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  • 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 [1] (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

    Topics in data management include:

        Data Governance
            Data asset
            Data governance
            Data steward
            Data Ethics
        Data Architecture
            Data architecture
            Data flows
        Data modeling and Design
        Database & Storage Management
            Data maintenance
            Database administration
            Database management system
            Business continuity planning
            Data subsetting
        Data Security
            Data access
            Data erasure
            Data privacy
            Data security
        Reference and Master Data
            Data integration
            Master data management
            Reference data
        Data Integration and Inter-operability
            Data movement (Extract, transform, load )
            Data Interoperability
        Documents and Content
            Document management system
            Records management
        Data Warehousing and Business Intelligence
            Business intelligence
            Data analysis and Data mining
            Data warehouse and Data mart
        Metadata
            Metadata management
            Metadata
            Metadata discovery
            Metadata publishing
            Metadata registry
        Data Quality
            Data discovery
            Data cleansing
            Data integrity
            Data enrichment
            Data quality
            Data quality assurance
            Secondary data

    Usage

    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) for their operations.[2]
    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.[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