The process of connecting a data field from one source to a data field in another source is known as data mapping. This decreases the risk of errors, helps standardise your data, and makes it easier to interpret your data by tying it to identities, for example.
The ultimate goal of data mapping is to merge numerous data sets into a single one. Data mapping is the process of joining multiple data sets with varied means of designating related points in a way that makes it accurate and usable at the final destination.
Any company that processes data has to know about data mapping. It's mostly used to integrate data, create data warehouses, convert data, and move data from one location to another. The process of matching data to a schema is an important aspect of any organization's data flow.
Manual mapping, semi-automated mapping, and automated mapping are the three basic strategies used in data mapping.
The rules for changing the source context to the target context are defined by data mapping. Each context is made up of one or more nodes that represent the changed data. In the mapping editor, you specify which nodes are mapped to each other by dragging and dropping.
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