If there were ever a role that debunked today’s obsession with technology stealing our jobs, it is that of the data scientist.
The growth in artificial intelligence, data science, and big data analytics has created a jobs boom, with 2.7m new jobs forecasted globally by 2020.
The term summons up images of white coat-wearing people, beavering away on arcane topics of only theoretical interest to their employers. But the reality is quite different – both in terms of the role and its practical importance to the organisation.
Instead of fearing a lack of jobs, we should all be deeply concerned about the shortage of data scientists, which could soon be costing billions in lost opportunities across a range of industries.
What do data scientists do all day?
Money may make the world go round, but it’s data that greases the wheels. Each day, people and organisations create around 2.5 exabytes of structured and unstructured data. That’s a whopping 2.5 billion gigabytes. But this information – thanks to its size, format, and dispersal among so many different platforms and silos – is a wasted asset without data scientists who can translate the raw data into insights, and consequently solve real-world problems.
I must interject here to say that a good data scientist is more than a mathematician, statistician, or writer of algorithms – although these skills are obviously central to the role. The government defines data scientists as “hybrid experts in analysis and software programming (who) possess strong business acumen, coupled with the ability to communicate findings.”
This means you can’t drop a statistics graduate straight into a data scientist position and expect them to start delivering insights from day one. The job requires far more than theory – data scientists need to have a thorough understanding of the domains in which their insights will be applied. So on top of maths, data engineering and visualisation, a data scientist might also need a high level knowledge of supply chain, finance, logistics, human resources, or any other line of business.
Little wonder, then, that data scientists are so valued – and why they are in such short supply.
A role with serious ROI
Good data scientists don’t come cheap. Fresh graduates achieve starting salaries in the $100K+ range, the figure significantly higher for more experienced experts.
But, used wisely, data science is one of the best bargains a business can make. By turning billions of bytes into actionable insights, data scientists can solve long-standing business problems, identify inefficient processes, develop new revenue streams or markets, improve data security, enhance customer service, develop tailored services – and provide answers to the unknown.
And while data science might once have been a luxury, that’s no longer true. It’s business benefits – such as accelerated time-to-market – make the discipline compulsory for organisations in practically every industry. But finding the right talent is becoming increasingly difficult.Read more:Qantas Credit Union to replace core platform
Skills in short supply
As we move into the year, regulatory changes like Open Banking will dictate an entirely new paradigm – one where users have more rights around how their data is used. In fact, last year’s Productivity Commission Data Availability and Use report gave multiple recommendations around how this will play out, and advised that new legislation will be in place by the end of 2018. In a world where customers finally have a say over their own information, managing this newly complex network of data will be in the hands of data scientists.
But the scale of the data science skills shortfall is so large, you’d almost have to be a data scientist to make sense of it. And the low number of data science graduates is only part of the problem. As we’ve discussed, no matter how many technical skills they have, graduates need several years at the coalface where they can learn to apply theory to the specific business challenges that they have been tasked to solve.
Turning talent into tangible impact
How do we access raw talent, and then commoditise these skills so that they can be applied to different areas of the business? In Silicon Valley, businesses take the cream of the graduate crop and put them to work in the research and development department, where it is hoped they will add some value while they learn enough about the business to be useful elsewhere in the organisation.
Another approach is to groom industry-ready university talent. This is possible if there is a watertight engagement between academia and industry, and possibly government funding agencies. For instance, a part of the curriculum that graduates undergo could be dedicated to gaining industry knowhow through paid internships and a dissertation on the practical problem addressed by the would-be data scientist in the company that they select.
Back home on Australian soil, organisations are taking matters into their own hands by upskilling existing employees – and filling the data science gap themselves. Rio Tinto and ATCO are supporting WA-based professional development platform CORE Skills in retraining geoscientists in the data sciences realm, enabling them to re-enter the market with an unparalleled understanding of the geosciences industry as a foundation to their newfound data science capabilities.
From the top down: support from Australian authorities
The Australian government’s new APS Data Skills and Capability Framework is a small step forward in the giant leap towards functional national data capabilities, with a Data Fellowship programme, additional university courses, an APS Data Literacy programme and multiple Data Training Partnerships in the works.
With specialised data analytics courses and support from tertiary institutions across Australia, the APS will improve technical data analytics capabilities across the country, boosting available talent and filling the much-needed gaps in the industry.
The assistant minister for cities and digital transformation, Angus Taylor, said that “it is our vision that the government will set an example by cultivating a workforce that has the skills and capability to get the most value out of data for all Australians.”
Whichever of the above approaches is most effective will take time to ascertain. In the shorter term, businesses must develop a strategy for how they acquire the talent they need, including offering the right incentives to attract the best data scientists and on-the-job training to ensure that they can start delivering value as quickly as possible. It might seem expensive, but businesses should see it as strategic, and as important as any investment they will make over the next decade.