Data mining, also known as 'knowledge discovery', is based on sourcing and analyzing data for research purposes. Data mining is quite common in market research, and is a valuable tool in demography and other forms of statistical analysis.
Data mining often includes association of different types and sources of data. When analyzing shoppers' buying patterns, for example, correlations are often made between types of purchase. In some cases a pattern may emerge where different types of goods are routinely bought at the same time, like lettuce and mayonnaise.
This type of information gathering and assessment often directly affects marketing strategies and in some cases even affects merchandising and packaging. For example, if lettuce and mayonnaise are routinely purchased together, it's quite likely that a retailer would provide packaged lettuce with little bottles of different types of mayonnaise, combined.
Data mining is also a scientific process, in which correlations between information can reveal previously unknown information. This is the basis of the term 'knowledge discovery'. Thanks to the ability of computers to process extremely large amounts of data, the sciences have been able to make previously unknown correlations between seemingly unrelated events and processes.
Data mining is now also a commercial service, providing useful information throughout industry and the business sector. Data mining consultants are used to analyze information in depth and provide commercial applications for what may be a very wide range of data across multiple fields.
For example, hardware chain may have information relating to sales of guttering, ladders, gloves and roofing material. Data analysis may find that many homeowners conduct repairs to their roofs and guttering during spring and summer, meaning that the hardware chain will need to stock up on those materials before those seasons.