Data integration is the process of combining data from different sources into a common schema to combine information. Data in each source may be structured or unstructured, and data formats may vary as well. Integrating that data can present challenges due to lack of compatibility between systems, incompatibility between versions of software applications used in the various departments or entities, and data that has become obsolete. Data integration can be performed manually but is more often done through automated processes that require software to perform the task.
Integration is a method of combining two or more systems in order to provide access to the combined information.
Data integration deals with making connections between different data sources, such as relational databases, websites, or spreadsheets.
This is an important part of the big data technology stack, where it is sometimes called Data Access/Integration.
In addition to making connections between sources, integration can also consist of manipulating and cleansing datasets from these different sources so they can be combined together on a single platform with a common schema. The main goal of data integration is to produce a single, consistent set of integrated data from different sources.
A broad range of technologies is used for migrating and integrating data in an enterprise IT environment—from simple file transfer tools to expensive middleware products that can handle complex metadata requirements.
Data Integration enables your company to:
•Integrate all your HR, payroll, and benefits data
•Combine your customer data from all sources into a single view.
•Streamline all the technical support interactions across your organization.
•Improve sales performance with better knowledge about the status of an account or opportunity
•Create a 360° view of your customers by integrating data from multiple channels
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