This is why data mining is dead: it died of a broken heart. It was killed by disappointed expectations. In addition to a perfect storm of tough economic times, another reason data mining technology has not lived up to its promise is that "data mining" is a vague and ambiguous term. It overlaps with data profiling, data warehousing, and even such approaches to data analysis as online analytical processing (OLAP) and enterprise analytic applications. When high profile successes have occurred (e.g., a front-page article in the Wall Street Journal, "Lucky Numbers: Casino Chain Mines Data on Its Gamblers, And Strikes Pay Dirt" by Christina Binkley, May 4, 2000), they have been a mixed blessing. Such results have attracted a variety of imitators with claims, solutions and products that ultimately fall short of the promises. The promises build on the mining metaphor and typically are made to sound like easy money. This has resulted in all the usual dilemmas of confused messages from vendors, hyperbole in the press and disappointed end-user enterprises.
Data mining is regrouping as "predictive analytics." The differentiators are summarized in Figure 1.
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