Data mining is a business process

Data mining by discovery and interpretation of patterns in data is:

• The use of business knowledge
• To create new knowledge
• In natural or artificial form

The most important thing for you to know about data mining is that it is a way of using business knowledge.

The process of data mining uses business knowledge to create new knowledge, and this new knowledge may be in one of the two forms. The first form of new knowledge that data mining can create is "natural knowledge", that is, knowledge sometimes referred to as insight. The second form of new knowledge that data mining can create is "artificial knowledge", that is, knowledge in the form of a computer program, sometimes called a predictive model. It is widely recognized that data mining produces two kinds of results: insight and predictive models.

Both forms of new knowledge are created through a process of discovering and interpreting patterns in data. The most well-known type of data mining technology is called a data mining algorithm. This is a computer program that finds patterns in data and creates a generalized form of those patterns called a "predictive model". What makes these algorithms (and the models they create) useful is their interpretation in the light of business knowledge. The patterns that have been discovered may lead to new human knowledge, or insight, or they may be used to generate new information by using them as computer programs to make predictions. The new knowledge only makes sense in the context of business knowledge, and the predictions are only of value if they can be used (through business knowledge) to improve a business process.

Data mining is a business process, not a technical one. All data mining solutions start from business goals, find relevant data, and then proceed to find patterns in the data that can help to achieve the business goals. The data mining process is described well by the aforementioned CRISP-DM industry standard data mining methodology, but its character as a business process has been shaped by the data mining tools available. Specifically, the existence of data mining workbenches that can be used by business analysts means that data mining can be performed by someone with a great deal of business knowledge, rather than someone whose knowledge is mainly technical. This in turn means that the data mining process can take place within the context of ongoing business processes and need not be regarded as a separate technical development. This leads to a high degree of availability of business knowledge within the data mining process and magnifies the likely benefits to the business.