Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven 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.

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 computers, 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 community because it is supported by three technologies that are now sufficiently mature:

• Massive Data Collection
• Powerful Multiprocessor Computers
• 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 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer 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.

• Market segmentation - Identify the common characteristics of customers who buy the same products from your company.
• Customer churn - Predict which customers are likely to leave your company and go to a competitor.
• Fraud detection - Identify which transactions are most likely to be fraudulent.
• Direct marketing - Identify which prospects should be included in a mailing list to obtain the highest response rate.
• Interactive marketing - Predict what each individual accessing a Web site is most likely interested in seeing.
• Market basket analysis - Understand what products or services are commonly purchased together; e.g., beer and diapers.
• Trend analysis - Reveal the difference between a typical customer this month and last.