Key Challenges Strategic Advisor For Data Resources Professor Vernon Gayle University of Stirling MRC, London 12th February 2010

Structure of 10 minute presentation Current position of the UK data infrastructure Overall data strategy objectives Changes in the economic & political climate Five key challenges 1. Balance between stability and innovation 2. Identifying portfolio gaps and overlaps 3. Appropriately engaging with new data sources (examples) 4. Advances in information technology 5. Research capacity building

The Current Position UK is well resourced with social science data Strong social science infrastructure Two decades of increasing global connectivity Improved data archiving, curation and access Thousands of electronic files Multipurpose (omnibus) data resources Strength in household panel data Strength in birth cohort data

The Current Position The National Data Strategy Successful in their aims to provide a coherent framework for the development and maintenance of a robust data infrastructure ensuring that relevant and timely data are available to inform and address future research priorities emphasise the importance of creating better resources for the study of complex research questions

Overarching Data Strategy Objectives Maintain (and extend) UK position in the global knowledge economy Contributing the UK economic competitiveness Supporting the highest quality social science Supporting high-quality research informing public sector, third sector, government and business Data must reflect the complexities of contemporary social and economic life Ensuring data investments provide their fullest analytical returns

Changes in the Climate Moving into uncharted territory… The UK economy & pressure of public finance High levels of expenditure on data collection? Political change Potential change in political leadership Reorganisation of government departments Regional variations in government

Key Challenges Striking the (delicate) balance Sustainable resources versus innovations Stability (esp. longitudinal data resources) Comparability (esp. cross-national data) Flexibility (reacting to emerging issues) Emphasis on multipurpose data resources but also remembering the role of adhoc data

2. Identifying portfolio gaps and overlaps - example of ageing in Scotland - overlapping MCS & GUS data - developing resources in an international context - collaborating with data stakeholders

3. Appropriately engaging with new data sources - “Richer data resources” * e.g. inclusion of health/medical data - Data Linking (esp. administrative data) - Qualitative data resources (inc. visual, audio) - Geo-spatial data - Non standard forms of data * Biometric data (physical samples) * Transactional data * Tracking and sensoring

3. Appropriately engaging with new data sources - “Richer data resources” - Identifying new forms of funding * Cross-council funding (especially in the area of health) * European and international sources

Understanding Society (UKHLS) Examples of maximising the analytical potential of new data on the horizon Understanding Society (UKHLS) [Innovation panel] Sample size Ethnic boost Data linking (e.g. qualifications) Biometric data 2012 Birth Cohort (Olympic) Advances in relation to earlier cohorts & US

Examples of maximising the analytical potential of new data on the horizon CENSUS 2011 Key statistics etc. Special products (inc. neighbourhood, small areas) Samples of Anonymised Records ONS-LS; Scottish Longitudinal Study; N.I. British Longitudinal Study?

4. Advances in information technology - “The ever changing climate” - Implications for data collection - Data access (maximising) - Data security (appropriately managing risks) - Ethical issues (e.g. data linking) 5. Capacity Building in data analysis - The next generation(s) of researchers - Capitalising upon and extending ESRC investments - Promoting awareness of resources