A recent survey by Rexer Analytics found that the average data miner uses five different software tools and R, the open source statistical programming language, is used by 70 percent of them.
R is on the rise as a business analytics tool. And on this episode of the Data Informed podcast, we’re talking about commercial implementations of the R programming language with David Smith, vice president of marketing and community at Revolution Analytics.
Revolution Analytics is to R what the vendor RedHat is to the Linux operating system—a company devoted to enhancing and supporting open source software for enterprise deployments. In this case, the open source software R comes out of academia, where researchers and community members have developed more than 5,000 algorithms.
On October 28, Revolution Analytics announced its R Analytics Enterprise 7, the latest version of its platform designed to work with Hadoop distributions from Cloudera and Hortonworks, as well as enterprise data warehouse systems from Teradata and IBM Netezza, among other enhancements.
In this podcast, Smith talks about business analytics use cases for R programmers and his company’s efforts to enable algorithms written in R to meet the performance requirements of big data. He also discusses what he calls “the last mile problem” – making the results of R programs accessible to business users accustomed to using BI tools, spreadsheets and data visualization software.
We pick up the conversation with Smith discussing the trajectory of Revolution’s work since its founding before the big data trend, as a spin-off from Yale University in 2007.
Michael Goldberg is the editor of Data Informed. Email him at Michael.Goldberg@wispubs.com.