Alumni Spotlight: Tom Niccum

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April 12, 2019

CS&E alum Tom Niccum sat down with us recently to talk about his circuitous route to success—from dropping out of school as a sophomore to graduating Magna Cum Laude ten years later to launching an extremely successful career as a serial entrepreneur with an aim to help the healthcare industry make better decisions with data.
 
Tell us a bit about your early connection to the University of Minnesota. What made you decide to come here as an undergraduate?
 
I graduated from Columbia Heights Senior High School in 1975.  At that time, it was the policy of the U of M to automatically accept any Minnesota high school senior who graduated in the top 10% of the class.  No one else in my family had ever attended college in a traditional way, and my only model was my father, who was taking courses in business through the extension college, using his GI Benefits from the Korean War.  Since he was my role model, and I was already accepted, I followed an easy path.  The tuition prices were very reasonable--I could work a part-time job and pay my way through the university.
 
What are some of your memories from your time here at the U, both as an undergrad and ultimately a graduate student?
 
As an undergrad, I have to separate my journey into two parts.  My first two years, I was a very poor student, and ultimately dropped out after my second year.  I was just smart enough in high school to get decent grades without working very hard--that proved poor preparation on my part for the rigors of the U!
 
In 1977 I dropped out, but found a series of good jobs as a computer programmer.  My educational failure gnawed at me, though, and after 10 years I went back, sorted out where I stood, re-took several classes that I had done especially poorly in and made extensive use of the continuing education and correspondence classes to complete my bachelor's degree.  It took me about four years to finish, however I raised my GPA substantially and took enough honors courses to finally graduate Magna Cum Laude. 
 
During my last semester, two professors (Dr. Martin Stein and Dr. Jaideep Srivastava) approached me about considering applying to the Ph.D. program.  Their encouragement was key, and I received a teaching assistant position after I was accepted.  After passing my written preliminary exam, I also applied for and was awarded a fellowship with the Army High Performance Research Center, which carried me through most of my Ph.D. studies.  I received a masters degree fairly quickly on the way to the Ph.D.
 
Unfortunately during this time the lure of business proved attractive and I started a company with several friends, while my thesis languished.  Dr. Srivastava continued to encourage me during this time, and ultimately I finished my thesis and successfully defended it.
 
What drew you to study computer science? What were your early experiences with computing that inspired you to pursue a computer science education and career?
 
From early childhood I wanted to be a chemist. This included the obligatory "almost blew up the basement" scenario.
 
In 1969 while I was in 7th grade, our math class received the gift of a teletype machine--which connected to a computer over the telephone.  The speeds were amazingly slow.  But we could program a computer!  Several of us quickly formed a club and from then through high school we would spend most waking minutes trying new things.  It was a natural jump to do a computer science degree from that.
 
What was the computer science program at the U like when you were a student here?
 
In 1975 you didn't get to touch or even see the computer.  We generally wrote programs on IBM Punch cards.  These "jobs" could be submitted to lab assistants who would run them, and return the output in a bin some time later, or, there were a few stations where you could run the jobs yourself via a remote card reader/printer combination.  A bit later there were CRT terminals where you could be a bit more modern.
 
Fortran was common in the sciences and COBOL in the business school. Computer science classes often used Assembly language. We had classes that taught exotic languages like LISP and Algol.  When I returned for my second attempt at a BS, Pascal was the language of choice.  Later, during grad school, that changed to C and then C++.
 
What made you decide to continue on to graduate school and what was your research focus as a graduate student?
 
Encouragement by my professors really helped!  My research originally followed my BS concentration which was Artificial Intelligence.  I even transferred that knowledge over to work I was doing at the office, creating some Rules-based expert systems for medical claims.  However AI was stalling out around then, so after 1 year I switched to Database Systems and Jaideep Srivastava became my advisor.  We worked together on projects around parallelizing database algorithms such as joins.  My thesis was entitled "Skew Insensitive Parallel Join Algorithms and their Optimization."
 
How have the fields of computer science and data science changed since you initially started studying? What remains the same?
 
Data Science wasn't really a thing back then. Parallel computing was just getting started and now is in use on pretty much every PC.  We thought we were doing "Very Large Databases" back then--our idea of Very Large would be very small today.  The biggest trends I see are the massive ramp up in stored data and the need to make some sense out of all that.  My two main themes of study, AI and large databases are coming back together!
 
How has your degree and/or experiences at the U impacted your career and/or your life? What skills, lessons, experiences did you gain while here and how might they have influenced your later career path, interests, etc.?
 
My degrees from the U led directly to having the confidence to start a new company in the late 90s in what was then called "Business Intelligence," but has morphed into Big Data, analytics, AI, and Data Science.  We picked a particularly lucky time to start such a business and we were able to build it (with setbacks, of course) over a 19 year period.
 
Tell us about your career as a co-founder and President of Lancet Data Sciences and later a consulting director and project manager at Think Big Analytics. What interested you about business intelligence, big data, and consulting work? What inspired you to apply your computer science technical work in such an entrepreneurial way?
 
My colleagues and I started Lancet after having been involved in a medical records startup that floundered.  We felt that we could start something that was better, so we gave it a try.  This was during the time I took a break from my Ph.D. work, so there wasn't much of a transition. We didn't have a lot of business experience, but we tried to create a company where we wanted to work.  We attracted a lot of software folks with similar mindsets and built a very nice company, eventually reaching about 90 employees plus an office of 60 in India. 
 
Initially we were led into Business Intelligence by our first customer; they engaged us to help build an early stage healthcare analytics system, and after study we felt that using the BI tools available at the time was a good approach.  We became known for our BI abilities. 
 
In 2015 we were acquired by TeraData and folded into their ThinkBig analytics group.  I transitioned from CEO to being a consulting director there, with my primary job of keeping my old Lancet customers happy and looking for ways to help them further with new services like AI.
 
What you are up to now? Any new projects on the horizon?
 
I retired from ThinkBig in March, 2018, after completing my mission of transitioning Lancet and its customers over.  I'm currently working with some new potential customers in healthcare and retail along the same lines--using data to make better decisions.
 
Do you have any advice for current computer science students who may or may not be considering a similar career path in entrepreneurship?
 
If you come out with a tech degree you probably already have the tech down.  What every entrepreneur needs to know is to learn how to sell.  Period.  You have to be able to promote your ideas, your product, your service, yourself. 
 
The one thing an entrepreneur needs to be able to do is sell.  Many people are frightened by that or think its "sleazy."  It's not if it's sincere and done with the customer's needs in mind.  And you can't just hire a salesperson to do that for you.  As the head tech person you'll be called on to do the selling all the time.  No one will know your product/service/offering better than you.
 
What are your predictions about the future of computer science? Data science? What is it about computer science that keeps you excited and motivated for the future?
 
CS seems to move in big cycles.  Centralization of processing vs. De-centralization, AI came and went three times.  It's hard to predict the future very accurately, but I believe computing power is about to make another surge with the advent of Quantum computing, which may unlock many problems that were previously unsolvable.  That seems pretty exciting!
 
You have been a very generous supporter of Computer Science at Minnesota. What made you decide to establish the Niccum/Dowling Travel Award and what is meaningful to you about your support of the Department and the University?
 
It was easy for me.  All throughout my Undergraduate and Graduate time at the U I received financial support.  In the early days the State of Minnesota underwrote much of the cost of the university education I received.  Later it was teaching and research fellowships.  I always said that if I was successful in life the first check I'd write would be to pay back a lot of that, so that other students would get a chance at the unique opportunities I had.

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