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【正文】 plied to improve business processes throughout the anization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure 1 illustrates an architecture for advanced analysis in a large data warehouse. Figure 1 Integrated Data Mining Architecture The ideal starting point is a data warehouse containing a bination of internal data tracking all customer contact coupled with external market data about petitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access. An OLAP (OnLine Analytical Processing) server enables a more sophisticated enduser business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROIfocused business analysis directly into this infrastructure. An advanced, processcentric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the anization can continually mine the best practices and apply them to future decisions. This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans. Profitable Applications A wide range of panies have deployed successful applications of data mining. While early adopters of this technology have tended to be in informationintensive industries such as financial services and direct mail marketing, the technology is applicable to any pany looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, wellintegrated data warehouse and a welldefined understanding of the business 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。 may carry important information. parallel processing The coordinated use of multiple processors to perform putational tasks. Parallel processing can occur on a multiprocessor puter or on a work of workstations or PCs. predictive model A structure and process for predicting the values of specified variables in a dataset. prospective data analysis Data analysis that predicts future trends, behaviors, or events based on historical data. RAID Redundant Array of Inexpensive Disks. A technology for the efficient parallel storage of data for highperformance puter systems. retrospective data analysis Data analysis that provides insights into trends, behaviors, or events that have already occurred. rule induction The extraction of useful ifthen rules from data based on statistical significance. SMP Symmetric multiprocessor. A type of multiprocessor puter in which memory is shared among the processors. terabyte One trillion bytes. time series analysis The analysis of a sequence of measurements made at specified time intervals. Time is usually the dominating dimension of the data.
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