As more of the world is technology-driven, more and more data is available, particularly about customer behavior. Data mining seeks to use all this data to advantage, by summarizing data and applying algorithms that produce meaningful results even on large data sets.

In the midst of all this technology, though, the customer relationship still maintains its central position. After all, customers-because they provide revenue-are the one thing that businesses need to remain successful, year after year. Eventually, other funding sources dry up. No computer ever made a purchase from Amazon; no software ever paid for a Pez dispenser on eBay; no cell phone ever made an airline or restaurant reservation. There are always people, individually or collectively, on the other end.

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Data Warehousing, OLAP, and Data Mining

Since the introduction of computers into data processing centers in the 1960s, just about every operational system in business has been computerized. These automated systems run companies, spewing out large amounts of data along the way. This automation has changed how we do business and how we live: ATM machines, adjustable rate mortgages, just-in-time inventory control, online retailing, credit cards, Google, overnight deliveries, and frequent flier/buyer clubs are a few examples of how computer-based automation has opened new markets and revolutionized existing ones. This is not a new story; it has been going on for decades.

In a typical company, such systems create vast amounts of data spread through scads of disparate systems, from general ledgers to sales force automation systems, from inventory control to electronic data interchange (EDI), and so on. Data about specific parts of a business is there-lots and lots of data, somewhere, in some form. Data is available but not information-and not the right information at the right time. The goal of data warehouses is to make the right information available at the right time. Data warehousing is the process of bringing together disparate data from throughout an organization for decision-support purposes.

A data warehouse serves as a decision-support system of record, making it possible to reconcile reports because they have the same underlying source. Such a system not only reduces the need to explain disparate results, but also provides consistent views of the business across business units and time. We