Data management

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


The concept of data management arose in the 1980s as technology moved from sequential processing 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 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:
  1. Data Governance
  2. * Data asset
  3. * Data governance
  4. * Data steward
  5. * Data Ethics
  6. Data Architecture
  7. * Data architecture
  8. * Data flows
  9. Data modeling and Design
  10. Database & Storage Management
  11. * Data maintenance
  12. * Database administration
  13. * Database management system
  14. * Business continuity planning
  15. * Data subsetting
  16. Data Security
  17. * Data access
  18. * Data erasure
  19. * Data privacy
  20. * Data security
  21. Reference and Master Data
  22. * Data integration
  23. * Master data management
  24. * Reference data
  25. Data Integration and Inter-operability
  26. * Data movement
  27. * Data Interoperability
  28. Documents and Content
  29. * Document management system
  30. * Records management
  31. Data Warehousing and Business Intelligence
  32. * Business intelligence
  33. * Data analysis and Data mining
  34. * Data warehouse and Data mart
  35. Metadata
  36. * Metadata management
  37. * Metadata
  38. * Metadata discovery
  39. * Metadata publishing
  40. * Metadata registry
  41. Data Quality
  42. * Data discovery
  43. * Data cleansing
  44. * Data integrity
  45. * Data enrichment
  46. * Data quality
  47. * Data quality assurance
  48. * Secondary data


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 for their operations.

Integrated data management

Integrated data management 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: