Data mining has long been a way to attain high business value from corporate data. As the means of automating discovery to explore and identify new business insight, it stands alone as an access method. Interactive query or OLAP presents the measures of the business organized around its logical dimensions. Hierarchies in the dimensions allow for organized grouping and lead to drilling up and down in the data to find what you're looking for in a manual discovery process. Data mining goes further.

Much of data mining has been relegated to the domain of a special breed of experts, often holding Ph.D.s in statistics, mathematics or some scientific discipline. The mining process currently deployed in many organizations is not only time-consuming due to the challenge of the tools and the semantic gap between the business user and the statisticians, it is also noniterative in nature. Discovered nuggets are only selectively interesting and actionable. Mining tools that are interactive, visual, understandable, well-performing and work directly on the data warehouse/mart of the organization could be used by front-line workers for immediate and lasting business benefit.

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