IBM Gets Big and Personal with Business Analytics

IBM’s Information on Demand (IOD) event showcased its products for both information management and business intelligence. I’ve covered the information management aspects of IOD in a separate post. In this post I’ll look at the business intelligence aspects. Earlier this year IBM made predictive analytics a major focus of its Business Analytics analyst summit, an event that often foreshadows the IOD messages. In addition to predictive analytics, IBM emphasized both large-scale “big” data and a concept it calls “personal analytics” at the summit. Both of these received more attention at IOD.

In the run-up to IOD, IBM released SPSS 20, which includes new mapping features, some modeling and performance improvements, file compression in temporary tables and the ability to launch server jobs and then disconnect while the jobs run. But the emphasis at IOD was on the “decision management” capabilities of SPSS and embedding predictive analytics in software and services across the IBM portfolio. Decision management refers to automating routine operational decisions using a combination of predictive analytics and business rules. IBM combines the capabilities of its SPSS and ILOG products to provide decision management. Since only 13% of 2,600 organizations participating in our business analytics research currently use predictive analytics, IBM may be ahead of the market with these capabilities. We’re collecting more information about how organizations are managing their predictive analytics in a benchmark research project currently in progress.

IBM already has a big presence in big data, but it has not yet permeated the company’s business intelligence product line. For example, you can use IBM BigSheets, a spreadsheet-style tool, to interact with big data in Hadoop, but you cannot do the same in the Cognos products. In certain point applications, such as Cognos Consumer Insight, IBM has brought together Hadoop-based processing of social media content with the Cognos presentation layer to categorize and display what people are talking about in public channels. IBM also introduced the first application based on Watson, the data-processing technology used to compete on Jeopardy. IBM has announced Content and Predictive Analytics for Healthcare, a product designed to examine unstructured data such as notes and comments in addition to structured healthcare data such as lab results. Our benchmark research into Healthcare finds a large opportunity for improvement. Overall then, IBM is making strides bringing big data and predictive analytics to the masses, but to date the efforts are confined mostly to point applications and custom consulting engagements. I expect that the experience that IBM gains from these activities will give it a competitive edge in big-data analytics for the near term.

The third theme, personal analytics, although nascent, seems to be gathering some momentum in the market. MicroStrategy recently introduced Cloud Personal, which I covered in a previous post. At IOD IBM devoted a significant portion of its Business Analytics keynote to personal analytics including an in-depth demo of some capabilities that are still in the labs. Personal analytics means more than visualization to IBM. It also includes the ability to update data and perform what-if analysis to enable driver-based planning, which my colleague Robert Kugel explained in this post. IBM also plans to support disconnected usage so users can work with analytics offline. Many users would also consider mobile access a form of personal computing. At the event, IBM introduced its first native iPad application in the Cognos product line, which my colleague Mark Smith recently reviewed.

In the weeks before IOD, IBM introduced extensions to its Smart Analytics systems, which are bundled business intelligence hardware and software appliances that provide operational intelligence or real-time analysis of data generated in an organization. These bundles include InfoSphere Warehouse and Cognos BI software. (Oracle recently introduced similar capabilities in its Exalytics appliance, but the Oracle version only comes in one configuration.) IBM’s configurations available now range from a single-server version on System X (x86 architecture) and Power Systems to mainframe-based systems based on z/OS. Solid-state disks (SSD) are available on some of the larger configurations for higher performance.

IBM continues to provide a broad range of analytics capabilities that are reasonably well integrated. It has work to do to integrate the pieces fully, and individual products from other vendors may be better at specific analytic tasks. Even so, in my opinion, IBM has claimed a leadership position with its vision for analytics.


Ventana Research

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