The policy briefing,Analytic Britain: Securing the right skills for the data-driven economy, was launched yesterday in London. At the launch, Sam Roseveare of Universities UK described “deserts and oases” of data skills teaching across higher education courses in the UK, and alongside Juan Mateos-Garcia of Nesta, outlined a number of recommendations around how to “upgrade data analysis education and skills provision” to address the imbalance of supply and demand for analytical skills in the UK.

The topline recommendations were as follows:

Schools and colleges

• Stronger teaching of mathematics and statistics in schools and colleges
• More and better information about analytical career prospects and role models in schools and colleges
• Embed data analytics in other subjects
• Support development of extracurricular data activities

Universities and vocational education

• Increase the visibility of strong data analytics courses
• Embed quantitative analysis across disciplines
• Boost the business and soft skills of graduates from data analytics courses
• Increase the supply of high-end analytical talent
• Foster interdisciplinary research and skills development programmes

Labour market and industry

• Create a cross-cutting taskforce around data analytics
• Actively convene industry and community analytics networks
• Support innovative interventions enabling local authorities to boost local analytical skills
• Raise awareness of the value of data for business and local public services
• Develop innovative solutions for data analytics training

During a panel debate following the presentation of recommendations, Damian Kimmelman, co-founder of and CEO of DueDil, argued that even at primary school level there is too much of a focus on memorisation of mathematical processes and not enough on analytical skills. Professor of statistics at UCL, Sofia Olhede, pointed out that hiring effectively in the field of data science, as well as assessing the usefulness of new analytical technology, both require a degree of skill that is often lacking.