Led by data experts from academia, industry and the public sector, the Royal Society is exploring trends in the movement of people across sectors to better understand data science skills needs.
The accelerating exchange and use of data is affecting everyday lives, activities and communities in new and unexpected ways. Data-enabled technologies can stimulate innovation and efficiency in public services. They can provide evidence for research, deliver societal benefits and increase productivity.
To realise the benefits of data and digital technologies it is necessary to train and develop a skilled workforce. There is a high demand for people with data science skills across all sectors, with specialists in the field being highly sought after everywhere from government departments to technology start-ups. Additional resources to increase this talent pool are critically needed. The UK needs to take a series of ‘no regrets’ steps to build and embed digital literacy in order to prepare for possible changes in the employment landscape, as the fields of machine learning, artificial intelligence and robotics develop.
This project is exploring these skills needs and specific demand challenges which may be holding back the development of new technologies. Through a series of interviews and case studies it will identify ‘locked in’ points in the landscape, and some of the common motivations for movement.
The project is working towards identifying a range of models and mechanisms to upskill, retain and share data science talent across sectors – from individuals building careers across sectors to ways of enabling access to data – all to ensure that we have a thriving data science landscape. It will conclude in Spring 2019.
Terms of reference
The aims of the Dynamics of Data Science project are to:
- Identify what may be different about the data science research landscape in comparison to other disciplines, specifically the movement of talent and skills between academia, industry and the public sector, and understand the lessons that can be learned from other disciplines. The scope of this project will exclude specific focus on experimental scientific data
- Consider the interplay between dynamics of data and data science in terms of how access to data influences the movement of skills, and how data management and governance practices create need for certain data science skills, such as data cleaning
- Identify lessons to be learned from industry’s success in data science and how universities, smaller businesses and the public sector can best support research excellence and innovation, and access data science talent
- Investigate the mechanisms, models and policy options that would enable a stronger business-university-public sector relationship in data science, including mechanisms that would allow researchers to thrive in multiple roles across the research landscape at the same time, or move with ease between them
- Make proposals for future university-business-public sector interaction to ensure the UK can capitalise on its comparative strength in this area, use data science skills for public benefit, and ensure that learning and good practice spread as fast as possible. These will be focused on practical actions that can be taken
- Build on and support cross-Society messaging on STEM skills, research culture and research translation, in order to address the issue of overall supply of talent in data skills
Who will inform this project?
This project is guided by a Working Group.
The project is also informed by a series of evidence gathering events, involving a range of stakeholders across academia, government and private sector.
What will come out of the project?
The project will report in Spring 2019, with a slide pack summarising the evidence and case studies.