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KXEN Brings Data Mining to Operations
Simplification of data mining brings sophistication to business.
October 27, 2004
3 Min Read
Data mining software has yet to gain the widespread adoption of other BI software because of its complexity and skill set requirements. Current leading tools used to mine data can be cumbersome because of limited automation of data massaging. KXEN's Analytic Framework 3.0 offers differentiated enhancements that can speed delivery of data mining models and reports. These enhancements address data preparation, predictive model development, and predictive model deployment. Ventana Research recommends the application-centric deployment of data mining like KXEN, where the process of gaining results is simplified and leveraged more effectively.
The purchase and use of data mining software has traditionally required a statistician be shrink-wrapped with the package at time of purchase so the software can be used effectively. As with other promising but complex technologies, data mining software has yet to overcome user approachability issues that bar the way to widespread use. The challenge is that data mining depends on sophisticated concepts and beliefs to provide value, and business people are often a keep-it-simple, skeptical bunch. KXEN approaches the marketing of its Analytic Framework 3.0 software with these issues fully in mind. While continuing to provide a flexible data mining workbench for statistics professionals, KXEN is working toward delivering software that is accessible to non-statisticians. Its approach to providing increased ease-of-use stems from both underlying technology differentiators and artful marketing. Data mining is a process that can be broken down into three basic activities: data preparation, predictive model development, and predictive model use. If certain issues are ignored in any of these activities, analytic outcomes can be negatively impacted. KXEN approaches ease-of-use by eliminating the risk of overlooking issues through built-in and automated functionality.
KXEN's approach will require some assistance from a sophisticated analyst who can understand the applicability of the supplied modeling alternatives. KXEN has provided some good methods to ease data preparation through the application of specific models and model creation, providing organizations the ability to reduce costs and apply data mining. KXEN has made data mining simple — not a rocket science project like many of the high-end tools in the market — and has made the science a practical application for most organizations.
KXEN's Analytic Framework 3.0 provides some significant new benefits to the field of data mining and analysis. These benefits provide value to both data mining practitioners and business managers. KXEN has made progress toward fire-and-forget data mining, but we aren't completely there yet. And SAS, Fair Isaac and other leading analytics vendors have large and loyal installed bases that will want to survive regardless of any innovations toward end user self-serve data mining.
Ventana Research recommends business managers start with the specific analytic questions they need to have answered. They should then consult with their IT and statistics departments to see if obtaining those answers require data mining technology. Those IT and statistics professionals can then provide help in understanding how to apply data mining technology. Ror data mining professionals who want to improve their productivity, KXEN is well worth evaluating. Business managers who recognize potential benefits for their business from data mining and application developers looking to embed data mining should include KXEN in their technology evaluations.
Eric Rogge is VP & Research Director - Business Intelligence & Performance Management at Ventana Research (www.ventanaresearch.com), a research and advisory services firm.
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