Performing and documenting data lineage can be the basis for a successful enterprise data management function Though data management and data linage may seem quite
It is important to understand the value of capturing as much detailed root data as possible to enable the development and usage of views and
Data catalogs are an essential component of every successful enterprise metadata management program Cataloging items has been a process used since the early 1900s to
It is important to determine expected usage patterns for business intelligence and analytical applications before design, and to evaluate their actual usage after implementation to
Every effective data governance program develops and maintains a mission statement to provide focus for the program’s objectives. Just as organizations require a vision for
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