Solving the “skills gap” that exists between outgoing college graduates and incoming new hires has taken on increasing urgency in the US and around the world amid growing economic inequality and rapid technological innovation. To scale opportunity and impact in addressing this challenge, which sectors should government-industry-academia focus on and what kind of systemic changes do we need to pursue? Earlier today, PwC and our partner the Business-Higher Education Forum (BHEF) introduced a groundbreaking report which answers these questions and many others. “Investing in America’s Data Science Talent: The Case for Action” provides a comprehensive look at the skills supply and employer demand deficit and outlines a roadmap for vital employer and educator transformation to ensure that the US economy and workforce—through reskilling and upskilling in Data Science and Analytics (DSA)—are able to “unlock the promise and potential of data and all the technologies that depend on it.”
According to the report, “…by 2021, 69% of employers expect candidates with DSA skills to get preference for jobs in their organizations. Yet only 23% of college and university leaders say their graduates will have those skills.” It’s important to understand that this gap does not just apply to data scientists in training looking for jobs in Silicon Valley. We are all living in a big data world, and DSA skills will soon be fundamental at every level of the workforce from entry-level to CEO.
Addressing the Data Science Skills Gap
As the report notes, “the current shortage of skills in the national job pool demonstrates that business-as-usual strategies won’t satisfy the growing need.” Recommended actions include:
1. Hire for skills, not only diplomas: Clarify demand with signals that motivate educators and job seekers
2. Be bold with investment: Invest in market-driven programs that link learning with work
3. Know the roles: Structure your people plan for the digital economy
4. Prioritize lifelong learning: Modernize training and development for long-term employability
For Higher Education Stakeholders
5. Create hubs, not silos: Use data science to build multidisciplinary strength
6. Champion data literacy for all: Enable all students to become data literate and open more routes to data science
7. Step up professional ties: Strengthen alignment with societies that drive professional conduct
8. Design for inclusion: Expand the pathways that lead to a diverse analytics workforce
The Challenges for Institutions
To support higher education institutions in this important transformation, Wiley has been working closely with BHEF—the oldest membership organization of Fortune 500 CEOs, college and university presidents, and other leaders dedicated to the creation of a highly skilled future workforce—to develop a DSA competency map aligned with industry needs that would also form the basis of a DSA fundamentals course. Leveraging our experience in competency-based learning, BHEF and Wiley hosted workshops with both industry and academia to assess and bridge the requirements of both sides to not only define relevant competencies and skills but validate the feasibility of a three-credit undergraduate course targeting a non-computer science or data science major. Together, we agreed that a DSA-enabled graduate possesses the individual and team skills necessary to identify appropriate data and request, consume, capture, and synthesize data and information to develop and communicate data-driven insights that drive value.
We learned a lot in this process. While industry was very clear about the skills gaps they see in the workforce, it was important for them to hear about the challenges faced by academia in determining where in the curriculum a course like this fits. In essence, how do we create a place in the two- and four-year university setting for the DSA-enabled student so desired by employers? Part of the challenge and opportunity is to help faculty integrate the DSA curriculum into their specific disciplines to build a diverse analytical workforce. Another takeaway is that since most schools teach data science in many different departments (math, stats, engineering, social sciences, etc.), a multi-disciplinary foundational course can be used as either an elective in Gen Ed or, for example, a required course that is part of a new Minor in Data Analytics program being developed by The City University of New York (CUNY) for their undergraduate Sociology Department.
Today’s PwC and BHEF report highlights the urgent need to increase the pipeline of DSA-enabled students and workers. Wiley continues its work jointly with BHEF and other stakeholders in solving this challenge and creating pathways to opportunity in higher education and the workforce.
Please contact Lydia Cheng if you’d like more information about the Wiley-BHEF DSA course.