What Is the Most Underutilized Trend in Data Analytics? Q&A with Ken Kelley

Author: Data Informed website

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Statistical methods have always been an important matter for businesses. But in today’s world of big data and real-time analysis, certain areas of statistics are taking on a new importance – and other areas have been overlooked. This is something that Ken Kelley, Associate Dean for Faculty and Research and Professor of Information Technology, Analytics, and Operations at Notre Dame’s Mendoza School of Business, understands well.

Kelley teaches in several programs at the Mendoza School, including Notre Dame’s unique Master of Science in Business Analytics (MSBA) program, and has been named to Poets&Quants’ exclusive list of 40 outstanding MBA professors under 40. His expertise includes methodological issues and applied statistics, which he shares with students of the Mendoza School’s MSBA program in its two-part Statistical Methods for Managers course. Data Informed recently had the opportunity to sit down with Kelley and discuss new trends in statistics and old ones that deserve to come back. Read this insightful discussion of the state of statistics in today’s big data world and discover the benefits of the MSBA program available at the Mendoza School.

Data Informed: What areas of business statistics and analytics are you most interested in?

Ken Kelley (KK): In the world of big data, what we are often interested in is a behavioral outcome, whether it’s if someone purchases a product, how long they stay in a store, or how employee personality characteristics map onto employee effectiveness and other outcomes. I’m interested mostly in the area of analytics that measures and models behavior, such as predictors of why a person does something or thinks in a certain way. Many important measures are psychological, such as motivation, engagement, effectiveness at teamwork, et cetera can’t be measured directly. Rather, only manifestations of such latent constructs can be obtained and using such variables is different and contains measurement error. I like to focus on these sorts of issues and thus bring psychology and the methods used therein into the business analytics space.

I realize I approach analytics differently than some, such as those who work mostly with directly measurable variables via computer systems and online activity. I’m not doing that as much as looking at indirectly measurable behavioral factors. Nevertheless, much online activity is behavioral in nature. So to me, the things that influence a behavioral outcome, such as a psychological or personality attribute, are fundamental to many aspects of analytics.

Q: What underutilized trends in the analytics space could have the biggest impact on businesses?

KK: I think psychometric testing and measurement is currently underutilized by many. There’s certainly a movement of people using it, but I think more widespread use could make a big impact. For instance, trying to hire the right people for a job is something that could potentially be better performed by using data than by using an interview. An interview is so limited, and certain factors that don’t determine job performance are probably often given more weight than they should have.

There’s a 1954 book by the famous psychologist Paul Meehl called Clinical vs. Statistical Prediction that discussed how using statistical methods to predict recidivism rates of prisoners was more accurate than using the clinical, subjective method of a parole board evaluation. That idea of making decisions with behavioral statistics, which was first discussed in the fifties, is now something people are talking about in the analytics space – there’s a growing trend of using data instead of situational knowledge to make decisions. The book Moneyball by Michael Lewis presents a great example of this concept. It tells how the Oakland Athletics started using data rather than scouts to figure out which baseball players they should draft or trade for, with great results. These kinds of stories have been very enlightening for many people, but at the end of the day, it’s the same set of underutilized ideas that have been talked about since at least the fifties in psychometrics and behavioral statistics. For example, Michael Lewis also talks about the same sort of issues for basketball players in the Undoing Project, which itself speaks to issue of behavioral outcomes.

Read the entire interview on the Data Informed website.