Data mining is the process of extracting hidden patterns from large amounts of data. As more data is gathered, with the amount of data doubling every three years,[1] data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.

While data mining can be used to uncover hidden patterns in data samples that have been "mined", it is important to be aware that the use of a sample of the data may produce results that are not indicative of the domain.

Data mining will not uncover patterns that are present in the domain, but not in the sample. There is a tendency for insufficiently knowledgable "consumers" of the results to treat the technique as a sort of crystal ball and attribute "magical thinking" to it. Like any other tool, it only functions in conjunction with the appropriate raw material: in this case, indicative and representative data that the user must first collect. Further, the discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is representative of the whole population from which that data was drawn. Hence, an important part of the process is the verification and validation of patterns on other samples of data.

The term data mining has also been used in a related but negative sense, to mean the deliberate searching for apparent but not necessarily representative patterns in large amounts of data. To avoid confusion with the other sense, the terms data dredging and data snooping are often used. Note, however, that dredging and snooping can be (and sometimes are) used as exploratory tools when developing and clarifying hypotheses.