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What is Data Integration

Encyclopedia of Information Science and Technology, Second Edition
Unifying data models and databases so that all departments of an enterprise use the same data entities, with the same values.
Published in Chapter:
Enterprise Resource Planning and Integration
Karl Kurbel (European University, Germany)
DOI: 10.4018/978-1-60566-026-4.ch221
Abstract
Enterprise resource planning (ERP) is a state-of-the-art approach to running organizations with the help of comprehensive information systems, providing support for key business processes and more general, for electronic business (e-business). ERP has evolved from earlier approaches, in particular, materials requirement planning (MRP) and manufacturing resource planning (called MRP II) in the 1980s. The focus of MRP and MRP II was on manufacturing firms. The essential problem that MRP attacked was to determine suitable quantities of all parts and materials needed to produce a given master production schedule (also called a “production program”), plus the dates and times when those quantities had to be available. Application packages for MRP have been available from the 1960s on. In the beginning, they were mostly provided by hardware vendors like IBM, Honeywell Bull, Digital Equipment, Siemens, etc. MRP was later expanded to closed-loop MRP to include capacity planning, shop floor control, and purchasing, because as Oliver Wight (1884) puts it: “Knowing what material was needed was fine, but if the capacity wasn’t available, the proper material couldn’t be produced” (p. 48). The next step in the evolution was MRP II (manufacturing resource planning). According to the father of MRP II, Oliver Wight, top management involvement in the planning is indispensable. Therefore, MRP II expands closed-loop MRP “to include the financial numbers that management needs to run the business and a simulation capability” (Wight, 1984, p. 54). Enterprise resource planning (ERP) has its roots in the earlier MRP II concepts, but it extends those concepts substantially into two directions. ERP takes into account that other types of enterprises than those producing physical goods need comprehensive information system (IS) support as well, and even in the manufacturing industry, there are more areas than those directly related to the production of goods that are critical for the success of a business.
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Process of unifying data that share some common semantics but originate from unrelated sources.
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This involves the merging and analysis of data from both environmental, social, and governance (ESG) sources and artificial intelligence (AI) systems. This process requires thorough examination and combination of the data. These include a variety of information, including environmental statistics, governance indicators, and other measures of development outcomes. In order to enhance the accuracy of predictions and evaluations in AI-powered ESG activities, it is crucial to use data of superior quality.
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Data Analysis and Integration in Healthcare
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Integration of Clinical and Genomic Data for Decision Support in Cancer
The problem of combining data residing at different sources and providing the user with a unified view of these data. This important problem emerges in several scientific domains, for example, combining results from different bioinformatics repositories.
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Data Integration: Introducing Semantics
The problem of combining data from multiple heterogeneous data sources and providing a unified view of these sources to the user. Such unified view is structured according to a global schema. Issues addressed by a data integration system include specifying the mapping between the global schema and the sources and processing queries expressed on the global schema.
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Challenges and Solutions of Real-Time Data Integration Techniques by ETL Application
The process of combining data from several sources within an organisation to produce a comprehensive, accurate, and current dataset for BI, data analysis, and other technologies and business processes is known as data integration.
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The problem of combining data residing at different sources and providing the user with a unified view of these data.
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