The skills gap is set to widen unless there are systematic ways to address it. We see eight actions to take now.
Today’s tight market for data science and analytics (DSA) skills involves data scientists, but it extends much further to existing job classifications from the C-suite to frontlines—all of which are increasingly enabled by analytics.
And when we look at the talent pool coming out of American colleges and universities, too few are likely have the skills employers are looking for.
To help you think about what this means for your business, we've worked with the Business-Higher Education Forum to get at heart of the disconnect between educators and employers.
The modern marketer, the new human resources professional, the 21st century finance and accounting manager all need to be able to self-manage data and command powerful analytical tools. Jobs in the data sciences are booming, from analysts, to engineers, to scientists who create data models that predict what is going to happen or prescribe what should happen.
The phenomenon is widespread. Every major industry is growing its soft-quant and hard-quant workforce: from transaction-heavy financial services and retail industries to data-intensive health industries to fast-transforming manufacturing and knowledge-intensive industries like information and professional services.
Closing the skills gap will require change. Business will need to structure hiring and workforce planning less by traditional skillsets and more by the set of DSA skills needed to build cohesive, multidisciplinary teams that can deliver business results.
For higher education, there are great opportunities to build capacity and to attract more students to DSA coursework, helping them prepare for the changing nature of work.
1 Hire for skills, not only diplomas
Clarify demand for skills 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
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 paths that lead to a diverse analytical workforce
Find out how in Investing in America’s data science and analytics talent, a joint report from the Business-Higher Education Forum and PwC.