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Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making. [ 1 ] Concept
Database theory encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems.. Theoretical aspects of data management include, among other areas, the foundations of query languages, computational complexity and expressive power of queries, finite model theory, database design theory, dependency theory, foundations ...
Master data management (MDM) is a discipline in which business and information technology collaborate to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared master data assets.
In the transitional period leading up to the strategic view of information management, Venkatraman, a strong advocate of this transition and transformation, [5] proffered a simple arrangement of ideas that succinctly brought together the management of data, information, and knowledge (see the figure) argued that:
ISO 8000 is the international standard for Data Quality and Enterprise Master Data.Widely adopted internationally [1] [2] [3] it describes the features and defines the requirements for standard exchange of Master Data among business partners.
Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. [1] Data classification is typically a manual process; however, there are tools that can help gather information about the data. [2] Data sensitivity levels are often proposed to be considered. [2]
A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. [1] The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; [2] this may lead to data being well-managed in the present ...
The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data.