'Portfolio' Strategy Helps Firm Maintain Leadership Role in Competitive Market
Analytics Outsourcer Leverages Continually Evolving R Library to Stay at the Cutting Edge
Mu Sigma is a pioneer in analytics outsourcing and provides business decision support services to clients worldwide. The company applies its expertise in statistics and econometrics to help its clients solve problems in marketing, risk and supply chain management.
In today’s ultra-competitive and rapidly changing economy, the ability to predict customer turnover with reasonable accuracy is essential to many of Mu Sigma’s clients. Large data sets pose difficult technical challenges for older analytic methods, creating the need for newer, faster and more flexible strategies for handling “big data.”
Coping with huge amounts of complex data, often from multiple sources, requires a “portfolio” of analytic methods – everything from quantile regression to neural networks to random forest and decision trees. “We like to diversify our models,” says Zubin Dowlaty, vice president/head of innovation and development at Mu Sigma. “We have a portfolio of about 10 models that we’ll run to assess the stability of the coefficient and the predictive capability of that particular model. By running all the models, you can see which ones are the best predictors.”
The benefit of an “ensemble” approach is that when new analytic techniques emerge, they can be brought into the mix without causing disruption. This makes the R especially valuable to Dowlaty, since the R software library evolves continually as members of the worldwide R community contribute new packages and programs. Using R to crunch data is much more economical than using software from traditional vendors. “You could buy two Aston Martins with the money you would spend to license software from some of these guys,” says Dowlaty.
Dowlaty uses a variety of R packages to generate insights into complex data sets, visualize trends, determine key variables, calculate the potential ROI of campaigns and help clients make better business decisions with confidence. He uses Revolution R when parallel processing capabilities are required, and when the data sets are too large for regular R.
The variety and availability of R packages align perfectly with Mu Sigma’s “portfolio” strategy of running multiple models to determine which are the most stable and which have the most predictive power. The ability to run better, faster and more advanced analytic processes helps the company maintain its leadership role in a competitive marketplace. Additionally, the continuing emergence of new R packages helps the company to stay on the cutting edge of analytic science.
“We see a lot of potential in Revolution’s big data package,” says Dowlaty. “In the past, people would say that R couldn’t handle big data. That was the number one excuse for not using R. Well, now R can handle big data because Revolution is tackling the problem.”
About Revolution Analytics
Revolution Analytics was founded in 2007 to foster the R community, as well as support the growing needs of commercial users. Our name derives from combining the letter "R" with the word "evolution." It speaks to the ongoing development of the R language from an open-source academic research tool into commercial applications for industrial use.
Though our Revolution R products, we aim to make the power of predictive analytics accessible to every type of user & budget. We provide free and premium software and services that bring high-performance, productivity and ease-of-use to R – enabling statisticians and scientists to derive greater meaning from large sets of critical data in record time.
We also offer our full-featured production-grade software to the academic community for FREE, in order to support the continued spread of R's popularity to the next generation of analysts.
For customers such as Pfizer, Novartis, Yale Cancer Center, Bank of America and others, our flagship Revolution R Enterprise product stands for faster drug development, reduced time of data analysis, and more powerful and efficient financial models.