How SAS is assisting enterprises to do better

Infotech Lead America: Business analytics company SAS has made a number of announcements on Wednesday. is presenting the highlights here:

SAS said its High-Performance Analytics Server now supports more analytics, including text mining and optimization. The predictive modeling capabilities of SAS High-Performance Analytics Server will also use Hadoop Distributed File System (HDFS), an open source big data infrastructure.

SAS Revenue Management and Price Optimization Analytics, is designed specifically for hospitality, travel, sports and entertainment industries to help solve challenging pricing problems.

With initial components generally available in Q1 2013, SAS Financial Crimes Suite is built on SAS’ analytics and information management technology. Organizations can use the flexibility of interconnected modules to roll out either single or multiple modules, decreasing implementation times and improving results.

Using SAS High-Performance Marketing Optimization organizations can perform analysis in minutes.

A marketer with hundreds of campaign offers, thousands of contact policy constraints, and millions of rows of customer records can quantitatively predict the impact of changes in near-real-time.

“I cannot stress enough the importance to marketers of being able to predict the impact of new programs before they deploy them,” said James Taylor, CEO of Decision Management Solutions.

SAS also unveiled new software for fast-moving organizations that need immediate insight from real-time data streaming into their enterprises.

The new SAS software enables real-time decision making by continuously analyzing data as it is received.

SAS DataFlux Event Stream Processing Engine, available in December, incorporates relational, procedural and pattern-matching analysis of structured and unstructured data. The innovative design enables flexible deployment as a callable, embeddable service.

By cutting time to action, this new software lets enterprises make critical decisions in time to be most effective. Traditional approaches, which apply analytics after data is stored, may provide insight too late to act.

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