News Stay informed about the latest enterprise technology news and product updates.

Jim Goodnight on SAS in-memory analytics and the data scientist

A podcast Q&A with SAS execs Jim Goodnight and Jim Davis looks at big data enablers, including in-memory analytics, and the role of data scientists.

Steadily, SAS Institute Inc. is expanding its in-memory analytics capabilities, as witnessed by advances to its High-Performance Analytics and Visual Analytics product lines announced last month. Setting the stage for such efforts was the development of the vendor's LASR Analytics Server software, which SAS CEO Jim Goodnight and Senior Vice President and Chief Marketing Officer Jim Davis discussed recently during an interview in Boston with SearchDataManagement.

As big data gets bigger, memory prices continue to decline and fast analytics performance becomes more and more critical, in-memory technology such as LASR may become prevalent in data warehousing and business intelligence strategies. With LASR and its SAS in-memory analytics applications, SAS is joining SAP AG, Oracle Corp. and others in that pursuit.

"The LASR server was developed after we worked on all our high-performance analytics," Goodnight said. "We decided we really wanted to keep data in memory for a long period of time and let people get at it and do reporting and exploration."

Speed of processing is the primary objective, he said. According to Goodnight, visual analytics tools based on in-memory technology allow end users "to access billions or more rows of data instantaneously." Your mileage may differ, but his claim mirrors ones other vendors make as well as assessments of industry analysts.

SAS colleague Davis said the rising tide of big data is overwhelming the capabilities of traditional database management systems that need to pull information from disk storage before it can be analyzed. "It's not an issue of whether you can store the data," he said. "The issue now is whether [you can process] it quickly enough." In-memory processing and analysis can help companies keep up with the increasing data demands, Davis added.

The SAS leaders also discussed the role of data scientists in analytics programs during the interview, which was recorded for a podcast. Both Goodnight and Davis said the data scientist should focus on practical business problems and be a communicator who can connect different people in an organization on advanced analytics initiatives. Educated as a statistician himself, Goodnight pointedly remarked that the application of analytics -- rather than the theory of analytics -- is the big challenge businesses now face.

In the 12-minute podcast, listeners will:

  • Hear Goodnight's take on the potential benefits of SAS in-memory analytics and other in-memory tools;
  • Get advice on the responsibilities that data scientists can take on in organizations; and
  • Find out how SAS judges which new technologies deserve the most attention.

Email us at and follow us on Twitter: @sDataManagement.

Dig Deeper on Enterprise data architecture best practices

Join the conversation

1 comment

Send me notifications when other members comment.

Please create a username to comment.

The podcast was very insightful and gave us a lot of information from the perspective of someone who has worked in the industry for a very long time.