SPSS Rebrands Its Analytical Offerings

Predictive analytics software provider SPSS is once again expanding its offerings. In addition to adding new features to its modeling and text analytics products, the vendor is doing away with the Clementine product branding in favor of an umbrella label for its entire portfolio: PASW (predictive analytics software).

"What SPSS did in the rebranding," says Rebecca Wettemann, vice president of research for Nucleus Research, "is make it a lot easier for the customers and prospects to understand how the different pieces of technology fit together."

The new version of the SPSS modeling product -- the erstwhile Clementine -- is now known as PASW Modeler 13; its text analysis product (formerly Text Mining for Clementine) is now PASW Text Analytics 13. SPSS says that, over the course of the year, the rest of the SPSS product line will update under the PASW umbrella -- including Statistics and Data Collection.

David Vergara, director of product marketing for SPSS, explains that the change was intended to help customers and prospects understand what the products are doing and how each offering pieces together within the broader portfolio.

Aside from the name change, the new versions of SPSS products focus on usability -- and not just for data experts. Wettemann says that SPSS has "recognized that moving beyond the data analyst audience is where you get the real power." PASW Modeler 13 features a drag-and-drop interface, and functionality that will appeal to business users. Two integral updates include a "comments" tool, in which users can flag notes within the software, and automated data preparation. Data automation mitigates human error and avoids common issues in data quality.

"If you think about data mining, one of [the] most unappealing process[es] is trying to condition and get data ready where you can begin to model it," Vergara says. "Auto-data-preparation allows an analyst to condition and prepare on data quality in the click of a button." Modeler 13 also includes "auto-cluster" and integration with the still-for-now-named SPSS Statistics. Vergara says that the automated features help as a guide through the process for business users, who, as a result, can essentially use the solutions out-of-the-box to bring in and conduct analysis on unstructured textual data.

"What SPSS recognizes is that not everyone's going to be comfortable driving a Ferrari, so we need to take the power and make it more manageable and enable people to take advantage of it without having deep expertise in analytics," Wettemann says, adding that the text mining space, in particular, is new for many organizations. The potential value, though, is huge, she says.

"For the average business user, think about the amount of text and content we have in front of us every day," Wettemann says. "If we could leverage that in a meaningful way, it could be an incredible productivity boost." Referring to SPSS's breadth and depth of offerings as "strong," Wettemann nevertheless notes that the vendor's competitive circumstances are unique: SPSS is often up against, on the one hand, smaller vendors offering point solutions and, on the other, intelligence megavendor SAS, a company that also continues to bolster its analytical portfolio.

Wettemann says that, although SPSS is making progress getting average business users involved in analytics, a greater vertical focus and additional training could help users get up to speed with predictive analytics.

"Ultimately," she says, "SPSS's goal is to have a chicken in every pot and the analytics tool on every desktop. This is one more incremental step toward getting them there."

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