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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).
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".
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.
System integration is defined in engineering as the process of bringing together the component sub-systems into one system (an aggregation of subsystems cooperating so that the system is able to deliver the overarching functionality) and ensuring that the subsystems function together as a system, [1] and in information technology [2] as the process of linking together different computing ...
The W3C introduced R2RML as a standard for mapping data in a relational database to data expressed in terms of the Resource Description Framework (RDF). In the future, tools based on semantic web languages such as RDF, the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process.
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 .
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. While big data is a recent phenomenon, the requirement for data to aid decision-making traces back to the early 1970s with the emergence of decision support systems (DSS).
Data Moat Large amounts of data acquired by an organization that can be harvested for sustainable, differentiating competitive advantage. [2] Deliverable(s) Finished product or outcome Downsize Reduce the number of employees through a lay-off End-user perspective Point of view of a customer about a product or service Evergreen