The ability to create value from data is constrained by, among other things, the skill and experience of the analyst to make and verify hypotheses by sifting through large amounts of data. Data mining products attempt to overcome this limitation by using the computer, rather than humans, to discover data relationships that can be used to make predictions. Although both data mining and decision-support applications can lead to the discovery of new data relationships, data mining tools often find unsuspected relationships in data that traditional techniques will overlook. This process does not require people to create and test hypotheses about data relationships and correlations. For example, data mining can identify micro-market segments by analyzing hundreds of attributes in customer sales records rather than having a marketer rely on traditional age and income ranges.
Users can ask data mining tools to identify the variables that affect sales of a specific product.
Rather than having to guess that either cents-off coupons or weather may be the most significant indicator, say, it may really turn out that an unsuspected product placement of complementary items is most important. In this article I look at tools that do not require human initiative to predict data relationships to use the product. Table 1 compares and contrasts these technologies. Although many tools specialize in only one technique, a growing number of tools use a combination of technologies, matching the technique to the problem at hand. Several types of business problems are appropriate for data mining analysis.