Open main menu
The data lifecycle

Data Management comprises all disciplines related to managing data as a valuable resource.

Contents

ConceptEdit

The concept of data management arose in the 1980s as technology moved from sequential processing (first cards, then tape) to random access storage. Since it was now possible to store a discreet fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As software applications evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.

Topics in Data ManagementEdit

UsageEdit

In modern management usage, the term data is increasingly replaced by information or even knowledge in a non-technical context. Thus data management has become information management or knowledge management. This trend obscures the raw data processing and renders interpretation implicit. The distinction between data and derived value is illustrated by the information ladder.[citation needed] However, data has staged a comeback with the popularisation of the term Big data, which refers to the collection and analyses of massive sets of data.

Several organisations have established data management centers (DMC)[1] for their operations.

Integrated data managementEdit

Integrated data management (IDM) is a tools approach to facilitate data management and improve performance. IDM consists of an integrated, modular environment to manage enterprise application data, and optimize data-driven applications over its lifetime.[2][3][4][5] IDM's purpose is to:

  • Produce enterprise-ready applications faster
  • Improve data access, speed iterative testing
  • Empower collaboration between architects, developers and DBAs
  • Consistently achieve service level targets
  • Automate and simplify operations
  • Provide contextual intelligence across the solution stack
  • Support business growth
  • Accommodate new initiatives without expanding infrastructure
  • Simplify application upgrades, consolidation and retirement
  • Facilitate alignment, consistency and governance
  • Define business policies and standards up front; share, extend, and apply throughout the lifecycle

See alsoEdit

ReferencesEdit

  1. ^ For example: Kumar, Sangeeth; Ramesh, Maneesha Vinodini (2010). "Lightweight Management framework (LMF) for a Heterogeneous Wireless Network for Landslide Detection". In Meghanathan, Natarajan; Boumerdassi, Selma; Chaki, Nabendu; Nagamalai, Dhinaharan (eds.). Recent Trends in Networks and Communications: International Conferences, NeCoM 2010, WiMoN 2010, WeST 2010,Chennai, India, July 23-25, 2010. Proceedings. Communications in Computer and Information Science. 90. Springer. p. 466. ISBN 9783642144936. Retrieved 2016-06-16. 4.4 Data Management Center (DMC)[:] The Data Management Center is the data center for all of the deployed cluster networks. Through the DMC, the LMF allows the user to list the services in any cluster member belonging to any cluster [...].
  2. ^ Integrated Data Management: Managing data across its lifecycle by Holly Hayes
  3. ^ Organizations thrive on Data by Eric Naiburg
  4. ^ Fragmented Management Across The Data Life Cycle Increases Cost And Risk[permanent dead link] - A commissioned study conducted by Forrester Consulting on behalf of IBM October 2008
  5. ^ integrated IBM Data Management information center

External linksEdit