Resumen
The article focuses on application of data mining to business processes. The traditional approach to data analysis for decision support has been to couple domain expertise with statistical modeling techniques to develop handcrafted solutions for specific problems. More recently as of August 1, 2002, several trends have emerged to challenge this approach. One is the increasing availability of large volumes of high-dimensional data occupying database tables with millions of rows and thousands of columns. Another is the competitive demand for the rapid construction and deployment of data-driven analytics. A third is the need to give end users analysis results in a form they readily understand and assimilate, helping them gain insights they need to make critical business decisions. Moreover, knowledge discovery in databases techniques emphasizing scalable, reliable, fully automated, explanatory structures have shown that in data analysis, such structures supplement, and sometimes supplant, human-expert-intensive analytical techniques for improving decision quality.