Data scientist might be considered the “sexiest job of the 21st century,” with many companies eager to hire data science talent. But 20 years ago the term was barely used, save for a few academic articles.
Even a decade ago, explaining data science to employers was challenging. Few people understood the value of a skill set that combines computer science, statistics, operations research, engineering, business insights and strategy — and the impact it can have on a business.
But things have changed over the last five years. Not only has the term “data science” become commonplace, but data scientists have become highly sought after in the marketplace and lauded in the press.
After all, it was data science that gave consumers personalized shopping recommendations on websites like eBay and Amazon, customized entertainment recommendations on Netflix and Spotify and tailored content and advertising on Facebook, Twitter and Pinterest — all at tens of millions of dollars (if not more) of revenue upside for these businesses.
Over time, organizations started to see the value of data science as the volume of data they had to analyze increased dramatically. For instance, today an estimated 2.5 quintillion bytes of data are created daily. Within this voluminous quantity of data lie insights that could enable companies to gain a competitive edge and provide a better consumer experience, which is why many organizations have now made data science a top priority.
In a KPMG survey of C-suite executives, 99 percent said that analysis of big data was important to their strategy for next year. As the study stated, “Today, the issue is no longer about owning the most data but rather … how to turn data into insights.”
Which is where data scientists come in. The role of a data scientist is to mine enormous volumes of data and pull out key insights. In an era where enterprise-generated data is expected to exceed 240 exabytes daily by 2020, the need for experts trained in extracting insights from data is more important than ever.
So what’s the problem? We have a global talent shortage, and the demand for data scientists continues to grow rapidly, far outpacing the anemic growth in supply. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.
The need for experts trained in extracting insights from data is more important than ever.
The global talent drought may be partially caused by the dearth of universities that offer data science programs dedicated to preparing the next generation of data scientists. Fewer than one-third of U.S. News & World Report’s Top 100 Global Universities offer degrees in data science. Of the 29 universities that offer data science programs, a mere six make them available to undergraduates (the rest are reserved for graduate students).
Moreover, the average cohort size for one of these data science programs is just 23 students. The small cohort size may be partially attributed to the fact that most of these data science programs tend to be offered to graduate students, and sizes of graduate programs tend to be smaller. Still, at 23 students per cohort, we are unlikely to make a meaningful dent in closing the global data science talent gap.
An analysis of the curricula for these data science programs also suggests cause for concern. Most are focused almost exclusively on the computer engineering aspects of data science, with course titles such as Software Design, Parallel Computing and Software Development.
Largely missing from the data science curricula at many of these universities are courses in statistical analysis, insights and strategy. This oversight may have serious consequences for graduating students and their future employers.
Without training in these other areas, data scientists may be capable of designing an algorithm that is mathematically elegant, but doesn’t make strategic sense for the business. They also may not know how to design an experiment to determine whether the algorithm is effective. In other words, computer science skills alone are insufficient for success as a data scientist in today’s marketplace.
Furthermore, many data science programs lack courses that help students apply their technical data science skills to fields such as marketing, operations, product development and supply chain, and industries such as energy, bioinformatics, transportation and healthcare.
While there are some universities that have distinguished themselves for having curricula that focus on the multi-faceted aspects of data science, such as New York University, there are still far too few universities producing enough graduates to meet today’s growing demand for data scientists.
There are three steps we can take to help stem this talent shortage:
- Increase the number of universities offering data science degrees: To accelerate this, employers can partner with universities to help design and fund the creation of these programs. Accenture’s recent partnership with the University of California, Berkeley to create a new Big Data and Data Science curriculum is one such example. As part of the partnership, Berkeley’s Haas School of Business collaborated with Accenture to create new classes and a lecture series to equip MBA students with the skills needed to analyze big data, including a new course on data analytics.
- Offer data science programs for undergraduates: Enrollment in data science programs must radically increase — cohort sizes of 23 students simply will not close the talent gap fast enough. Offering data science degree programs to undergraduates as well as graduate students can significantly expand enrollment. Universities also should consider offering courses online and during evenings and weekends in an effort to reach non-traditional students.
- Launch programs that train analysts to become data scientists: Many organizations are flush with analysts who typically have some data science skills in key areas such as statistics. Zynga, for example, recently started a Data Science Transition Program, which enables high-performing analysts to be apprenticed to a data scientist for twelve months, during which time they learn data science skills on the job while taking online data science courses. At the end of the program, the participant becomes an Associate Data Scientist at the company.
Closing the global data science talent gap won’t be easy, but it’s critical to ensure companies have the ability to make data-driven strategic decisions. If companies and universities work together to address the problem, we can better prepare both students and employers for success.