In its latest effort to remain relevant in the age of Big Data, the SAS Institute of Cary, N.C., has moved several of its advanced analytics packages to Amazon Web Services (AWS), writes Wikibon Principal Research Contributor Jeff Kelly.  The privately held analytics vendor, founded in 1976, remains popular with statisticians and business analysts in financial services, healthcare and manufacturing, and its business intelligence (BI) toolset is so far holding its business executive market, as witnessed by its reported $3.02 billion in 2013 revenues. According to IDC, as reported in the Charlotte (N.C.) News and Observer,  SAS grew its market share in advanced analytics from 35.3 percent to 36.2 percent in 2012.

However, writes Kelly, these products are increasingly becoming legacy tools – designed for use with enterprise data warehouses (EDW) in a world that is moving towards Hadoop, Big Data and new analysis tools like R, an open source environment for statistical computing and graphics created by The R Project for Statistical Computing. The new generation of data scientists and analysts learn to use these new tools in grad school and may not even consider using SAS or see an advantage to learning how to use SAS analytics.

One of the issues SAS faces, Kelly writes, is that it is designed to be used on-premise running against data behind the corporate firewall that can be imported into the SAS database. A large percentage of Big Data, such as social media and Internet-of-things data, is in the cloud. This data is “heavy” in the sense that moving multiple terabytes across a network is expensive and time consuming. So the Big Data approach is to move the analysis code to the data.

By porting its products to AWS, SAS is making its tools more available for analysis of data in the cloud. It is developing interfaces to allow its tools to work directly on data in Hadoop, based on the in-database technology debuted in 2007 to allow those tools to work with Teradata.

It has added R functionality to its packages, allowing practitioners to make R calls directly from SAS environments, Kelly writes. Whether this is a viable long-term strategy, as Python and other open source analytics tools and languages gain popularity, remains to be seen.

Kelly sees these moves as a good start but warns that to avoid becoming a legacy vendor with a shrinking user base, SAS must continue integrating its proprietary tools with new Big Data technologies. It also needs to capture mindshare with the new generation of Big Data scientists.

Kelly’s Professional Alert is available in full without charge on the Wikibon Web site. Information technology professionals are invited to register for free membership in the Wikibon community, which allows them to help set the direction of Wikibon research and post their own tips, Professional Alerts, and longer research.

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