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This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users.\r\nData integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation. Data integration ultimately enables analytics tools to produce effective, actionable business intelligence.\r\nThere is no universal approach to data integration. However, data integration solutions typically involve a few common elements, including a network of data sources, a master server, and clients accessing data from the master server.\r\nIn a typical data integration process, the client sends a request to the master server for data. The master server then intakes the needed data from internal and external sources. The data is extracted from the sources, then consolidated into a single, cohesive data set. This is served back to the client for use.","materialsDescription":" <span style=\"font-weight: bold;\">Integration helps businesses succeed</span>\r\nEven if a company is receiving all the data it needs, that data often resides in a number of separate data sources. For example, for a typical customer 360 view use case, the data that must be combined may include data from their CRM systems, web traffic, marketing operations software, customer — facing applications, sales and customer success systems, and even partner data, just to name a few. Information from all of those different sources often needs to be pulled together for analytical needs or operational actions, and that can be no small task for data engineers or developers to bring them all together.\r\nLet’s take a look at a typical analytical use case. Without unified data, a single report typically involves logging into multiple accounts, on multiple sites, accessing data within native apps, copying over the data, reformatting, and cleansing, all before analysis can happen.\r\nConducting all these operations as efficiently as possible highlights the importance of data integration. 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Data integration needs to be collaborative and unified in order to improve collaboration and unification across the organization.\r\n<span style=\"font-style: italic;\"><span style=\"font-weight: bold;\">Saves time and boosts efficiency</span></span>\r\nWhen a company takes measures to integrate its data properly, it cuts down significantly on the time it takes to prepare and analyze that data. The automation of unified views cuts out the need for manually gathering data, and employees no longer need to build connections from scratch whenever they need to run a report or build an application.\r\nAdditionally, using the right tools, rather than hand-coding the integration, returns even more time (and resources overall) to the dev team.\r\nAll the time saved on these tasks can be put to other, better uses, with more hours earmarked for analysis and execution to make an organization more productive and competitive.\r\n<span style=\"font-style: italic;\"><span style=\"font-weight: bold;\">Reduces errors (and rework)</span></span>\r\nThere’s a lot to keep up with when it comes to a company’s data resources. To manually gather data, employees must know every location and account that they might need to explore — and have all necessary software installed before they begin — to ensure their data sets will be complete and accurate. If a data repository is added, and that employee is unaware, they will have an incomplete data set.\r\nAdditionally, without a data integration solution that synchronizes data, reporting must be periodically redone to account for any changes. With automated updates, however, reports can be run easily in real time, whenever they’re needed.\r\n<span style=\"font-style: italic;\"><span style=\"font-weight: bold;\">Delivers more valuable data</span></span>\r\nData integration efforts actually improve the value of a business’ data over time. 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It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users.\r\nData integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation. Data integration ultimately enables analytics tools to produce effective, actionable business intelligence.\r\nThere is no universal approach to data integration. However, data integration solutions typically involve a few common elements, including a network of data sources, a master server, and clients accessing data from the master server.\r\nIn a typical data integration process, the client sends a request to the master server for data. The master server then intakes the needed data from internal and external sources. The data is extracted from the sources, then consolidated into a single, cohesive data set. This is served back to the client for use.","materialsDescription":" <span style=\"font-weight: bold;\">Integration helps businesses succeed</span>\r\nEven if a company is receiving all the data it needs, that data often resides in a number of separate data sources. For example, for a typical customer 360 view use case, the data that must be combined may include data from their CRM systems, web traffic, marketing operations software, customer — facing applications, sales and customer success systems, and even partner data, just to name a few. Information from all of those different sources often needs to be pulled together for analytical needs or operational actions, and that can be no small task for data engineers or developers to bring them all together.\r\nLet’s take a look at a typical analytical use case. Without unified data, a single report typically involves logging into multiple accounts, on multiple sites, accessing data within native apps, copying over the data, reformatting, and cleansing, all before analysis can happen.\r\nConducting all these operations as efficiently as possible highlights the importance of data integration. It also showcases the major benefits of a well thought-out approach to data integration:\r\n<span style=\"font-style: italic;\"><span style=\"font-weight: bold;\">Improves collaboration and unification of systems</span></span>\r\nEmployees in every department — and sometimes in disparate physical locations — increasingly need access to the company's data for shared and individual projects. IT needs a secure solution for delivering data via self-service access across all lines of business.\r\nAdditionally, employees in almost every department are generating and improving data that the rest of the business needs. Data integration needs to be collaborative and unified in order to improve collaboration and unification across the organization.\r\n<span style=\"font-style: italic;\"><span style=\"font-weight: bold;\">Saves time and boosts efficiency</span></span>\r\nWhen a company takes measures to integrate its data properly, it cuts down significantly on the time it takes to prepare and analyze that data. 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