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The practice of data blending involves taking data from different sources and compiling it into a single useful and standardized data set. It is a major part of strategy in the big data age, as businesses work with large and diverse volumes of data to try to define business intelligence and make decisions about enterprise.
Data blending takes place in many different ways, but it typically starts with the process of aggregating data from different sources. Experts might segment the process of data blending into three steps: the first step being data acquisition, the second step being the compilation of data, and the third step being the refinement or cleansing of data into a more consistent and accessible end result.
For example, a company may have three or four different kinds of database tables in different data centers or different parts of an IT architecture. The data blending approach would begin with intaking all of these different data from different sources and compiling it into one single database table, consolidating it into something that can be kept in a single repository.