David Steier, Distinguished Service Professor, Carnegie Mellon University Shares His Academic Data Field Challenges, Goals for 2022

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David Steier, Distinguished Service Professor, Carnegie Mellon University

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Our Leading Data Academic Leaders List 2022 honors these great academic leaders, introducing them on a global platform where they share their insights and work, highlighting their significant successes in the previous year, the challenges they faced, and their aspirations and goals for 2022.

Introducing: David Steier, Distinguished Service Professor, Carnegie Mellon University, Heinz College

1. What were your most significant achievements in 2021, specifically in the last two years, and why?

Having spent a few decades in research and professional services, I'm now focusing my energies on giving back through teaching. So, my 2021 achievements were:

Helping to create and deliver a number of executive education courses, most notably CMU's Chief Data Officer certificate program, aimed at practitioners solving complex data and analytics challenges in both public and private sector
Delivering courses in master’s programs at both CMU and Berkeley that help students to not only build data skills, but to think critically about what problems they are solving with data and for whom. These include "Managing Analytics Projects," "Data Science for Product Managers," and Capstone classes.
Keeping myself grounded in data with active collaborations with a variety of organizations ranging from the Allegheny County Department of Human Services to Clinicas del Azucar, the largest independent provider of diabetes care in Mexico, to the Acram Group, a real estate investment firm.

2. What challenges do you think you are facing in the academic data field?

Getting students access to raw, real-world data. Too often, students use whatever toy or synthetic data sets they are given and just focus on the modeling. While modeling is certainly important, working with raw data from the field gives the chance to ask important questions — How good are the data? How does the data need to be prepared for modeling? And, finally, what other data could be used in conjunction with what they have to generate even more value? That is a question students don't often ask.

3. What traits and qualities are required to be a successful academic data leader?

Given that a key responsibility of an academic data leader is to prepare the next generation of data leaders, leaders should recognize that there are many ways for students to excel with data. They could become data scientists, sure, but they could also be data engineers, architects, analysts, or specialists in some other field who are particularly adept with data-driven tools and techniques. Academic data leaders need to cultivate talent in all these roles, with enough knowledge and respect for other specialties to be able to have civil conversations whenever members of a data team need to work together — which is always.

4. Tell us about your priorities in 2022. What are your key targets?

We've been experiencing unprecedented demand for our data classes, both at the executive education level and for the full-time programs. So, while I'm looking forward to teaching as many students as I can myself, in 2022 we have to increase our capacity to deliver. We are actively recruiting both teaching faculty and coaches with data experience who can advise teams as they work on the challenging (but fun!) projects that are a part of every one of our classes.

5. What advice would you offer aspiring academic leaders to help them prepare for the role?

Never assume that just because you give someone the right data, or even the right analysis of that data, they will do what you want. To adapt Peter Drucker's quote, their values will eat your data for breakfast. Everybody brings their priorities and their biases to the table (even you). I've seen too many great analyses sit on a shelf because somebody didn't want to act on the results. So, make sure you are solving a problem that somebody wants solved, and figure out how the results of your analyses need to be framed for a given audience to take action. Yes, we are getting into psychology here (but I'm the son of an engineer and a psychologist, so I can't help it, it's in my genes). Combining data science with behavioral science might lead to some of the biggest opportunities for data leaders of the future.

David Steier joined the faculty of Carnegie Mellon University in 2018 as Distinguished Service Professor in the Heinz College School of Information Systems and Management. Prior to joining CMU, David was Managing Director in Deloitte Consulting's Data Science practice. Prior to Deloitte, David was Director in the Center for Advanced Research at PwC, Senior Director of Technology and Business Development at Kanisa, and Managing Director at Scient. In addition to his CMU affiliation, David is also a Lecturer at the University of California Berkeley's School of Information, where he is course lead for the data science capstone class in the Masters in Information and Data Science program. David holds a Ph.D. in computer science from Carnegie Mellon and a bachelor’s degree in computer science from Purdue University.