Case Study: The State University of New York achieves faster analysis of massive research data with Revolution Analytics

Leading Research Center Speeds up Analysis and Simplifies Complex Analysis on Very Large Data Sets

The State University of New York, specifically SUNY Buffalo’s multiple sclerosis research center, needed a way to analyze huge genetic and environmental data sets and identify complex gene-gene and gene-environment interactions. The scale of the problem created a combinatorial explosion, and running models on commodity hardware could take almost a week, making it difficult to iterate and discover meaningful results. Revolution Analytics and Revolution R Enterprise for IBM Netezza were used to help address this challenge.

With Revolution Analytics, SUNY Buffalo consolidated reporting and analysis in one environment, simplified model building, and quickly added or removed variables without rewriting large amounts of code. The solution dramatically accelerated analysis time from 27.2 hours to 11.7 minutes, reduced database administration, enabled more complex interaction studies, and helped the team publish multiple scientific articles.

The State University of New York