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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
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.
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]
Codd's twelve rules [1] are a set of thirteen rules (numbered zero to twelve) proposed by Edgar F. Codd, a pioneer of the relational model for databases, designed to define what is required from a database management system in order for it to be considered relational, i.e., a relational database management system (RDBMS).
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 ...
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 mesh is based on four core principles: [17] Domain ownership; Data as a product [18]; Self-serve data platform; Federated computational governance; In addition to these principles, Dehghani writes that the data products created by each domain team should be discoverable, addressable, trustworthy, possess self-describing semantics and syntax, be interoperable, secure, and governed by ...