2018-2019 exchange Winter 2019 Newsletter FINAL | Page 12

Taking a Deeper Dive
into Data Science: An
Interview with Glenn
Hofmann
N.B. This interview was initially published in the Spring 2018 issue
of exchange.
Jen Best: Let’s start off with defining data science. How would you
define it?
Glenn Hofmann: Data science lies at the intersection of statis-
tics, programming and the specific business application or any
application for it. The statistics part comes in when one does more
than data analysis. It does involve writing code and it requires some
subject matter expertise to make sense of it. It’s those three things
coming together. Making sense of data, but not just to create a re-
port or dashboard about it, but gaining insights, predicting some-
thing, making a forecast, and ultimately changing an outcome and
creating a business benefit.
So that’s generally considered data science. It really comes under
multiple names - we used to call it analytics; now, data science is a
more popular name. There are strong parallels to predictive model-
ing, artificial intelligence and machine learning. You could call those
specific kinds of data science.
Jen: You make a good point about involving subject matter experts.
One of the articles in a prior issue was about asking powerful ques-
tions and how data can assist in answering them. Part of that is hav-
ing someone who has broad based knowledge of the organization,
understands the current business strategies and where the organi-
zation is going, and is able to articulate those questions. It goes
hand in hand.
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