Savvis introduces Big Data solutions

Savvis has introduced the Savvis Big Data Solutions, a full suite of services designed to help organizations glean the most value from their data.

Savvis Big Data Solutions gives enterprises and government organizations access to the compute, storage and high-bandwidth network capabilities required to power virtually any analytics application. The suite includes Savvis’ managed services for Cloudera and MapR platforms based on Apache Hadoop.

“Businesses wanting to transform their operations and customer experiences through powerful, new analytics capabilities need a robust IT foundation,” said Steve Garrou, vice president, global solutions management, at Savvis. “Savvis Big Data Solutions helps organizations maximize value from their analytics investments through scalable, secure infrastructure services that deliver faster time to market for competing in today’s dynamic marketplace.”

Big Data Solutions features enterprise-grade Infrastructure-as-a-Service capabilities, including scalable compute and storage platforms, software and security services.

The suite also offers secure, high-bandwidth network connectivity for accessing, integrating and processing massive amounts of data. The product also includes software licensing and operations management for Cloudera and MapR distributions of Apache Hadoop, including configuration, monitoring, upgrades and security.

Other features are Big data planning and implementation services, including environment design, security planning and project management; and consulting services for client business-case development.

 “Big data strains the ability of technology to store, process and access customer data,” writes Forrester Research Inc. principal analysts Mike Gualtieri and Noel Yuhanna in the July 2013 report, “Customer Engagement Can’t Begin Without A Next-Gen Customer Data Management Platform.”

“The increasing volume, velocity and variety of data should be a boon for customer data richness, but only if firms have the technology and processes to collect, store, process and access it. Traditional data management approaches often lag behind when it comes to huge data volumes, superfast data collection or the analysis of customer events stored in different data formats,” Gualteri and Yuhanna write.