Data mapping bridges the differences between two systems, or data models, so that when data is moved from a source, it is accurate and usable at the destination.
It is the translation of information between source and destination, such that the logical/semantic meanings are kept intact and the fitness of use of the destination data is enhanced.
During the data mapping process, the source data is directed to the targeted database. The target database can be a relational database or a CSV document — depending on the use case.
Data mapping allows the user to organize, distill, analyze, and understand vast amounts of data that live in various locations so he can draw conclusions and insights.
Data mapping has been a common business function for some time, but as the amount of data and sources increase, the process of data mapping has become more complex, requiring automated tools to make it feasible for large data sets. Data mapping is used in a range of use cases and industries to streamline data processes.
Data mapping
