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SPSS Refreshes Its Data Mining Software
Clementine is equipped with nearly 50 major enhancements spanning data preparation, graphs, modeling, output, security, performance, scoring, and more.
Posted Jan 16, 2007
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Predictive analytics software player SPSS today unveiled the latest installment of its enterprise data mining software, Clementine 11, featuring 47 major enhancements to features, including data preparation, graphs, modeling, output, security, performance, and scoring, according to the company. The release, unveiled exactly one year from the date of SPSS's announcement of version 10, is generally available. One of the data mining software's new components is its ability to integrate with the company's statistical software offerings. The release also features the addition of several new predictive algorithms providing support for credit scoring (Discriminant Function Analysis), complex pricing models (Generalized Linear Models), CRM and response modeling (Logistic Regression), forecasting (Time Series), and rule-based models that incorporate users' business knowledge, according to the company. The release also focuses heavily on graphics. "One of the things that we have seen that's very important for organizations when they are doing analytics is that the analytic group understands what the analysis is telling them," says John Held, product manager for Clementine at SPSS. "They're getting insight about customers and how various types of customers will behave in response to different marketing stimuli. But analysis groups sometimes struggle to communicate with people who are not analysts, like executives, creative marketers or call center managers." The release features a new graphics engine (along with integration capabilities) enabling users to edit and send images to more efficiently manage reporting and communication. "One of the things that customers like about the new release is that the [graphics quality] saves them time in working with the rest of their organization to help them to understand what the analysis is telling them," he says. Other core areas of improvement include strengthened transformation capabilities, more automated data cleansing, the use of optimal binning, and the use of secure socket layer (SSL) encryption and stream password protection.
SPSS, along with SAS, were the only vendors tapped as leaders in the data mining space in Gartner's "Magic Quadrant for Customer Data Mining, 1QO6," authored by Gareth Herschel, research director at Gartner and report author. SPSS "has aggressively pursued the customer data mining market," the report states. "Consider SPSS as a best-of-breed data mining vendor with a strong focus on CRM." "We've seen companies really achieve significant ROI from SPSS because the solution is relatively low cost to implement and support, and also because they can really drive a lot of top-line benefit," says Rebecca Wettemann, vice president of research at Nucleus Research. "What we'll continue to see in the data mining space is the integration of not just text data, but voice and other data, and a lot more companies taking advantage of their content assets in a way that they haven't before." Related articles: Gartner Crowns SAS and SPSS Data Mining Kings SPSS Digs Deep In the Data Mine Analytics Brought to Bear
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