This article is the second article in the series, “IQ Graduates at Work,” which looks at the careers of graduates from the Information Quality Graduate Program at the University of Arkansas at Little Rock.
Jeff Tyzzer, a 2009 graduate of the UALR MSIQ program, has taken a new position as Senior Sales Consultant at Oracle Corporation, an enterprise hardware and software company headquartered in Redwood Shores, California. At Oracle, the Sales Consultant provides technical guidance to the application software sales team. Jeff specializes in Oracle’s data quality and master data management (MDM) solutions and focuses solely on the public sector. He works with clients to understand their data quality and master data management issues and opportunities, and recommends Oracle-based solutions that address them, often in partnership with systems integrators.
Before taking this role with Oracle, Jeff was a consultant with CGI, where he worked as a Data Solutions Architect and the MDM Lead on a large public-sector project. Prior to CGI, he worked for Accenture as a consultant where he had a range of duties such as the “unofficial” SQL expert supporting more than 70 developers, a Data Conversion Lead, and the Data Quality Lead.
In his own words, Jeff talks about his background and experience in the information quality program in a recent interview:
Question: Where did you grow up and what is your educational background?
Answer: I was born and raised in California, but during my childhood we moved around quite a bit, so my hometown is a bit difficult to peg. After high school I joined the Marine Corps Reserve, where I spent my first year on active duty and served the rest of my four year enlistment while attending college at California State University, Fresno, from which I received a B.S. in Business Administration with a concentration in Computer Applications and Systems. From 2001 to 2007 I attended California State University, Sacramento, where I earned an M.A. in Liberal Arts.
Question: How did you find out about the UALR IQ program and what prompted your interest in IQ?
Answer: The project I worked on while at Accenture was the Child Support Enforcement system for the California Department of Child Support Services. In 2007 I was tasked with writing a series of SQL-based queries to identify possibly duplicate employers and participants within the enterprise database, which had been populated, in part, with the data from three legacy systems. These reports used deterministic (as I now know it to be termed but certainly didn’t then!) matching rules that were to match records based on several different criteria. Thus I was faced with the following problem: if employer 1 and 2 match on criterion A and employer 2 and 3 match on criterion B, how do get employer 1, 2, and 3 to show up as matches only once in a single report?
Many of the students who have taken the UALR entity resolution class (INFQ 7348) will no doubt recognize this problem as one of computing the transitive closure of the resulting equivalence classes into mutually disjoint subsets. However, I certainly didn’t know that then. I spent the next few weeks studying this problem and trying to figure out its solution, which often took me into set theory and graph theory. The biggest hurdle initially was that I simply didn’t know what the problem was called that I was trying to solve, so I didn’t know where to look for its solution. As I learned in the program, the first step in studying something is to develop or learn its vocabulary.
I eventually figured out a way to collapse these sets of related employers by “stitching” them together based on the employer identification numbers they had in common, which took the form of a fairly compact stored procedure. Needless to say, I was pretty proud of myself for having done this, but it wasn’t long before I discovered that the algorithm I “developed” was called the UNION-FIND algorithm, and that Jeff Ullman and John Hopcroft, among others, had written about it back in the early ‘70s!
At any rate, I was hooked on this wondrous mix of theory and practice, and more assignments related to data cleansing and de-duplication followed in short order. I soon began purchasing books on those topics, one of which was Journey to Data Quality by Lee, et al. The back flap of that book’s dust jacket mentions the UALR MSIQ program, which I researched and then applied to not long after.
Question: What was your experience in the UALR IQ program?
Answer: I was a part-time remote student, and am so thankful that UALR was, and still is, a pioneer in distance-based graduate education, as I would not have been able to be a part of the program otherwise. Little Rock is a wonderful place, but my employer, house, and childrens’ school are here in California, so relocation wasn’t an option. Distance education took a bit of adjustment, but once I did I flourished in the program–there’s just nothing else like it. All of the courses, and their professors, were uniformly excellent, but my favorite was INFQ 7348, Entity Resolution, the entirety of which was a “breakthrough learning experience” for me, but particularly the “entity resolution challenge.” I also, of course, learned a lot from my master’s project.
Question: How have you applied what you learned in the IQ program to your current position?
Answer: A big part of my job consists of educating my clients on data quality and master data management, and their role in the overall information ecosystem of data governance. I draw upon what I learned at UALR almost daily as part of that effort.
Question: What advice or encouragement do you have for current or prospective students in the program?
Answer: As I mentioned before, the IQ program at UALR is sui generis–no other program compares. The faculty are leaders in the field and are first-rate instructors, researchers, and mentors, as are the frequent guest speakers. Likewise, as I did, you will learn a great deal from your fellow IQ graduate students, whom you’ll find to be experienced, dedicated, and highly motivated. The future is bright for information quality practitioners, and if you want to make information quality your career, start here!
