Data Mining for Business Intelligence

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We are seeing today widespread and explosive use of database technology to manage large volumes of business data. The use of database systems in supporting applications that employ query based report generation continues to be the main traditional use of this technology. However, the size and volume of data being managed raises new and interesting issues. Can we utilize methods wherein the data can help businesses achieve competitive advantage, can the data be used to model underlying business processes, and can we gain insights from the data to help improve business processes? These are the goals of Business Intelligence (BI) systems, and Data Mining is the set of embeddable (in BI systems) analytic methods
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These will no longer suffice, however. The CIMA Forum, a network of senior finance personnel from major organizations, believes that management accountants have a much bigger contribution to make.

Data Mining:
Data mining enables users to discover hidden patterns without a predetermined idea or hypothesis about what the pattern may be. The data mining process can be divided into two categories: discovering patterns and associations, and predicting future trends and behaviors using the patterns. The power of data mining is evident as it can bring forward patterns that are not even considered by the user to search for. Hence, you have the answer to a question that was never asked. This is especially helpful when you are dealing with a large database where there may be an infinite number of patterns to identify. Interesting to note is the fact that “the more data in the warehouse, the more patterns there are, and the more data we analyze the fewer patterns we find.” What this means is that when there is richness of data and data patterns, it may be best to data mine different data segments separately, so that the influence of one pattern does not dilute the effect of another pattern in a large database.
Currently, due to significant advances in data acquisition technologies, increasing importance of data asset, customer and harsh competition, there is an exponential growth of data volume. Companies are overloaded with huge repository of data
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