Survey: Energy sector seeing major data science skills gap
According to a survey from Energy Systems Catapult, while digitalisation is driving vast opportunities for new services and innovations in the energy sector, proper and optimal implementation will require personnel with the necessary data science skills, of which there is currently a jarring lack.
At least 40% of businesses in the energy sector find it difficult to hire data scientists with the skills needed to meet the challenges of a data-led future, according to the survey, which was recently released by the UK tech and innovation centre.
The results, released in the Data Science Skills in the Energy Sector: Survey Results report, found that the vast majority (68%) of data science teams within the energy sector were created within the last five years, while 39% of teams only had four members or fewer.
Feedback from the respondents – which mostly included utilities and network operators (43%), as well as SMEs (26%), large businesses (14%), research/international organisations (15%) and Catapult’s own network (2%) – highlights the relative immaturity of the adoption of data science within the energy industry in comparison to other sectors, such as FinTech.
This is despite an increasing need and demand for the technical skills and experience of data scientists to overcome the challenges faced by organisations in the pursuit of Net Zero.
Dr Stephen Haben, digital & data consultant at Energy Systems Catapult, said: “We expect that as the opportunities from the energy sector become more evident, there will be a rapid uptick in organisations trying to build their data capabilities. We have already witnessed the gradual occurrence of this over the last five years, and as this is ramped up, it will put further stress on recruitment and training”.
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The survey found that while there is an evident demand for data scientists, domain knowledge and coding skills are two of the most prominent weaknesses and are hindering efforts to produce the operational implementation of the algorithms data scientists develop.
The demand from the sector for defined skillsets was borne out in the survey results. Of the programming languages used, 95% of respondents used Python, while 40% used Excel. As expected, another in-demand skill is energy forecasting, with 95% of data scientists polled identifying this as the most common modelling technique implemented within their teams.
However, the survey highlighted that a diverse set of additional skills were also desirable, including natural language processing and data engineering capabilities.
Filling the data science skills gap: Recommendations
In response to the findings, the report sets out four key recommendations – although not exhaustive – for the energy sector to fill this skills gap:
1. Enable training for future data scientists: Prospective data scientists must have all the resources and training available to them to ensure they can be immediately valuable to the energy sector. This means also ensuring that domain knowledge and communications skills are also included, not just the highly technical data science skills
2. Upskilling: Many organisations don’t have the time to keep the team up to date with the latest methods and technologies. There needs to be easily accessible resources so that cutting-edge research and development in the areas of data science can be identified and used across the sector. This includes making the outputs from academic research openly accessible but also ensuring that the research is shared in easily digestible ways to save time and resources.
3. Reskilling: To reach net zero, decarbonisation will be a gradual transition by integrating existing technology with increasing deployment of low-carbon and renewable technology using a whole-systems approach. As well as upskilling the workforce, there will be a need to repurpose existing skills for emerging technologies such as hydrogen and carbon capture and storage. This will be challenging as we go into an uncharted territory full of uncertainty. Data science plays a crucial role in leveraging what we know from existing vectors and can learn from early adoption of new technology. We will need a workforce that adapts, continually develops data science approaches, and embraces digital tools (such as digital twins) to accelerate to net zero.
4. Support the creation of data science/analytics teams: Recruiting, upskilling and reskilling are irrelevant if the frameworks and infrastructure are not in place for the data scientists to flourish and optimise their outputs. This includes understanding the skills needed within the organisation, that the leadership is chosen appropriately and that the team has the right mix of personnel.
“As digitalisation drives new opportunities for services and innovation, we need to ensure that we have a workforce that can respond.
“If we do not demonstrate the exciting challenges facing the industry or provide the necessary upskilling to the next generation of data scientists, then the energy industry risks losing out to other sectors such as social media and FinTech”, added Dr Haben.
