Difference between revisions of "Data Management"
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“The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”<ref name=":0"><nowiki>https://dama.org/content/what-data-management</nowiki></ref> | “The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”<ref name=":0"><nowiki>https://dama.org/content/what-data-management</nowiki></ref> | ||
| − | Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.<ref | + | [https://www.gartner.com/en/information-technology/glossary/dmi-data-management-and-integration Gartner] defines Data management as: |
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| + | Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.<ref><nowiki>https://www.gartner.com/en/information-technology/glossary/dmi-data-management-and-integration</nowiki></ref> | ||
| + | <references /> | ||
Revision as of 08:19, 30 March 2020
The Data Management Body of Knowledge (DMBOK) refers to Data Management as:
“The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”[1]
Gartner defines Data management as:
Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.[2]
