How Large Corporations Use Data Mining to Create Value

Abstract

MOST COMPANIES HAVE DEPLOYED DATA MARTS AND DATA WAREHOUSES, BUT ABOUT 35% SAY THEY DO NOT USE DATA MINING. WE REVIEW THE DATA-MINING PROCESS, WITH A REPORT ON A SURVEY OF PRACTICES AMONG FORTUNE 500 COMPANIES AND AN EXAMPLE OF A DATA-MINING TASK. Recent literature describes several cases where managers are tapping into their corporate databases and transforming their raw data into knowledge that provides significant business intelligence and competitive advantage. This process, which companies such as Johnson & Johnson, GE Capital, Fingerhut, Procter & Gamble, and Harrah's Casino have used very effectively to create competitive intelligence, is known as data mining. (1) The data-mining process allows a company to harness data generated from normal business processes to create knowledge for solving business problems. One study reports that the payoff from an effective data-mining project can be as high as $24 million in certain companies. (2) Despite the potential of data mining, management accountants are not very familiar with its concepts, and it is seldom among the repertoire of tools used to create value for their employers. We will briefly review the data-mining process, report on a survey of data-mining practices among Fortune 500 companies, and provide an example of a data-mining task. Included are insights into (a) the functional areas within Fortune 500 companies that use data-mining techniques, (b) the reasons companies give for not using data mining, (c) the types of data-mining software companies use, (d) the data-mining techniques companies use, (e) the data sources companies use for data mining, and (f) the types of business applications for which companies use data mining. We also offer an example that shows how management accountants can use a data-mining technique to create highly intuitive guidelines to evaluate the financial health of a business. We conclude with a set of recommendations for financial professionals who want to use data mining to create business intelligence and value for their employers. THE DATA-MINING PROCESS Whether a project is simple or complex, effective data mining originates with a need for business intelligence and culminates with the creation of such intelligence. Figure 1 shows an overview of the data-mining process. [FIGURE 1 OMITTED] In practice, the goal of data mining is often modest. A successful data miner looks for a solution to a well-defined business problem, and data mining provides necessary business intelligence to solve it. After defining the problem, the data miner determines the type and scope of the data needed. Data may come from normal operational activities such as sales and marketing, procurement and logistics, production, and accounting. Data may also come from external, nonroutine sources such as government statistical sources, surveys, and commercial databases. Usually, the data miner must pool the data into a single usable data repository and then build the necessary data-mining databases from scratch or prepare the data from existing business databases. Management accountants may store data for data mining in either informal or formal data repositories. Informal data repositories include the ad hoc data files they keep to facilitate their projects. These data files typically enable a very restricted function and are maintained by a single person in an end-user-oriented application such as Microsoft Access or Excel. Formal repositories include data warehouses and data marts that managers intend to use as part of the organization's documented knowledge base. A data warehouse is a systematic repository of large volumes of data that serves as a knowledge base for a company's business decisions. Unlike operational databases that support ongoing business transactions, a data warehouse includes integrated data for customers, vendors, products, events, and transactions that span several years. …

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