Revolutionary Case Studies

Big Data Creates Specific Challenges for Firms Competing in
Financial Services Sector

Asia Capital Reinsurance Chooses Revolution DeployR to Analyze Complex Data and Generate Business Insights

Headquartered in Singapore and regulated by the Monetary Authority of Singapore, Asia Capital Reinsurance Group (ACR) is the world’s first independent reinsurance group with an exclusive focus on serving clients in the fast-growing pan-Asian region, extending from the Middle East, through China to Japan, and south to Australia and New Zealand. ACR provides clients with a combination of strong Asian dedicated capacity, global underwriting standards and expertise, as well as in-depth knowledge of the Asian insurance markets. The specialty lines ACR underwrites include aviation, casualty, credit & surety, medical, motor, energy, engineering, marine and property.

Like many companies in the financial services sector, ACR looks for the most effective and strategic ways to leverage the explosion of Big Data. Specific challenges include:

• Transferring of data between various applications and systems
• Generating useful insights from the data that can be applied in real-world business contexts to strengthen the company’s market position

ACR responded to those challenges by seeking a web-based analytics platform for underwriting that was powerful and fast enough for handling Big Data. Flexibility, extensibility and cost-effectiveness were also required. Since the R programming language is designed specifically for data analysis, the firm was open to considering software written in the R language.

Improved Performance of Models Enables Innovation of New Methods for Analyzing Post-Market Drug Efficacy

The testing of the safety and effectiveness of new drugs continues even after drugs are approved for sale.  In the months and years after a drug goes to market, researchers can observe how well drugs perform and what side effects may occur, but valid assessment of drugs’ performance outside of randomized trials can be challenging.  Development of methods for non-randomized research requires extensive simulations based on large and complex datasets, which in turn require extensive staff time and computing capabilities.  The rapid development of research methodologies allows medical investigators to draw more accurate conclusions more quickly, thus helping improve patients’ lives.  

Very large simulation studies are often required to create and improve methods for assessment of drugs’ safety and effectiveness. Unlike randomized early-stage trials, the hallmark of these studies is that they work with data observed in the real world from patients who’ve been to their doctors, been prescribed a medication, taken the medication and experienced a qualitative health outcome, either positive or negative, as a result of the medication.                  

As a consequence, drawing conclusions from the data observed in the real world of medical care tends to be more complex and more problematic than the data analysis in the controlled environment of early-stage trials.

Complex Data Sets in Genomic Diagnostics Require Multiple Analytic Methods

Revolution helps accelerate process, reducing project time

Analyzing complex clinical data from thousands of patients, and leveraging the results to built diagnostic algorithms used by physicians to determine the likelihood that their patients have obstructive coronary artery disease.

eXelate Turns Big Data into Smart Data for Marketers with Revolution Analytics and IBM PureData for Analytics

Big Data Analytics solution leveraging eXelate’s proprietary prediction models developed and running on Revolution R Enterprise and IBM PureData System for Analytics, powered by Netezza technology.

Goral Trading Uses OneTick Database Integrated with Revolution R Enterprise for High-Frequency Data Crunching

Combined capabilities of Revolution Analytics’ and OneMarketData’s applications create the perfect marriage of IP for Goral’s quantitative equity analyses

The collection and analysis of large sets of tick data for high frequency trading

Open Source Analytics Offer Cost-Effective Choice for Marketing Automation Leader

Marketo uses Revolution R Enterprise for Building Prototypes Rapidly & Cost-Effectively

The modern global economy has created an explosion of data. As data sets become larger and more complex, analysts look for software solutions that are both fast and cost-effective. Open source techniques based on R, a programming language designed specifically for data analysis, offer users the speed, agility and economy required to handle today’s larger ‘Big Data’ sets.

Merck Optimizes Clinical Drug Development With Revolution Analytics' gsDesign Explorer

Promising New Drugs Get to Market Faster and Less Time and Money is Spent on Drugs That Don’t Work

Interim analyses during the clinical trials and research process offer opportunities for early stopping of trials, if, for example, the new treatment is demonstrably better than the standard treatment or clearly inferior. This can have benefits in many areas, from improved patient outcomes to significant savings in money and time.

