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SAS CEO Goodnight on revenue, talent, collaboration and the year ahead

SAS Institute CEO Jim Goodnight celebrates another profitable year for the privately held software vendor and considers the data and analytics road ahead for 2012.

Late last week, SAS Institute Inc. announced it saw a 12% increase in revenue in 2011 -- more than double 2010’s jump in business -- to $2.73 billion.

“I think the market is very much interested in using analytics to help them get out of the slow economy,” said Jim Goodnight, CEO and co-founder of SAS. “Almost every business now of any size that has any amount of data is talking to us about helping them to improve their performance, optimize processes within the company and do a better job of prediction.”

Specifically, Goodnight said, insurance companies, state and local governments are expressing interest in analytics to help with fraud detection.

“Way back in 1990, we started seeing a lot more interest from banks in developing models,” Goodnight said, “but now it’s gone mainstream and all of the different businesses are interested in doing better forecasting and better modeling.”

The revenue numbers reinforce the privately held company's continued foothold in the analytics market, but they also provide an opportunity to look ahead at the year to come. did just that last week with Goodnight and Jim Davis, chief marketing officer at Cary, N.C.-based SAS.

On buzzwords of 2012
Goodnight and Davis agree that “big data” is here to stay.

“The latest buzzword is big data, and that’s something SAS has worked with for many years,” Goodnight said. “We’re quite well prepared for that.”

Beyond the term’s sheer popularity as a topic, it has become a real challenge for businesses to manage and analyze data that doesn’t neatly fit into a relational database and, if it does fit, takes a long time to process. Davis said for these reasons, high-performance computing platforms, such as the one SAS rolled out at the end of 2011 or SAP’s HANA, will become attractive alternatives for businesses.

“It’s an area where analytic processes that used to take 10 and 15 hours can now run in these distributed processing environments in a matter of 15 minutes or less,” Davis said.

Along with that, Davis said services that host an organization’s data will become increasingly popular. He draws a line of distinction between the cloud and a hosting service, which can provide an organization with the hardware, software and talent. Cloud computing, on the other hand, is a buzzword Davis believes to be waning.

“I think that’s probably too broad a term,” Davis said.

On finding analytics talent
Talent has become a popular topic of conversation itself these days as businesses realize advancing their analytics program may not only mean investing in technology, but in personnel as well. One way SAS is responding to the talent deficit is through graduate-level programming at universities.

“About four years ago, we helped provide the seed money to start the Institute of Advanced Analytics at [North Carolina] State University,” said Goodnight, referencing the very university where SAS first got its start almost 40 years ago. “[Students] spend almost a full year working with very large amounts of data, learning different techniques of how to analyze data and then they come out with a master’s degree in advanced analytics.”

This year, SAS will expand the program at North Carolina State as well as at Louisiana State University and Texas A&M.

On the role of the data scientist
The term data scientist became mainstream in 2011. EMC Greenplum, for example, sponsored its first Data Science Summit last May and O’Reilly Strata held its first Making Data Work symposium in late February featuring sessions on data science. But the term’s definition is still in flux and a bit vague.

“The people in demand are the ones with analytics experience,” Goodnight said.

Goodnight’s description is key in differentiating data scientists from other analytics professionals: The skills of a data scientist are typically described as stretching beyond statistics and analytics.

“It’s not just statistics. There’s econometrics and forecasting involved. There’s optimization involved in operations research,” Goodnight said. “It’s really a combination of knowledge areas we are trying to create data scientists with.”

Others have included the more-difficult-to-measure qualities of curiosity and communication. As Davis points out, regardless of varying definitions, this kind of highly skilled talent is in demand with more businesses looking to fill data scientist positions while meeting or exceeding starting salaries for employees with master’s degrees in business administration.

On the wall between IT and analysts
The International Institute for Analytics predicted that IT and analysts would form more collaborative working relationships in 2012 because of how quickly the data environment is changing. And Goodnight agrees the two teams have to work together to achieve success.

“Really they have to because when you’re analyzing company data that is spread over numerous departments and different divisions, you really need the IT folks to bring it all together so that you can do the analysis,” Goodnight said.

In fact, the divide between the two is already starting to shrink as businesses introduce things like master data management, especially when visualizing a billion rows of data coming from divergent sources, Goodnight said.

IT is also being stressed to ensure platforms are supporting business initiatives, and to do that kind of work, they’ve got to involve the business user, Davis said. He referenced a recent Gartner report on enterprise architecture trends that included IT departments evaluated based on more business-related terms.

“They’re being measured on customer retention. They’re being measured on profitability. They’re being measured on quality control,” Davis said. “So their metrics are changing and that will help force the relationship with the end-user community.”

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