【正文】 heir data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledgedriven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most panies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought online. When implemented on high performance client/server or parallel processing puters, data mining tools can analyze massive databases to deliver answers to questions such as, Which clients are most likely to respond to my next promotional mailing, and why? This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users. The Foundations of Data Mining Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on puters, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business munity because it is supported by three technologies that are now sufficiently mature: ? Massive data collection ? Powerful multiprocessor puters ? Data mining algorithms Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of In some industries, such as retail, these numbers can be much larger. The acpanying need for improved putational engines can now be met in a costeffective manner with parallel multiprocessor puter technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods. In the evolution from business data to business information, each new step has built upon the previous one. For example, dynamic data access is critical for drillthrough in data navigation applications, and the ability to store large databases is critical to data mining. From the user’s point of view, the four steps listed in Table 1 were revolutionary because they allowed new business questions to be answered accurately and quickly. Evolutionary Step Business Question Enabling Technologies Product Providers Characteristics Data Collection (1960s) What was my total revenue in the last five years? Computers, tapes, disks IBM, CDC Retrospective, static data delivery Data Access (1980s) What were unit sales in New England last March? Relational databases (RDBMS), Structured Query Language (SQL), ODBC Oracle, Sybase, Informix, IBM, Microsoft Retrospective, dynamic data delivery at record level Data Warehousing amp。t know the long distance calling usage of these prospects (since they are most likely now customers of your petition). You39。 Bradstreet can yield a prioritized list of prospects by region. ? A large consumer package goods pany can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and petitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments. Each of these examples have a clear mon ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These anizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them. Conclusion Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific enduser problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are