Powerful systems for collecting data and managing it in large databases are already in place in most large and medium-scale companies. However, the bottleneck of turning this data into your success is the difficulty of extracting knowledge about the system that you study from the collected data.
Below are all the questions that can probably be answered if information hidden among megabytes of data in your database can be found explicitly and utilized.
What goods should be promoted to this customer? What is the probability that a certain customer will respond to a planned promotion? Can one predict the most profitable securities to buy/sell during the next trading session? Will this customer default on a loan or payback on schedule? What medical diagnosis should be assigned to a particular patient? How large are the peak loads of a telephone or energy network going to be? Why does the facility suddenly start to produce defective goods? Modeling the investigated system and discovering relations that connect variables in a database are the subjects of data mining.
Modern data mining system self learn from the previous history of the investigated system, formulating and testing hypotheses about the rules, which this system obeys. When concise and valuable knowledge about the system of interest had been discovered, it can and should be incorporated into some decision support system, which helps the manager to make wise and informed business decisions.
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