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1 Overview of data mining This first chapter defines data mining and sets out its main applications and contributions to database marketing, customer relationship management and other financial, industrial, medical and scientific fields. It also considers the position of data mining in relation to statistics, which provides it with many of its methods and theoretical concepts, and in relation to information technology, which provides the raw material (data), the computing resources and the commu- nication channels (the output of the results) to other computer applications and to the users. We will also look at the legal constraints on personal data processing; these constraints have been established to protect the individual liberties of people whose data are being processed. The chapter concludes with an outline of the main factors in the success of a project. 1.1 What is data mining? Data mining and statistics, formerly confined to the fields of laboratory research, clinical trials, actuarialstudiesandriskanalysis,arenowspreadingtonumerousareasofinvestigation,ranging from the infinitely small (genomics) to the infinitely large (astrophysics), from the most general (customer relationship management) to the most specialized (assistance to pilots in aviation), from the most open (e-commerce) to the most secret (prevention of terrorism, fraud detection in mobile telephony and bank card applications), from the most practical (quality control, production management) to the most theoretical (human sciences, biology, medicine and pharmacology), and from the most basic (agricultural and food science) to the most entertaining (audience prediction for television). From this list alone, it is clear that the applications of data mining and statistics cover a very wide spectrum. The most relevant fields are those where large volumes of data have to be analysed, sometimes with the aim of rapid decision making, as in the case o