What Good Looks Like: SASPosted: February 16, 2012
I am a big fan of SAS (http://www.sas.com/), the world’s largest privately held software company, which was founded in 1976 and specializes in the application of advanced analytics to reveal insights and to enable decision-making. Long before “big data” became a hot buzzword, SAS steadily built an enormous body of talent and intellectual capital in this area.
They operate in 56 countries, have 400 offices, had revenues in 2011 of $2.725 billion and over 12,000 employees. As professional process improvement professionals schooled in the arts and sciences of using analytics, Black Belts and similar professionals ought to study the methods and tools of an organization such as SAS and also to learn which companies effectively leverage SAS’s applications.
SAS has products covering data mining, data visualization, forecasting, text analytics (including sentiment analysis), customer intelligence, master data management, human capital predictive analytics, supply chain intelligence, predictive asset maintenance, and many more applications.
They also operate in a huge range of sectors such as casinos, health care, hotels, insurance, retail, oil and gas, utilities, transportation, and many others.
In a recent interview with the National Post’s Matt Hartley, SAS’s CEO James Goodnight offered several insights into analytics:
James Goodnight has made a fortune helping big business make sense of the digital world through data analytics. The founder and CEO of SAS Institute Inc., one of the world’s largest private software companies, ranks 38th on Forbes’ list of richest people and SAS ranks third on Fortune’s list of the top companies to work for in the United States. Mr. Goodnight recently sat down with FP TechDesk editor Matt Hartley to discuss the role of analytics in the era of big data, the economic environment in the United States and the future of SAS. Following is an edited transcript of their conversation.
Q One of the adages economists tell us is that good companies use downturns to invest in new technologies and that those who do end up coming out ahead. Have you noticed a higher level of investing in new technologies as the downturn has abated? Are there certain industries that are taking this opportunity to retool and invest in new technologies?
A We are seeing exactly that, especially in the financial sector. This past year was a boom for us in the financial sector, mainly because for the last few years everybody was being very cautious and careful. But you can’t stop improving IT for two years; otherwise you’re going to be behind, so I think a lot of the banks tried to catch up this past year.
Q When big companies invest in technologies such as those produced by SAS, it can be an indicator of the economic health of a particular region. I’m wondering what you’re noticing from your end from a broad economic perspective.
A We hope the Canadians are here to bail out the U.S. when we go bankrupt, so keep your money dry to help bail out the U.S. Even in Europe we’ve had a pretty good year this year, even though we’re starting to suffer now because of the dollar value to the euro is falling. So that affects the amount of revenue we can report from Europe.
Q One of the things you and I chatted about that last time we sat down was the use of analytics in areas beyond the business world, such as in law enforcement and in the justice world. What kind of appetite for analytics technology are you seeing from governments and the justice system?
A We’re using a lot of our text analytics in these areas to extract context of what a document is about. So doing things like social media analytics, where we can go out and pull in all the tweets about a particular brand and then decide whether something is a positive comment, a negative comment or a neutral comment about a particular brand. And then we can follow that over time, so you can see what happens, if your policies change, say, in a retail store, or if you have sales or a new advertising campaign, you can see the difference in sentiment among the people who are out there tweeting. So this is going to be a very popular thing that we’re doing a lot of work with. Law enforcement is interested in tweets as well … things like flash mobs, or flash robs as some of them have been called. If we’re constantly looking out for that kind of thing and can alert the police to be on this corner at this certain time because something could happen, that makes a lot of sense.
Q Of course, the health-care industry is also very interested in analytics. Have you noticed a change in the investment there?
A We’re doing a lot of work in the health-care industry in the fraud area to determine when a claim is put in, whether it’s fraudulent or not. Some of our social networking [analytics technology] can link people together that are involved in a particular type of claim to see if there’s a criminal network involved. We have been able to uncover a lot of fraud that way for some of the state health and private health carriers.
Q When you started out, analytics wasn’t exactly mainstream, but it’s something many companies today now use. Where are we on the evolutionary curve of analytics?
A I think we’re just beginning the hockey stick upturn, and I think it’s going to accelerate. We’ve been working for the last two years on our high-performance analytics and we just started shipping it now…. There’s a lot of business processes that will be changing because of the speed at which we can do analytics; using a thousand processes in parallel to do these computations can make it possible to do huge problems that we would never have been able to do before because it would take too long on a single processor.
Q How are businesses changing the way they operate because of the increased technical capabilities of analytics? Are there broad trends in the reorganization of big companies as a result of these new technologies?
A We’re going to see it. This is the rollout year where more and more companies are going to be adopting analytics technologies. Mainly, the first takers are the big banks because they’ve got numerous groups where all they do is modelling and forecasting – over and over again. [They’re] changing models on almost a daily basis that they’re using to judge whether a credit card is being used fraudulently, or whether or not they should loan money to someone, or what kind of interest rate they should charge people. All of these things are based on analytical models that have been developed using the company’s past historical data to do forecasting. This is the year we’re going to see a lot of high performance analytics.