- Data Management comprises all disciplines related to managing data as a valuable/vital resource.
The concept of data management arose in the 1980s as technology moved from sequential processing  (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete 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 application software 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 management include:
Data modeling and Design
Database & Storage Management
Database management system
Business continuity planning
Reference and Master Data
Master data management
Data Integration and Inter-operability
Data movement (Extract, transform, load )
Documents and Content
Document management system
Data Warehousing and Business Intelligence
Data analysis and Data mining
Data warehouse and Data mart
Data quality assurance
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. 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) for their operations.
Integrated data management
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. 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