Identifying the skills gap
In the UK, there are currently over 35,000 data scientist jobs being advertised on Linkedin, yet the government estimates that only 10,000 new data scientists will graduate from university each year. The effects of this are already being seen, as it was found that the impact of too few staff is already leaving 88% of businesses unable to meet customer demands. The lack of skill has also been a hit to staff morale, according to three-quarters of businesses surveyed. These factors combined will mean the cost of recruiting and retaining staff will continue to rise, while productivity and innovation will be held up, meaning organisations risk not achieving their goals from investments made – or about to be made – in analytics and AI technology.
The deficit of skills and skilled staff is already large, and the demand for data science roles is expected to increase as 44% of businesses are looking to further invest in AI technology within the next two years. Already it’s companies’ use of AI and machine learning (ML) that has the biggest skills shortage. Of the 54% of companies currently using the tech, 63% said they have a shortage in the skills required to properly implement it.
Addressing the skills gap
The average data scientist salary jumped 31% between 2018 and 2020 to £60,000. Continued wage inflation at this rate is unsustainable. The report suggests that upskilling staff should be a priority in addressing the skills gap, especially at a time where businesses are looking to save costs. The analysis found that 54% of businesses wouldn’t need to hire as many people if existing staff could properly use the tools and technology deployed.
A short-term investment in training can mean a big saving in longer-term costs. Many senior management remain reluctant to invest in this training, however, as just under half believe staff tenures won’t be long enough to see a return on investment from the time and money spent on training.
Fortunately, as more businesses recognise that graduate and degree holders cannot alone sustain the demand needed, they are turning to other ways of assessing capability. Whilst 62% of respondents said they value degrees from an academic institution, a higher proportion (72%) said they valued completion of relevant courses and certifications, while the same proportion looked at completion of industry recognised certifications. A similar proportion look at case studies and project work that might be relevant (59%) while 56% assess candidates based on their participation in hackathons and data challenges. This reflects the value of ‘real-world’ problem solving and directly relevant experience or training, and how these factors have risen in importance compared to a university degree that might be in a much broader or less relevant discipline.
Solving the skills gap
This is possible by using optimisation techniques to uncover more efficient ways of working. Productivity gains are also possible as analytics can detect, for example, faulty components in machinery that might otherwise lead to business down-time. Aside from these savings, analytics also drives innovation, meaning new business models and revenue streams can be identified.
Nearly all areas of a business can be enhanced through this technology as it drives better, faster decision-making. So the data science skills shortage is not just a problem for technology companies that need these skills, but all other industry sectors that stand to benefit.
These skills can not only benefit business, but there are societal benefits – for example breakthroughs in healthcare such as earlier identification of cancers using computer vision technology, or better understanding how to mitigate the effects of climate change.
Addressing the skills gap is not going to be an option for many organisations. There needs to be a shift in attitudes and working practices around technology and data. Many businesses have amassed a large amount of AI and analytics tools over the years, making the task of using and training people on them unnecessarily complex. The first step for many is to consolidate these and look to use a modern, open, multi-language solution where those with less technical expertise can still make use of basic analytical tools, leaving data scientists to focus on core tasks.
Training from within businesses can include anything from allowing employees to take time out, to completing online training courses to setting up in-house data science academies to ensure a continuous supply of talent, with consistent and business-outcome focussed on-boarding and mentoring programmes. A good-quality technology vendor, with a focus on education, can support organisations with all of these training approaches.
There are other ways to increase the supply of talent. The SAS STEP Programme was launched during the pandemic with the aim of helping 10,000 job seekers across the UK and Ireland develop data skills. It is free to access and has four learning pathways from basic data literacy to the more advanced data science course. It provides opportunities to work on real business challenges and connect with employers.
Whilst still a large threat to future innovation and advancements in many sectors, UK employers are not powerless against the skills gap. Any organisation, whether large or small, can create environments to improve data skills among its workforce. As awareness of this challenge grows, young people who are data literate should become par for the course. But it’s important existing staff acquire the more advanced AI skills needed now – only then will we see the gap close and UK productivity increase.
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