But without ensuring teams are made up of expertise, experience and the right abilities, these investments will not give them the competitive edge they are looking for.

The shortage of skilled professionals in the field has been a hot topic as of late, presenting critical roadblocks in reaping the benefits of automation and data science. The rate of development in areas such as machine learning and automation are simply outpacing the availability of skilled talent. Digital skills are not static, but rather constantly evolving in the ever-changing world of technology. Within such a rapidly advancing digital landscape, adaptation is key. And those who fail to keep up will find themselves getting left behind.

Two-thirds (66%) of large UK businesses said they struggle to recruit employees with the skills they need. Data understanding and business acumen are essential drivers of performance in the sector. As a result, expensive and experienced team members are often required to undertake tasks that could be efficiently handled by lower-level employees with appropriate training.

Therefore, not only does the rampant skills gap come with reductions in productivity, greater employee turnover and low morale, this can in turn exacerbate economic problems. In fact, research has revealed that a shortage of digital skills is costing the UK economy £12.8 billion.

We are at an inflection point where if we want to deliver on initiatives of agility and innovation by harnessing the power of machine learning and AI, organisations must find better ways to acquire the right talent.

Why new starters are currently being failed

Those higher up are too busy with lower-level tasks to provide sufficient training and onboarding sessions, new starters are left to fend for themselves and immediately placed on the back foot. It’s common knowledge that supportive and collaborative environments underpin growth and continuous learning. However, some companies, often through lack of resources, may not be able to provide this environment of knowledge sharing, collaboration and mentorship.

The knock-on effect means new starters may also be limited in their access to real datasets and projects. Without access to real projects, it’s a challenge to build the skills needed for today’s data scientist roles. Data science is a highly practical profession, and hands-on experience is essential for mastering the tools and techniques used in real-world scenarios. Consequently, this all inhibits employee development, progression and ultimately, the company’s cultural and financial growth.

New starters bring new experiences, knowledge and skills that can freshen up a company’s processes and ways of doing things. With the right training, they can take on entry level tasks and free up time for more experienced professionals to focus on value-adding tasks. 

Yet many are coming into the industry ill-equipped to the current landscape and its demands.

Investing in the next generation of data scientists

Currently, the skills pyramid is weighted heavily towards the top. What’s needed is a fresh stream of talent to create a more sustained flow of skills from the bottom up. Yet there is a disconnect between education institutions’ curriculums and real-world needs.

The World Economic Forum published a report earlier this year to kickstart a drive for educators to take a ‘skills-first’ approach. The report highlighted a significant gap, identifying “that education providers (on the supply side) and the business community (on the demand side) do not have a common language when it comes to talking about skills.” Education often remains stuck in a knowledge-first approach, whereas the industry requires technical skills, social skills and adaptability.

While the report is predominantly aimed at education with children, businesses can replicate the same approach with higher education institutions as well. Partnerships can be created with universities, technical colleges and incubators to give students insights into how skills are used in the workplace and real-world settings, while fun events such as ‘hackathons’ can also be used as effective ways of building skill sets and spotting talent. The stronger the link between business and education, the easier it is to fix the pyramid.

Considerations for employers in launching careers and bridging the skills gap

The growing demand for data science skills brings questions of how best to fill the void. Inevitably, reskilling programmes are going to have to be implemented - there is plenty of untapped potential in the workforce and reskilling needs to be standard procedure. It’s a mentality and process that lends itself to nurturing new hires and launching careers.

Continuous training can be provided through vocational coaching, external placements, mentorships and internal upskilling programmes. These programmes can keep emerging data scientists up-to-date in key areas such as the latest programming languages, machine learning techniques and data visualisation tools.

Adopting a balanced structure of teaching and hands-on projects can help bring together theory and practical skills whilst also building valuable industry connections. This ongoing training is vital for continued development and to make sure careers don’t fall flat. This will all, ultimately, help bridge the skills gap.

Empowering the next generation

The AI and data science world is a rapidly evolving field, and it’s essential that companies can stay at the forefront of the latest innovations. According to the US Bureau of Labor Statistics, the number of jobs requiring AI skills is expected to grow a massive 27.9% by 2026. Demand for AI skills is outstripping supply, and this imbalance won’t be evened out unless companies invest in the next wave of talent.

With the right investment in training and experience, businesses can reshape their internal hierarchical structure to unlock the full potential of machine learning. Although the foundations for bridging the digital skills gap rest on empowering the next gen of data experts.