【正文】 ness process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on). Some successful application areas include: ? A pharmaceutical pany can analyze its recent sales force activity and their results to improve targeting of highvalue physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include petitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a widearea work that enables the representatives to review the remendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the anization to be applied in specific sales situations. ? A credit card pany can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20fold decrease in costs for targeted mailing campaigns over conventional approaches. ? A diversified transportation pany with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this pany can build a unique segmentation identifying the attributes of highvalue prospects. Applying this segmentation to a general business database such as those provided by Dun amp。 Decision Support (1990s) What were unit sales in New England last March? Drill down to Boston. Online analytic processing (OLAP), multidimensional databases, data warehouses Pilot, Comshare, Arbor, Cognos, Microstrategy Retrospective, dynamic data delivery at multiple levels Data Mining (Emerging Today) What’s likely to happen to Boston unit sales next month? Why? Advanced algorithms, multiprocessor puters, massive databases Pilot, Lockheed, IBM, SGI, numerous startups (nascent industry) Prospective, proactive information delivery Table 1. Steps in the Evolution of Data Mining. The core ponents of data mining technology have been under development for decades, in research areas such as statistics, artificial intelligence, and machine learning. Today, the maturity of these techniques, coupled with highperformance relational database engines and broad data integration efforts, make these technologies practical for current data warehouse environments. The Scope of Data Mining Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities: ? Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive handson analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events. ? Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors. Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand plex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions. Databases can be larger in both depth and breadth: ? More columns. Analysts must often limit the number of variables they examine when doing handson analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full depth of a database, without preselecting a subset of variables. ? More rows. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population. A recent Gartner Group Advanced Technology