SAS is opening the Big Data visualization floodgates with an update to SAS Visual Analytics. The company is making the case for Big Data in departmental, multi-user environments by announcing that its in-memory analytics visualization software now supports a wider variety of server hardware, putting it within reach of work groups and mid-sized businesses.
The latest version now runs on departmental-size servers and IT infrastructures, according to the company. Previously, it required relatively potent, enterprise-grade hardware such as blade computing systems and database appliances from the likes of EMC Greenplum and Teradata.
SAS launched Visual Analytics last year as a Big Data-driven alternative to simple business intelligence solutions. The in-memory data exploration and visualization platform leverages SAS LASR Analytic Server and the Hadoop Distributed File System (HDFS) to derive its insights from massive data stores. In an early demonstration, the company set Visual Analytics loose on 1.15 billion rows of data.
Scaling It Back for the Business
Today, SAS is bringing some of those capabilities to enterprises of more modest means.
"Customers unable to justify a dedicated system for SAS High-Performance Analytics Server can now reap its benefits on in-place hardware. With no additional hardware costs, they can run models that once took days or hours in minutes or seconds, and produce business insight on demand on any size data," said SAS Senior Vice President and Chief Marketing Officer Jim Davis in prepared remarks.
SAS Visual Analytics enables users and executives to explore data visually on PCs and tablets using a self-service reporting or mobile dashboard model. The result is quicker and insight-driven business decision making with a minimum of IT intervention.
For SAS, the move helps broaden the appeal of its Big Data portfolio. "SAS Visual Analytics is highly scalable, ranging from scenarios with a handful of users to a corporate wide, global deployment," stated Davis.
New Data Analysis Tools
The latest version of Visual Analytics also provides companies a look forward with new forecasting tools.
"There are numerous algorithms used in forecasting, and each of those algorithms is optimized for a certain class of data. How does a business analyst know which forecasting algorithm to use?" wrote Don Chapman, a director at SAS's Business Intelligence Research and Development in an official company blog post.
A recommendation engine helps eliminate the guesswork, added Chapman. "With the new version of SAS Visual Analytics, you don’t have to know – the product does all the work for you by looking at your data, sorting through the most robust set of forecasting algorithms available, recommending the one that fits your data the best and providing a visual of the forecast immediately."