Data mining was considered an esoteric activity, confined largely to academic circles and research departments within large corporations. Data mining is now big business. One reason that data mining has taken some time to find its way into mainstream IT departments is that it is so quantitatively focused. The tools that have been available until recently required a strong understanding of statistics and other linear and nonlinear methods of data modeling and pattern recognition. And talent in this field is scarce and expensive.
Data mining is an interdisciplinary field, with strong quantitative roots.
People have come into this field with varying backgrounds in neural networks, statistical analysis, evolutionary computation, fuzzy logic, decision trees, memory-based reasoning, and genetic algorithms. It's all about dealing with probabilities and helping companies minimize the risks involved in making a particular decision. Indeed, it is the potential for the applications of these technologies that now has companies deeply interested in this area. The retail and financial industries have been among the first to embrace data mining technologies, first with the analysis of data in large corporate data warehouses, and more recently, in the analysis of online Web-based activities. Grocery stores, retailers, and consumer packaged goods companies have long been purchasing data from information providers such as IRI and Neilson.