The Business of People: The Significance of Social Science over the Next Decade, launched yesterday (24 February 2015), recommends that greater use is made of administrative data for research purposes, and that more investment is made to ensure that there is a sufficient skills base to do this.
Specific recommendations relating to big data include:
- Equipping the Economic and Social Research Council (ESRC) to support innovation in collecting and analysing big data and new forms of data
- A strategy that 'embraces' collection and analysis of government statistical data, including ONS data, the census and the UK Data Service
- Continuing the Cabinet Office’s recent work on creating a statutory presumption in favour of sharing de-identified public administrative data for research purposes (this work is explained in a recent StatsLife interview with Melanie Griffith of the UK Data Service, 9th paragraph from the end).
The report points to projects under way exploiting government data, such as the ESRC's Big Data Network, which is creating opportunities to better understand patterns in retail trading.
The report also notes that sustaining the R&D budgets of the UK Statistics Authority and Office for National Statistics was 'vital' and 'could be better coordinated'.
Other recommendations made in the report include the appointment of a chief social science adviser to work alongside Sir Mark Walport, the government chief scientific adviser, and pledging a real terms growth of at least 10% over the lifetime of the next parliament (while the £4.7bn science and research budget has been ‘ring fenced’, over the five years to 2015-16, it is estimated to have lost £1.1bn in value).
Hetan Shah, executive director of the Royal Statistical Society and member of the report's working group, said ‘Good social science is underpinned by good data. We welcome this report’s proposals to support the UK’s longitudinal datasets, to strengthen data sharing in government for research purposes, and to invest in statistical literacy for all social scientists. This echoes the calls we have made in our own Data Manifesto.’
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