Teaching
The gap in our industry is not technical. It's conceptual.
Professor of Practice, Gaylord College of Journalism and Mass Communication, University of Oklahoma.
The gap in our industry is not technical. It's conceptual.
Professor of Practice, Gaylord College of Journalism and Mass Communication, University of Oklahoma.
I teach Marketing & Media Analytics in the graduate program at the University of Oklahoma's Gaylord College of Journalism and Mass Communication. As a Professor of Practice, I bring the working reality of the discipline into the classroom, not just the theory.
Gaylord College prepares students for leadership in media and communications. My students are future media executives, marketers, advertising and PR professionals, crisis communicators, and a few who will go into nonprofits or politics. Most of them will spend their careers working alongside data science teams or commissioning outside vendors, not building models themselves. I'm preparing them to know what's analytically possible, to avoid being overpromised by vendors, to interpret results without being misled by them, and to ask the questions that turn data into decisions.
That's exactly who I'm trying to teach.
I teach because the gap in our industry is not technical. We have enough people who can write Python. We have a shortage of people who can translate that code into business value, communicate uncertainty to non-technical executives without either dumbing it down or hiding behind it, and design measurement systems that actually improve decisions rather than just report on what happened.
We also have a serious problem with what I call the black box mentality: the assumption that complexity is a proxy for rigor, and that the right response to a difficult question is always a more sophisticated model. It isn't. The right response is often a clearer question, a simpler test, or the courage to act on imperfect information rather than wait for certainty that will never arrive.
I teach to immunize the next generation of leaders against this mentality, and against its inverse: the reflexive distrust of quantitative analysis that comes from too many bad experiences with it.
By the end of the course, the goal is for students to be able to:
Teaching is not a sideline. It is a forcing function. Explaining decision science clearly enough for a graduate student with a communications background is harder than explaining it to a practitioner, and doing it well sharpens every other form of communication I do. The explanations I develop in class show up in the boardroom. The questions students ask reveal the assumptions I didn't know I was making.
There's also something important about the direction of influence. I'm not trying to train data scientists — I'm training the executives and managers who will hire, direct, and evaluate data scientists. Getting that relationship right, building the conceptual foundation for a healthy analytics culture, is, I've come to believe, more valuable than producing another cohort of technical practitioners.