I recently motorcycled from sea to shining sea – almost. That is, from Charlottesville, Virginia, to San Diego, California, a total of 2956 miles in 10 days on the back roads of America. You can read about the trip in my travel blog if interested. What I want to focus on here is what I observed during that trip that is relevant to a dean of a school of data science and how, as a school within the University of Virginia, we should think about acting on those observations. Some of what follows is perhaps obvious, but, as they say, seeing is believing.

Small towns across America, many in beautiful areas, are in decline. Stores are shuttered, motels in ruins, weeds growing everywhere from lack of upkeep, and a general atmosphere of malaise hangs in the air. In contrast, a few towns have reinvented themselves, mainly for the benefit of tourists which at least indicates what can be done. In the post-COVID era when remote work is a new norm, opportunities exist since people no longer have to move to big cities to work. What contribution could we make toward revitalizing small-town America? We have faculty in the School of Data Science who study urban landscapes and influence the policies that govern such landscapes and we have colleagues in the School of Architecture and elsewhere who do the same. In the emergent economy associated with the digital transformation of society, there must be opportunities to work towards revitalizing these areas which, after all, are affordable.

This alludes to the need for changes in where work is done. Equally needed is how work is done. In Fort Scott, Kansas, I met Bill, a retired engineer who worked for the Ford Motor Company for 30 years. One study found that data scientists stay at one job for an average of 2.6 years. Our industry is not representative since jobs are plentiful and upward mobility achievable. Notwithstanding, the average duration in any job has declined over the years for a variety of reasons, but, to me, a big one seems to be corporate profits which I equate with less concern for the worker. The idea that you would be loyal to a company, as Bill was, and that company will be loyal to you, seems to be a thing of the past. Going beyond the worker, corporate profits are tied to mergers and acquisitions and the end result is a downer for the consumer. Less choice, less competition, less value, and spiraling prices as a result. You only have to stay at a hotel/motel along the B roads to see the full effect. Boring, poor service, lousy food, etc. The family-run motel is fast becoming a thing of the past. Our economics and business-trained data scientists have work to do to create interventions that change this trend.

The level of education varies widely between urban and rural areas. In part due to the availability of educational institutions, teachers, and mentorship and in part because of financial pressures, more prevalent in rural communities, to join the workforce at an early age. What can we do? As we put in place our version of life-long learning programs like our Bridge PLUS Program, we need to pay particular attention to current and potential students from rural and other socio-economically disadvantaged areas. Focusing on the Commonwealth of Virginia, we need to work with our colleagues at the University of Virginia’s College at Wise in southwest Virginia which provides a valuable testing ground. We need to work hard to acquire philanthropy that provides needs-based scholarships. I was a first-gen student but in a different circumstance and era. Circumstance, because my parents were not poor and tertiary education was free (in Australia)! Era because you could get a well-paying job with a high school diploma. Times have changed and we need to respond with ever greater effort.

America is big and beautiful, as ten days of motorcycling attests, but it is also saddening to see such great disparity, which I equate as the root of many of the problems that exist in America today. Such disparity would seem to be worsening over time. As data scientists we have an obligation to reverse this trend.