The simple explanation of data mapping is that it is the process of creating data element mappings that exist between two different data models. Data mapping is a part of the data integration process. Data mapping is a part of data integration including data transformation or data mediation, which takes place between a data source and the data destination. Data mapping is also a part of the identification of data existing as a data lineage analysis, and hidden sensitive data. Data mapping is used by a lot of different people and companies for a variety of reasons. Data mapping is a good way to transmit and receive purchases, invoices, and creating data maps for purchase orders. Data mapping is being used more and more between companies and data mapping is even being used in inter company department as a part of communications.



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Many people deal with data mapping every day without even realizing it. For instance, data mapping is utilized when a company would like to send and receive purchases. Data mapping could be used to create the data maps from the company's standard ANSI messages. Data mapping can be used for purchase orders, invoices, and other company messages.

There are different ways to go about data mapping. Hand coded or graphical manual data mapping is one way in which data mapping can be done. All data mapping is done via procedural code, creating XSLT transforms or by using graphical mapping tools. These data mapping tools will automatically generate programs that can be transformed. These data mapping tools are graphical in nature and will allow the data mapping user to draw lines from a filed in one type of data to fields in another type of data. These data mapping transformation programs are generally created in SQL, XSL, Java, or C++.

There are data driven mapping techniques, which is one of the newest data mapping techniques. Data driven data mapping requires one to evaluate actual data values into two different data sources at the same time. Many people like this type of data mapping technique because it allows for data exceptions that generally would not be discovered through transformation logic.

Semantic mapping is another approach and it is not unlike the auto connect features that graphical data mapping users utilize. The difference between this type of data mapping and graphical data mapping is that with semantic data mapping a metadata registry can be used to inquire about different data element synonyms. There are limitations to semantic data mapping though, as this sort of data mapping will only find exact matches between columns of data and is not able to locate any transformation logic or even any exceptions between columns.

Those that do data mapping usually have their preferred way to go about it. Some people like one type of data mapping better than others, and other professionals will tell you that the method of data mapping that they choose truly depends on the type of document and document language that they are dealing with. Having experience in all data mapping varieties is a great asset as it will allow the data mapping specialist to call on all of their data mapping experience to make each data mapping experience as streamlined as possible.