As R has become more popular, the role of analytics has become increasingly important to organizations of every size. Increasingly, the focus is on enterprise-scale analytics—using advanced, predictive analytics to improve every decision across the organization. Enterprise-scale adoption of analytics requires a clear sense of analytic objectives; an ability to explore and understand very large volumes of data; scalable tools for preparing data and developing analytic models; and a rapid, scalable approach for deploying results.
Table of Contents
- Introduction to R
- Enterprise Analytics Requirements
- Challenges of Open Source R
- Overcoming the Challenges of Open Source R
According to the widely cited Rexer Analytics Survey, R usage has steadily increased in recent years. Organizations using R to develop analytic models face particular challenges when trying to scale their analytics efforts at an enterprise level. Complex data environments can make integrating all the data involved difficult. The typical R package is single-threaded and memory limited, creating challenges in handling today’s increasingly large data sets. These same limitations can mean it takes too long to analyze and develop models using this data. When all the analysis is done, deploying the results can add a final hurdle to achieving business value at scale.
Solutions such as Teradata Aster R that combine commercial capabilities with open source R offer a way to address these challenges. This paper introduces R, explores the challenges involved in scaling analytics across the enterprise, identifies the specific issues when using open source R at scale, and shows how Teradata Aster R can help address these issues.
Sponsored by Teradata.