A billion dollars is hardly loose change. But even in a recessionary economy, corporate appetite for business intelligence (BI) software for driving greater efficiency and performance remains greater than ever. It’s for that reason that IBM has tabled $1.2 billion to buy SPSS, the predictive analytics firm that is often mentioned in the same sentence as SAS Institute.
The offer, valued at $50/share, or roughly 40% above recent closing price, is for a firm that has come a long way from its origins as a desktop statistical modeling firm. Although the company has struggled with an enterprise sales model since the early 2000s, it finally gained its groove in 2007 when net income doubled to $33 million; even with the onset of the recession in 2008, revenues grew modestly to $36 million.
SPSS specializes in advanced predictive analytics (a term often used interchangeably with data mining) and text-based analytics. Both areas have been conspicuous holes in IBM’s BI portfolio. The move once again plays into IBM’s information on demand (IoD) strategy, which seems to encompass every technology under the sun – IBM has completed 27 acquisitions for its IoD strategy and more than 100 deals in the name of IoD over the last decade.
The two companies are hardly strangers. IBM Cognos already OEMs SPSS analytics as part of its dashboard offerings, while IBM has long had similar deals for bundling SPSS’s PASW statistical modeling software.
More importantly, this acquisition prevents Oracle and SAP from getting their hands on the technology to buttress their BI analytics stories. They are left pursuing niche players such as InforSense, ThinkAnalytics or KXEN.
The deal is complementary; there are few product or technology overlaps
IBM clearly lacks the statistical processing technologies that SPSS offers. Although IBM has limited text analytics tools, SPSS has text mining workbenches and applications, especially in CRM analytics and survey processing. In turn, IBM’s global services and WebSphere Business Services Fabric offer vertical industry templates that could provide opportunities to expand SPSS’s text and predictive analytics into full-blown vertical solutions.
There are other potential synergies, such as in rules and event processing – and both. While SPSS uses rules to parse structured and unstructured data, IBM’s ILOG business rules management system could be used for automating complex, highly regulated processes in sectors such as healthcare, where the sources of data consist of a highly varied mix of structured and unstructured data, and regulations guarding patient privacy are stringent, yet the payoffs from analytics tracking outcomes could merit high payoff.
SPSS should also bolster the advanced business analytics and optimization (BAO) consulting services division that IBM recently launched by adding predictive analytics. Related to BAO are IBM’s Smart Analytics System initiatives, which are aimed at delivering deep and highly specialized analytics for specific industries. Smart Analytics requires workload performance optimization across computing resources. But you first need to drive home pretty heavy-duty analytic processing to require that optimization. That’s also where SPSS comes into play.
Although SAS is the leader, SPSS is the better match for IBM
SAS is the leader of the advanced analytics field. However, SAS’s proprietary programming language and database technologies are so entrenched that even if it were for sale it would pose an integration and cultural hurdle for IBM or any other prospective acquirer. Irrespectively, as a private company with a founder and CEO who has voiced no intention of retiring, SAS is likely to remain independent for some time.
Furthermore, with IBM’s deep resources, the deal could bolster SPSS. By joining the IBM fold, it could theoretically level the playing field versus SAS.
Tony Baer and Madan Sheina are analysts at Ovum