Data mining services are offered by companies that specialize in data mining. The process consists of analyzing information such as records or receipts, often with the aid of a data mining application. Various entities, such as certain businesses or government agencies, use the analysis to make decisions, allocate resources and cut wasteful practices. Researchers also use data mining to do scientific research in fields such as disease management and genetics. Another application of data mining is grouping similar items of information together, which allows consumers to make buying decisions.
With the increasing use of computers, data mining services became more important for two reasons. First, the large amount of information stored in computer databases became impossible to manually evaluate. Second, the ability to program computers to analyze large amounts of data made it possible to sift through the information looking for patterns and associations in a way that was not previously possible. This has allowed many businesses and governments to transform the way they operate.
Typically, data mining services use a multistage process. The first step involves determining what data will be analyzed. It is considered important to have sufficient data for an accurate result while avoiding extraneous data that will increase the cost, time and effort required. The goal of the data mining should usually be clearly defined before starting the process. Many institutions that use data mining services often choose to structure their databases so that they can more easily carry out future data mining.
Data cleaning is the next stage in the process. This involves removing or correcting inaccurate information. It also may be necessary to add data if the initial data is incomplete. Redundant data may also be discarded. Data cleaning may also cut down the expense of analyzing the data by reducing the processing power of the computers needed.
The next step is to analyze the data and determine the best mathematical model to use in analyzing it. A crucial part of data mining involves using the model to make predictions of already known results. If these predictions are accurate, it is more likely that the model is correct. Incorrect results require the mathematical model to be altered or discarded. Once a suitable model has been found and tested against real-world results, the results are formatted into a form, such as a graph, easily readable by human beings.