High Performance Analytics Improves Productivity for Busy Research Institution

Revolution R Enterprise with Microsoft HPC Server 2008 Outperforms University’s Alternatives

Erik Segur, Information Technologist, runs a mission-critical IT environment to Michigan State University’s Department of Statistics and Probability. Hundreds of researchers including professors and graduate students rely on the infrastructure he manages to process their analytics work. Revolution R Enterprise 5.0 delivered two important improvements for the Michigan State University’s Department of Statistics and Probability Statistical Computing Cluster compared to previous versions of Revolution R Enterprise and the Open Source distribution of R. They are the ability to automate the schedule of jobs to be run on the Statistical Computing Cluster and performance improvements. 

Prior to using Revolution R Enterprise 5.0, analytics jobs had to be scheduled manually on the MSFT HPC platform, so Erik was required to constantly check the status of the Statistical Computing Cluster to make sure everything was still running and to start a new job if the system was available. To complicate matters, the system did not close a stopped or killed session properly and there was no way to know the duration of a job prior to it being run. The manual scheduling process caused downtime on the machine because if the job ended shortly after Erik left for the day, a new job would not be started until the following day. Further, it’s common for professors to be unavailable until the afternoon due to teaching commitments, so this powerful, valuable computing resource could remain idle for nearly 24 hours.

‘Portfolio’ Strategy Helps Firm Maintain Leadership Role in Competitive Market

Analytics outsourcer leverages continually evolving R library to stay at the cutting edge

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.”

Northern Trust Bank Speeds Operational Risk Models with Revolution R Enterprise

Challenge:

Operational Risk – Monte Carlo Simulation Benchmarking

Solution:

Revolution R with doRSR & doSMP for parallelization of processing

Results:

Using Revolution R allows risk analysts to improve processing performance through parallelization. Utilizing Revolution R with doRSR and doSMP reduces the time to results and automates management of computer resources. Revolution Analytics’ parallelization routines are scalable to the resources available.

Revolution R at Pfizer

Pfizer's current uses of R include: Analysis of genetic data; Microarray pre-processing; analysis of gene expression data; Predictive modeling for Discovery Chemistry and Pharmaceutical Sciences; Data visualization, exploratory data analyses, curve fitting, and mixed model analyses for Discovery Biology; and Monte Carlo simulation.

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

The State University of New York (SUNY) at Buffalo is home to one of the leading multiple sclerosis (MS) research centers in the world.  From the beginning, the genetics of MS were known to be complex and it was apparent that no single gene was likely causative for the disease.  The SUNY team began to look at data obtained from scanning the genomes of MS patients to identify genes whose variations could contribute to the risk of developing MS.  Since gene products work by interacting with both other gene products and environmental factors, the team was interested in researching combinations of interacting genes.

The data sets used in this type of multi-variable research are very large and the analysis is computationally very demanding because the researchers are looking for significant interactions between thousands of genetic and environmental factors.  There are two issues to overcome; crunching through the immense data set and building analytic models that allow the team to look at more than simply first order interactions.  The researchers want to see not only which variable is significant, and also which pairs of variables or which three variables are significant.  This requires the ability to quickly build models and run them on a high-performing environment on huge data sets, and it also requires the ability to include an almost limitless variety of dependent variable types.

UpStream Software’s Big Data Analytics Platform for Marketing Optimization Helps Clients Understand Buying Behavior and Improve Customer Targeting

UpStream Software, San Francisco, CA
www.upstreamsoftware.com

Software Development (Marketing Attribution and Optimization)

Political Scientist Gets Inside the Minds of the GOP with Revolution R Enterprise

Revolution R Enterprise running on 64-bit Windows lifts artificial restrictions on the amount of data that can be processed and the speed with which it can do so—allowing analyses to be performed more quickly than ever before, and in some instances for the first time.

Validus Re Uses Revolution R Enterprise for Risk Management

Revolution R Enterprise is used to develop open, high-performance custom correlation and simulation models that include any and all data required for the analysis.   These are used for ad hoc analysis as well as systematically, driving the decision logic implemented in the company’s production system. 

[x+1] Completes Next-Generation POE; Its Origin Enterprise Data Management Platform for Automated, Big Data-Driven Marketing Optimization

Challenge:

[x+1]'s need for real-time analytics, automated model updates, ability to include new data types and manage quickly-growing data volumes (without sacrificing performance) were not well-matched for existing closed platform analytics application

Solution:

Re-engineer entire analytics application with Revolution R Enterprise, leveraging RevoScaleR for Big Data Analytics, and a distributed computing platform for data management