The act of data mining can be risky because no worthwhile activity is free of challenges and adjustments to new concepts. The most common risk is being submerged under loads and loads of data. However, some so-called risks are simply myths that should be brought to the open. It must always be remembered that data mining is a business process. It is a way of discovering configurations in your data that offers insight that you can use to make your business run more effectively and competently. In addition, as a consumer, data mining can help you make estimates in customer interactions and other business decisions. Amidst these advantages, several data mining myths have emerged and these must be dealt with properly to enlighten the minds of every businessman and client.
Myth #1: Everything that is needed for data mining is highly developed algorithms.
Sometimes a business person who encounters a typical data mining symposium or its proceedings may be misled to think that data mining is all about highly developed data analysis algorithms. One may get the impression that the better the algorithms, the better will be the data mining, where the usefulness of data mining may mean advancing his/her understanding of algorithms. This is obviously a misinterpretation of what the data mining process actually is.
Data mining is a procedure that is made up of many components like creating business goals, laying out business goals to data mining goals, getting, understanding and preprocessing the data, testing and presenting the results of analysis and setting up the results to attain business advantages. Data mining does involve new or improved algorithms but the trouble comes when data miners focus too much on the algorithms and overlook the other 90-95 percent of the data mining procedure. This false impression can be detrimental to the data mining project that may usually end up with failure to get the most from this process. There is therefore a need to broaden the understanding of the majority of clients regarding this issue.
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