The word “data” has come to connote both immense power and possibility in today’s constantly evolving global economy. Almost no sector remains untouched. The promise of “data-driven” decision-making and problem solving now presents exciting opportunities: an ability to uncover predictive insights across disciplines as diverse as consumer behavior and political allegiances, business efficiency, human health, and natural phenomena.
As a result, there is a fast-growing need for workers with the skills to design research initiatives, and to analyze and understand the results. In one study , the professional networking behemoth LinkedIn found a 650 percent increase in the number of Data Scientist roles since 2012 — job growth that is second only to Machine Learning Engineers, roles that also require an advanced understanding of data analysis.
A second report  from The World Economic Forum suggests that this trend will continue: “data analysts will become increasingly more important in all industries by 2020.” Crucially, though, organizations are already facing a significant lack of talent.
Meanwhile, everyone from university seniors to workforce veterans is clamoring to advance their careers, pursue new opportunities, and prepare for an age in which multiple professions are becoming obsolete. Contrary to some alarmist headlines heralding the elimination of human capital, there will still be an enormous need for talented people, but the nature of this need will shift dramatically. In fact, this shift is likely to increase the number of roles that require a firm understanding of both data analysis and creative problem solving. According to the same WEF report, organizations “will need help making sense of all of the data generated by technological disruptions.”
So, how can students and professionals make themselves indispensable in this new reality? Many may be inclined to enroll in bootcamps, or traditional graduate programs focused on computer science and data analytics. However, I encourage deeper consideration of the skills that will actually make workers indispensable: those that enable adaptability in a variety of dynamic contexts. Remember that while data can provide valuable insights to support a narrative, those stories don’t exist in a vacuum. The real world is far more complex, and messy, than compact phrases like “data-driven” suggest.
What’s more, the views data presents can be distorted on many fronts. Whether from intentionally nefarious efforts like cyber attacks and fake news, or from unintentional missteps in collection methods or database maintenance, the quality and utility of data are frequently compromised. This “noise” requires skilled interpretation, especially of multiple or interrelated data sets. But more than the need for clear analysis is the ability to act on it, and the wisdom to anticipate the effects that those actions might produce.
In my view, the best leaders and innovators of the future will be data-informed, not data-driven. The ability to guide organizations of all kinds will require a blend of skills that span traditional disciplines. In addition to analytical thinking and understanding of data science, students should look for ways to build other core competencies like creative thinking, communication, and collaboration. Moreover, some of the “hard” skills being taught in many programs — from data scraping and cleaning to graphing and visualization — will likely be automated in the near future.
Emerging leaders should seek programs specifically oriented toward developing capabilities beyond those that can be acquired by wielding a statistics toolkit, completing a Python course, or mastering current data science tools and methodologies. Graduates should learn to work with complex sources of evidence to find rational conclusions and apply their thinking to real-world challenges. They should be able not only to analyze data, but also to use insights to devise potential solutions, evaluate the implications of each choice, and select the best one.
At Minerva, we offer a Master of Science in Decision Analysis (MDA) program designed to help students understand a complex world and put that understanding into practice. Combining data science with elements of a traditional business program, the curriculum emphasizes development of competencies that are more fundamental — and therefore more flexible — than analysis alone. This, I’m confident, will help graduates advance their careers, but more importantly, make informed decisions about significant global challenges.
Looking past the current fervor surrounding Big Data and data-driven decision-making, the future depends upon a much more holistic, ethically informed use of that data and the insights it yields. Like any tool, the final utility of data science will be determined by the intentions — even the dreams — of the people who use it.
1 LinkedIn “U.S. Emerging Jobs Report,” 2017
2 World Economic Forum, “The Future of Jobs Report,” 2016