SWOT Analysis: Business Objects' Predictive Workbench
We look at the strengths, weaknesses, opportunities, and threats of Business Objects' Predictive Workbench.
- By Mike Schiff
- July 9, 2008
With the release of its Predictive Workbench, SAP/Business Objects has filled a noticeable gap in its business intelligence portfolio and can rightfully claim to offer a full spectrum of both core and advanced BI technology.
- The Business Objects Predictive Workbench fills a major gap in the company's overall business intelligence capabilities and allows the company to position itself as a provider of a complete spectrum of BI technology.
- The Predictive Workbench is the result of an OEM agreement with SPSS for its Clementine data mining technology and is therefore based on proven, commercially successful technology.
- Even in recessionary times, the market for data mining and predictive analytics grows, as companies seeking ways to identify new selling opportunities, reduce expenses, and discover or eliminate fraud. Furthermore, governmental agencies including Homeland Security, Medicare, and the Internal Revenue Service are certainly likely to increase their usage of predictive analytics in their efforts to identify suspicious behavior or prevent fraud.
- Many organizations are trying to reduce the number of vendors that they deal with and this permits Business Objects to offer one-stop shopping for both core and advanced BI technology.
- It demonstrates that contrary to the fear, uncertainty, and doubt (FUD) created by some of its BI competitors, SAP continues to evolve and improve the acquired Business Objects' products with enhancements and new functionality and is not optimizing them specifically for SAP-centric environments.
- Business Objects' OEM agreement with SPSS is for Clementine but not for Text Mining for Clementine. Business Objects already had the ability to analyze and mine with unstructured data as a result of its May 2007 acquisition of Inxight Software.
- The Business Objects/SPSS OEM agreement was originally announced in December 2007. There may be concerns about why it took over seven months to release the product.
- SPSS also has an agreement with IBM/Cognos to integrate SPSS Predictive Analytics technology with Cognos 8 BI. This will serve to reduce any competitive differentiation that Business Objects hoped to gain over its historic arch-rival.
- Business Objects has partnerships with other data mining vendors, including KXEN, that may not appreciate the OEM agreement with SPSS.
- Although many organizations consider data mining and predictive analytics as such a strong competitive weapon that they are reluctant to discuss its use, I believe that this is one of the fastest growing segments of the BI market. We are only seeing the tip of the iceberg relative to overall market opportunities.
- By offering the Predictive Workbench as an add-on to its BI platform, Business Objects can position itself as a full-service vendor that can provide prospects with both core and advanced business intelligence technology.
- As I have previously speculated, SPSS, with a market capitalization (outstanding shares of stock times the price per share) of under $700 million, is a potential acquisition target for companies wishing to augment their BI portfolio. Should this occur, and assuming SAP is not the acquirer, the OEM agreement would ultimately be at risk.
- SAS, the market leader in the advanced analytics market (of which predictive analytics is a part), is also a vendor of core BI technology and is likely to defend its market position aggressively. It will highlight its vast experience and numerous customer successes with data mining technology while positioning other BI vendors as relative newcomers, who, in many cases, rely on other companies for their data mining technology.
To avoid potential disputes, SPSS and Business Objects will need to clearly define rules of engagement when, for example, SPSS deals directly with Business Objects customers or prospects.
Michael A. Schiff is founder and principal analyst of MAS Strategies, which specializes in formulating effective data warehousing strategies. With more than four decades of industry experience as a developer, user, consultant, vendor, and industry analyst, Mike is an expert in developing, marketing, and implementing solutions that transform operational data into useful decision-enabling information.
His prior experience as an IT director and systems and programming manager provide him with a thorough understanding of the technical, business, and political issues that must be addressed for any successful implementation. With Bachelor and Master of Science degrees from MIT's Sloan School of Management and as a certified financial planner, Mike can address both the technical and financial aspects of data warehousing and business intelligence.