Ventana Research Analyst Perspectives

Enterprise Revolution of Predictive Analytics with Version 6

Posted by Mark Smith on Jun 15, 2012 11:40:38 AM

In our benchmark research in predictive analytics we’ve uncovered some intriguing tools for taking advantage of big data in the enterprise. Revolution Analytics, which we analyzed earlier this year, this month introduced its 6.0 release. Revolution extends the open source statistical programming language R with capabilities you would expect out of enterprise software. The company has grown substantially over the last several years and has an impressive list of more than a hundred customers in both the private and public sectors. Revolution partners with database and data integration providers such as Talend and Informatica and business intelligence providers who want to connect to more advanced level of analytics. Revolution can operate across a range of big data architectures, including Hadoop, working with Cloudera and IBM as well as data warehouse appliances such as IBM Netezza and Teradata. This is a smart move, since predictive analytics is the second most important unavailable capability cited by big data deployments in to our benchmark research.

With version 6, Revolution Analytics can now operate across grids of computing technology supporting Platform Computing’s LSF (Load Sharing Facility) clusters for analytic jobs operating across Linux servers. Revolution also can be managed within Microsoft HPC Server management tools to operate in the Azure Cloud to get more elasticity in compute power. Users can prototype locally then deploy into test and production environments. Revolution supports generalized linear models (GLM) to help with the design and deployability of predictive analytics. To support the largest obstacle reported by organizations in our benchmark research, difficulty integrating into information architectures, Revolution supports non-XDF (eXtensible Data Format) data sources, with direct support of ASCII and ODBC but also SAS and SPSS without having to install those platforms. If needed, data can be transformed to XDF format for further analytics in Revolution. For tighter integration with Hadoop, Revolution built RevoConnectR, which allows customers not just to integrate with HDFS but also import tables via Apache HBASE and write map-reduce tasks from within R. All of these advancements in version 6 help broaden the potential for not just the design and modeling aspect of Revolution but also for supporting deployment of the models in business processes.

Achieving a competitive advantage, identifying new revenue opportunities and increasing profitability are the top benefits we found in our benchmark on the value of predictive analytics. Predictive analytics help businesses to be more intelligent about the decisions they make every day. Revolution, with its latest release, provides more flexibility and openness to its technology while becoming more integrated with the range of platform and information architectures that IT organizations operate with today and that they will use in the future.


Mark Smith – CEO & Chief Research Officer

Topics: Big Data, Linux, Predictive, Revolution, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Cloudera, Data Mining, Strata+Hadoop

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Mark Smith

Written by Mark Smith

Mark is responsible for the overall direction of Ventana Research and drives the global research agenda covering both business and technology areas. He defined the blueprint for Information Management and Performance Management as the linking together of people, processes, information and technology across organizations to drive effective results. Mark is an expert in technology for business from Performance Management, Business Intelligence, Analytics to Information Management across finance, operations and IT. Mark has held CMO, product development and research roles at companies such as SAP, META Group, Oracle and IRI Software. He has experience across major industries including banking, consumer products, food and beverage, insurance, manufacturing, pharmaceutical and retail and consumer services.