RESEARCH & RESOURCES

SPSS 16 Pushes Deeper into the Enterprise

As last week’s SPSS 16 announcement makes clear, the company wants to push even deeper into the enterprise

Statistical analysis and business intelligence (BI) powerhouse SPSS Inc. often gets overshadowed by arch-rival SAS Institute Inc., which—at $2 billion in annual revenues and leadership in a number of BI market segments (including the lucrative ETL space)—is a much bigger company. The irony, of course, is that SPSS is actually the more venerable of the two vendors.

While SAS has been making considerable noise about its enterprise BI and performance management (PM) ambitions, SPSS has likewise fixed its focus on both markets. Over its last several iterations, that company’s flagship BI and statistical analysis platform, SPSS, has incorporated a number of enterprise-oriented BI amenities, including a push toward user self-service.

With last week’s SPSS 16 announcement, the company made clear that it hopes to build on this feature set and push even deeper into the enterprise. Thanks to its suddenly sexy predictive analytic credentials, SPSS may have the momentum it needs to do just that.

Last year, SPSS shipped SPSS 15, arguably its most important platform release in years. With improved usability features, enhanced report formatting capabilities, and strengthened support for scripting (as well as script-driven transformations), officials were optimistic that the then-new release would help take SPSS mainstream—and deeper into the enterprise. "This is where you’ll see [SPSS] going from more of a desktop tool really to more of an enterprise-level application," said SPSS director of product marketing Kyle Weeks, at the time.

One year later, Weeks reports that SPSS is seeing encouraging growth in the enterprise market, although he downplays talk of an enterprise singularity—i.e., of a sudden surge in enterprise SPSS deployments.

"In terms of the impact from SPSS 15, I think we’re seeing definite traction there. It’s more of a journey really than an event, in that these things don’t happen necessarily at the drop of a hat," he comments.

"I think one of the things we’re seeing from the SPSS user base is that as those [existing and new] customers grow and evolve, we’re really seeing a convergence or a synergy of those two things. They have certain needs and our technology and our platform are emerging to meet those needs and they’re coinciding."

Singularity or no, SPSS appears to be on a roll. Between 2005 and 2006, for example, its revenues grew by about 12.3 percent—on par with red-hot market leaders such as Hyperion, and ahead of the overall BI market (the size of which increased by 11.5 percent, according to IDC).

Advanced analytics is still SPSS’ most bankable technology asset: the company derives close to 90 percent of its revenues from sales of advanced analytics software, per IDC’s numbers; that makes it the second largest advanced analytics player in the business, behind only SAS.

At the same time, IDC notes, SPSS’ emphasis on "predictive analytics"—a more enterprise-friendly analytic offshoot—appears to be paying off. "SPSS’ focus on its concept of the predictive enterprise, which emphasizes forward-looking analysis of customer and operational data, enabled the company to improve its growth rate in 2006 to 12.3 percent," wrote IDC analysts Dan Vesset and Brian McDonough in a recent market research report. "More recently, SPSS has also emphasized its capabilities for the text mining of unstructured content, a functionality that further enhances its ability to address CRM analytics needs."

Enter SPSS 16

That sets the stage for SPSS 16, which the company unveiled last week—just about a year (to the week) after its SPSS 15 release.

Among other features, SPSS touts improved integration between the revamped SPSS 16 and SPSS’ Predictive Enterprise Services (PES) analytics platform. PES provides the plumbing infrastructure for SPSS 16, Clementine, and other SPSS offerings, officials say. It gives IT organizations a means to instantiate, manage, and disseminate predictive models, human knowledge, and other (often anomalous) byproducts of analysis.

In other words, says Colin Shearer, senior vice-president of market strategy with SPSS, PES provides a one-stop shop for analytics lifecycle management across all SPSS products.

"We introduced PES because we realize that customers have to go beyond an ad hoc approach to analysis to more of an infrastructural approach," Shearer explains. "PES lets you take what were intensely manual tasks, set them up, run them, and make them more automated and scalable. It lets you take the once individual work of analysts and set up a kind of analytical factory."

Elsewhere, the new SPSS boasts improved algorithms, expanded client platform support, beefed-up international capabilities, enhanced data management features, an improved deployment experience, and a host of other amenities—including client availability for MacOS and Linux.

Industry watchers see SPSS 16 as a feature-replete—and even more enterprise-capable—update to last year’s seminal SPSS 15 release.

"[The new release] targets the needs of organizations that value predictive modeling capabilities as corporate assets," notes James Kobielus, a principal analyst for data management with consultancy Current Analysis. "With this latest version of its flagship statistical analysis software package, SPSS has fully integrated the tool into its comprehensive predictive analytics product family, which [it] enhanced substantially this past May."

That’s the good news, Kobielus says. The not-quite-so-good news is that the predictive analytics segment is itself hardly bereft of competition: analysts cite not just SPSS arch-rival SAS, but IBM Corp., Teradata (soon and inevitably destined to split off from parent company NCR Corp.), and, of course, Oracle Corp. in this regard.

"SPSS, though clearly best-of-breed in its core segments, cannot provide as function-complete an enterprise data management … solution portfolio as these vendors," Kobielus argues.

Predictive Trump Card

Just why is SPSS making such a fuss about predictive analytics—and why, moreover, do enterprise buyers appear to be taking note?

SPSS officials cite a number of drivers, including smarter fraud detection (for regulatory compliance), maximizing cross-sell and up-sell opportunities (to boost revenues), and, improved quality and efficiency (for business process management and other quality control scenarios).

"[Predictive analytic] success stories are frequently the sources of ROI claims that make you think twice. You think you must have misread that the first time around. You are talking often about hundreds of percent increases in things like cross-sell or up-sell," Shearer comments. "But the reality is that by using predictive analytics, you are able to produce much more focused marketing, with much more efficient spend. Because [such marketing campaigns] are more accurately targeted, you get much better response rates."

Wayne Eckerson, Director of Research at TDWI, sees predictive analytics as a "perplexing" technology segment: on the one hand, he points out, it typically results in high (and frequently extremely high) ROI performance; at the same time, however, it has comparatively low penetration: only 21 percent of respondents to a recent TDWI survey had fully or partially implemented predictive analytic solutions in their organizations, while another 19 percent were in the process of developing them.

On the other hand, nearly half (45 percent) of respondents were still "exploring" predictive analytic strategies, while 16 percent had no plans to deploy predictive analytics at all—in spite of the fact that predictive analytic technologies have a gonzo track record, Eckerson points out.

"[P]redictive analytics can yield a substantial ROI. Predictive analytics can help companies optimize existing processes, better understand customer behavior, identify unexpected opportunities, and anticipate problems before they happen," he wrote in a recent TDWI report. "Almost all of TDWI’s Leadership Award winners in the past six years have applied predictive analytics in some form or another to achieve breakthrough business results.

About the Author


Stephen Swoyer is a technology writer with 20 years of experience. His writing has focused on business intelligence, data warehousing, and analytics for almost 15 years. Swoyer has an abiding interest in tech, but he’s particularly intrigued by the thorny people and process problems technology vendors never, ever want to talk about. You can contact him at evets@alwaysbedisrupting.com.

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