Revolution Analytics Sees Big Data Solutions Moving Into SMB Arena

"There's so much data to be collected that in the past wasn't saved. Now that SMBs are saving more of that data, the next logical question is 'How do we use that data?'" said David Smith, marketing and community vice president at Revolution Analytics, a supplier of software, services and support for the open-source R statistics programming language, in an interview with CRN.

Wednesday Revolution Analytics launched Revolution R Enterprise 6.2, a new release of the company's distribution of R, which is used by businesses to develop predictive analysis applications.

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While many of the company's sales are direct, the company also sells Revolution R Enterprise to ISV partners and through systems integrators that use the software to build predictive applications for their customers.

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Smith said the first wave of big data technology adoption was largely among big companies. "I think there's a second wave going on there," he said, referring to big data technology adoption among SMBs.

UpStream Software, a Revolution Analytics ISV partner based in San Francisco, plans to take advantage of 6.2's parallel algorithm capabilities to further develop its software-as-a-service marketing intelligence applications for multi-channel retailers.

"Revolution R Enterprise 6.2 enables UpStream to build highly efficient statistical models on extremely large data," said Tess Nesbitt, a data scientist at UpStream, in a comment emailed to CRN. "Because their parallelized algorithms are so efficient, it enables us to take multiple passes at the data, build iterative models, and it provides everything we need to glean as much information and build the best models we can for our customers."

The 6.2 edition of Revolution R Enterprise provides a high-speed parallel connection to Teradata's database, via the Teradata parallel Transporter, which developers use to write applications that extract data from the Teradata system. That, in turn, helps users save time when working with large datasets, Smith said.

New script management APIs and priority scheduling features in the RevoDeployR Web Deployment Framework improve the management and operation of deployed R routines. Also new is stepwise regression for big data linear models and parallel random number generation. Altogether the new release has close to 90 new features and enhancements.