Chapter 5. Data-Mining Predictions

Improving human-to-computer interaction through speech processing is just one area of computing that can benefit from enhanced computing. On the other side of the interface is the backend, which usually ties in to a database. It is here that enhanced computing can help users get the most from their data.

Over the past ten years, there has been a dramatic increase in computer usage and in the number of home users. Electronic commerce has resulted in the collection of vast amounts of customer and order information. In addition, most businesses have automated their processes and converted legacy data into electronic formats. Businesses large and small are now struggling with the question of what to do with all the electronic data they have collected.

Data warehousing is a multi-billion-dollar industry that involves the collection, organization, and storage of large amounts of data. Data cubes structures comprising one or more tables in a relational database are built so that data can be examined through multiple dimensions. This allows databases containing millions of records and hundreds of attributes to be explored instantly.

Data mining is the process of extracting meaningful information from large quantities of data. It involves uncovering patterns in the data and is often tied to data warehousing because it makes such large amounts of data usable. Data elements are grouped into distinct categories so that predictions can be made about other pieces of data. For example, a bank may wish to ascertain the characteristics that typify customers who pay back loans. Although this could be done with database queries, the bank would first have to know what customer attributes to query for. Data mining can be used to identify what those attributes are and then make predictions about future customer behavior.

Data mining is a technique that has been around for several years. Unfortunately, many of the original tools and techniques for mining data were complex and difficult for beginners to grasp. Microsoft and other software makers have responded by creating easier-to-use data-mining tools. A 2004 report titled "The Golden Vein" by the Economist.com states:

As the cost of storing data plummets and the power of analytic tools improves, there is little likelihood that enthusiasm for data mining, in all its forms, will diminish.

This is the first of two chapters that will examine how a fictional retailer named Savings Mart was able to utilize Microsoft's Analysis Services, included with Microsoft SQL Server, to improve operational efficiencies and reduce costs. The present chapter will examine a standalone Windows program named LoadSampleData which is used to load values into a database and generate random purchases for several of the retailer's stores. A mining model will then be created based on shipments to each store. The mining model will be the first step toward revising the way Savings Mart procedurally handles product orders and shipments.

Chapter 6 will extend the predictions made in this chapter through the use of a Windows service designed to automate mining-model processing and the application of processing results. Finally, a modified version of the LoadSampleData program will be used to verify that Savings Mart was able to successfully lower its operating costs.

The chapter also includes a Microsoft case study which examines a real company that used Analysis Services to build a data-mining solution. In the case study, a leaser of technology equipment needed to predict when clients would return their leased equipment. By using Analysis Services, it was able to quickly build a data-mining solution that helped to reduce costs and more accurately predict the value of assets.