ABSTRACT
This chapter discusses traditional data storing as well as data storing in the age of big data and the different steps that are necessary for data integration. It also discusses the process of creating marketing variables out of the different available data items within the commercial data environment. The first step in the process of data integration is called ETL; it is the process of extraction, transformation, and loading the input data sources into the data warehouse. The ETL steps described when discussing traditional data storage are typical for a conventional data warehouse in a commercial setting. Data sources vary in terms of content, scaling, source, and presence within the commercial data environment. The input data sources to be considered for integration were customer data, market performance data, brand performance data, net promoter score data, and pricing data.
