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Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including: [1] Data transformation or data mediation between a data source and a destination
Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software .
[11] Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence ...
An integration competency center (ICC), sometimes referred to as an integration center of excellence (COE), is a shared service function providing methodical data integration, system integration, or enterprise application integration within organizations, particularly large corporations and public sector institutions.
Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as "web data".
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In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications and hard to exchange data. This frequently results in a difficult process of data mapping and cross-referencing.