What sorts of real-world situations defy data mining? The most obvious would be problems featuring data that is too small, too narrow, too noisy or of too little relevance to allow effective modeling. Organizations which have not maintained good records, which still rely on non-computer procedures and those with too little history are good examples. Even within very large organizations which collect and store enormous databases, there may be no relevant data for the problem at hand (for instance, when a new line of business is being opened, or new products introduced). It is surprising how often business people expect to extract value from a situation when they have failed to invest in appropriate data gathering.
Another large area with minimal data mining potential is organizations whose basic business process is so fundamentally broken that the usual decision making procedures have failed to do the usual "heavy lifting". Any of us can easily recall experiences in retail establishments whose operation was so flawed that it was obvious that the profit potential was not nearly being exploited. Data mining cannot fine tune a process which is so far gone. No amount of quantitative analysis will fix unkept shelves, weak product offering or poor employee behavior.
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