What can be done about the data science skills gap?
The swelling demand for data scientists coupled with the evident data science skills gap has implications for not only the tech industry, but the global economy. What’s causing it, and what can be done to address it?
Most people working in STEM don’t need to be told that data science is a fast-growing and hugely lucrative enterprise, largely due to the estimated 20,000-fold leap in data volumes between 2000 and 2020.
If data is ‘the new oil’, then the data scientist functions much like an oil refinery, converting data into insights which can both save money and generate capital.
The International Data Corporation (IDC) predicts that worldwide revenues for big data and business analytics will reach more than $210bn in 2020. Having finally penetrated the mainstream, data analytics is now a massive priority for executives in top companies.
Data scientist, in turn, is now being touted as the hottest career to get into, even being dubbed the ‘sexiest job of the 21st century’ by Harvard Business Review. Life is pretty good, job-speaking, for the data scientist, with opportunities aplenty and the constant promise of massive compensation.
There’s just one problem – these conditions are created by the basic economic principle of supply and demand.
Demand is high, but, crucially, supply is low. While this works out well for data science professionals, it could be ruinous for the economy if not addressed.
In 2017, Burning Glass Technologies, Business-Higher Education Forum and IBM came together to produce a report on the demand for data science skills and forecast that the number of job openings for all data openings will increase by 364,000 by 2020, bringing the total to 2,727,000.
Industries such as finance, insurance, professional services and IT are the ones most desperately seeking these skills, accounting for 59pc of the total job demand.
It is the issues associated with recruiting for data science positions that raise the most disquieting issues for the economy at large. Data science positions, according to this report, take 45 days to fill, five days longer than the US average of 40 days.
While this may appear to be a relatively insignificant difference, this increase in wait time can translate into delays on large projects, which normally results in companies haemorrhaging funds as they are left sitting on their hands unable to progress.
Even when the positions in data science do get filled, the costs don’t cease there – for various reasons, including the aforementioned hefty salaries, there is a high cost to hire associated with these professionals.
