Data Analysis for Small Firms / SOHO

In chapter 11 Sousa and Oz (2015) discuss the various ways of processing data and gleaning meaning, trends, and patterns within the data.  These methods go by the names of Business Intelligence (BI), Online Analytical Processing (OLAP) Data Warehousing, and Data Marts (Sousa & Oz, 2015), I have a bit of experience in Microsoft Access and SQL Server databases, and although what I have done may not be as sophisticated as some of the applications in chapter 11, they have served their purpose.

For instance, a friend of mine wanted to send out a postal mailing for a special offer.  He wanted the offer to be available to his best previous customers in a four-state area in order to minimize shipping costs.  He has data available from his online store.  So we downloaded the data, and imported it into Microsoft Access.  In slicing and dicing the data, it became apparent that the data was not "clean."  In other words, it was not normalized.  By sorting on the criteria of last name, first name, ZIP code it became readily apparent that some of those who we thought were "good" customers were actually "great" customers.  Other customers, those who appeared in the data to have just purchased small items here and there were actually "great" customers as well, they made small purchases, yet they made several purchases which added up to a substantial sum over the course of a few years.  This is an example of how even small companies can mine their data to realize a benefit with desktop database tools. 

Reference

Sousa, K.J., & Oz, E. (2015). Management information systems (7th ed.). Stamford, CT: Cengage Learning.