Dynamics of data science skills: How can all sectors benefit from data science talent?
This report sets out what is distinctive about data science as a discipline and offers some key statistics from our commissioned research to show the level of growth in demand for a range of data skills. We explore the different drivers and blockers around data science roles in different sectors, and highlight examples from a body of case studies of data science careers and career mobility across sectors (the complete portfolio of case studies is available as a separate publication). Engagement with data scientists, analysts and other data users informed us about some opportunities to foster talent and to enable movement between sectors. The interdisciplinary nature of data science lends itself to joint appointments, and its applied nature fits with apprenticeship mechanisms. The following sections set out recommendations and activities across four major areas for action, with recommendations targeted across government, funders, universities, industry and the public sector to make progress towards a thriving data science landscape. They also contain a range of mechanisms for developing and sharing skills across sectors, highlighting examples of models that could potentially be replicated, scaled up and expanded. Accompanying this report is a detailed set of case studies featuring career stories of data scientists working in a wide range of roles, levels and sectors, including Accenture, the Alan Turing Institute, Channel 4, Cambridge University, DeepZen, GCHQ, Government Digital Service, HSBC, The One Campaign, the Office for National Statistics, UCL’s Institute of Neurology and the University of Warwick. (See Dynamics of data science: what data scientists say about data science.) There is a clear need for collaborative, sustainable mechanisms to develop talent and this report promotes a vision for the sharing of data science talent across all sectors. We have identified a range of models and mechanisms to enable this vision, such as outreach programmes, enrichment and fellowship schemes, capability-building programmes, informal/ peer-to-peer mechanisms, collaborative events and partnerships, data stores and data centres/institutes. All of which will be explored in more detail in the following chapters. The models can also be found in an accompanying document. (See Dynamics of data science: models and mechanisms) Examples of good practice have been collected with the input of members of the data science community from across academia, industry and the public sector. They feature a variety of tried and tested ideas from across the UK, which require minimal to major resource support and can be led by individuals as well as institutions. The aim of the models is to inspire scale-up and cohesion. The models and mechanisms can be used by people who are: concerned about the recruitment of data scientists, data analysts and domain experts; involved in developing data science talent at all levels; considering (re)training as data scientists, data analysts and domain experts; making decisions around skills funding on a local, regional and national scale; or seeking to ensure that data they hold is used for societal benefit.