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When is using data mining appropriate and not appropriate. And why might use data mining instead of other analytic methods?

Data mining is it a unique science used in the business world.
Long version: When is using data mining appropriate and not appropriate. And why might use data mining instead of other analytic methods?
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What is Data Mining?

In Business world, to take effective, accurate and efficient decisions, we need to collect and analyze data. There are several model-based analyses done in business world. This includes financial models, customer scoring systems, process optimization, forecasting, marketing analytics, etc. Data mining is not a unique science to the business world. It is just another way of accessing and analysis. With development in computing tools data mining has become quite popular as it can access and analyze a large amount of data in a matter of few milliseconds.

Data mining is the process of analyzing data from different perspectives and converting into useful information. This information is used by the business managers to take better business decisions. The decisions could be related to any aspect of the business such as to increasing revenue, cutting costs, or both. Data mining software is just one of a number of analytical tools for analyzing data. The data mining allows the users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Although the marketplace for data mining currently features a host of new products and companies, the underlying subject matter has a rich tradition of research and practice that goes back at least 30 years. The first name of data mining, in the beginning of 1960s was “Statistical analysis”. The major companies providing software or statistical analysis were SAS, SPSS and IBM. All the three companies are very active in the data-mining field too. Originally, statistical analysis consisted of classical statistical routines such as correlation, regression, chi-square and cross-tabulation.

In the late 1980s, the classical statistical analysis was augmented with more advanced techniques such as fuzzy logic, heuristics and neural networks. This area of expertise used in business is …

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