Data mining makes the most sense when there are large volumes of data. In fact, most data mining algorithms require large amounts of data in order to build and train the models that will then be used to perform classification, pre diction, estimation, or other data mining tasks. A few industries, including telecommunications and credit cards, have long had an automated, interactive relationship with customers that generated Team-Fly many transaction records, but it is only relatively recently that the automation of everyday life has become so pervasive. Today, the rise of supermarket pointof-sale scanners, automatic teller machines, credit and debit cards, payper-view television, online shopping, electronic funds transfer, automated order processing, electronic ticketing, and the like means that data is being produced and collected at unprecedented rates.
Data Is Being Warehoused Not only is a large amount of data being produced, but also, more and more often, it is being extracted from the operational billing, reservations, claims processing, and order entry systems where it is generated and then fed into a data warehouse to become part of the corporate memory. Data warehousing brings together data from many different sources in a common format with consistent definitions for keys and fields. It is generally not possible (and certainly not advisable) to perform computer- and input/ output (I/O)-intensive data mining operations on an operational system that the business depends on to survive. In any case, operational systems store data in a format designed to optimize performance of the operational task. This for mat is generally not well suited to decision-support activities like data mining. The data warehouse, on the other hand, should be designed exclusively for decision support, which can simplify the job of the data miner.