How organizations can close the data science skills gap amid a shortage of talent
Turning raw numbers into useful, valuable insights requires the help of professionals highly skilled in artificial intelligence (AI), machine learning (ML) and data analytics – and it’s no secret that this talent is in short supply.
To better understand what organizations are experiencing firsthand, SAS and Coleman Parkes Research surveyed key decision-makers in 111 major organizations across the US and UK/Ireland with an average of 27,000 employees and developed this recent report, How to Solve the Data Science Skills Shortage, which sizes up the skills shortage, examines the broader impact and proposes a path forward.
According to Microsoft’s DEGREE + DIGITAL report based on LinkedIn data, there’s no skill set with a more significant disparity between supply and demand. And, based on projections from the U.S. Bureau of Labor Statistics, the demand isn’t expected to let up anytime soon. The bureau’s Office of Occupational Statistics and Employment Projections reports that the data science field is expected to grow by 36% from 2021-2031 – significantly faster than the average profession.
Many organizations are putting pressure on universities to ramp up their efforts to train graduates in high-demand data and analytics skills. Unfortunately, the outlook is grim.
According to estimates from the UK government, universities supply up to 10,000 data scientists annually. Meanwhile, LinkedIn boasts 38,000 job postings for data scientists in the UK– while the US suggests around 320,000 job postings. These figures, plus the rapid pace of technological change, show that institutions of higher education can’t fill the gap alone.
So, how can organizations close the skills gap and make real, sustainable progress toward their organizational goals? Rather than solely relying on academia or poaching talent, what can be done to secure necessary analytics skills?
Surveying key decision-makers, the report found three top organizational priorities: improving innovation (34%), improving workforce productivity (32%) and increasing organizational agility (31%). Organizations lacking AI, ML and data analytics aren’t simply missing out; they’re also at risk of falling behind – threatening their overall resilience and competitiveness.
Because organizations focused on innovation are hungry to adopt emerging technologies, many have accumulated diverse tools over the years – creating a tangled web of resources. Consolidating AI and analytics tools to maximize their impact will ease the burden of bringing current and future employees up to speed.
By consolidating tools around modern, open, multi-language tools, organizations can improve the learning curve for end-users undertaking basic analytics, reduce inefficiencies and get the most value out of their data scientists by helping them focus on core tasks.
For many organizations, solutions like SAS® Viya®, which enable employees to use open-source coding, may be a good step forward.
