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Exploratory Analysis for Efficient Data Mining Thomas H. Burger, Eli Lilly and Company, Indianapolis, IN Loren W. Burger, Jr., Mississippi State University, Starkville, MS Abstract Competitive advantage requires rapid formation of sound inferences for strategic information delivery. Key to knowledge formation is data analysis and presentation. Exploratory analysis reveals relationships aiding selection of sites for data mining. Mining reveals unknown relationships through methodological data selection, transformation, exploration and modeling. Further, it enables hypothesis testing and recognition of patterns for decision support. SAS Institute (SI) advocates a framework (SEMMA) for exploration and mining which systematizes the activities of sampling, exploring, modifying, modeling and assessing as an iterative stepwise process. Data exploration and mining can be accomplished with code- or macro-based approaches. However, a tool-based approach delivered in a point-and-click interface radically accelerates this process, speeding knowledge acquisition and inference formation. Previously restricted to those with programming expertise, SAS Enterprise Minerï›› (EM) now enables non-programmers to mine. However, the need for reliable knowledge and sound inferences dictates that statistical experts guide its use. We consider issues in tool use for exploration when conceptualizing a data mining strategy. -Paper will be provided during presentation-