Jim Goodnight

Spread over 900 acres, the SAS headquarters at Cary, North Carolina is probably the biggest single-site office location in the world. And if not a data scientist, its CEO Jim Goodnight could have easily been a geologist, given his fascination for collecting rocks. The conference room where we are interviewing him has lots of it neatly named and arranged, his favorite being the deceptively heavy Gibeon Meteorite from Namibia. The collection built over the years now totals 400 and gets added via the annual shopping trip that Goodnight makes to Tucson every February. With revenue of $3.2 billion, SAS which he co-founded in 1976 is one of the world’s biggest privately-owned companies but its private ownership has not prevented it from becoming a highly-rated employer of choice. There is possibly no enterprise of consequence that does not use SAS’ analytical software. Not only are 90% of the Fortune Global 500 companies its clients, the IRS also uses its software to identify fraudulent tax filings. To ensure that its offerings stay cutting edge SAS spends 25% of its revenue on R&D each year and now has its sights on machine learning.

How did you get started on analytics? Can you take us through the work that you used to do, and how is it different from what SAS does now?

We started SAS back in 1966 at North Carolina (NC) State University. I was actually working part time, for the Department of Statistics, as I put myself through college. We were in charge of analysing all the agricultural-experiments conducted on campus. NC State is a land-grant college that was established to study agriculture and engineering. There are a lot of agriculture related departments over there. I was doing experiments all the time, to try to improve the crops, try to make cows give more milk or grow faster.

The Department of Statistics would help design the experiments. I was part of the group that analysed the data. We constantly had to rewrite the programs to analyse different kinds of data. What we needed was a method whereby we didn’t have to rewrite everything, every time; just change a few parameters. That’s how SAS got started.

Back in those days, we were predominantly focused on planned experiments. They were laid out in a way that made it possible to analyse all the experimental designs. It still goes on today, it is big in the universities, in the pharmaceutical companies where they are testing new drugs. And we had to test for the efficacy of the drug, if it’s better than the existing ones or if it had any side-effects.