The rapid response mounted by local government from the outset of the COVID-19 pandemic has put the spotlight on data and local government’s related capabilities and gaps.
Gathering information, collecting evidence, and generating intelligence have been central to how local authorities responded to the COVID-19 crisis. In turn, this has put the spotlight on data, prompting the following questions: how have local authorities identified and handled arising data needs?; how they have used and analysed data?; what related challenges have they encountered?; what innovation opportunities have they pursued?
In this context, the Urban Big Data Centre carried out an in-depth analysis of Scottish local government’s data engagement between autumn 2020 and spring 2021.
This project was guided by the following four objectives:
- To analyse existing and emerging data uses, capabilities and needs of Scottish local authorities;
- To investigate whether local government’s responses to the pandemic have reshaped data governance, strengthened existing collaborations, or generated new data networks;
- To identify emerging opportunities for public data collaborations and citizen engagement;
- To examine whether data applications and practices informed decision-making and improved outcomes concerning the management of COVID-19.
The research combined quantitative methods (survey) with qualitative methods (focus groups; interviews) and was developed in partnership with the Digital Office for Scottish Local Government (‘Digital Office’).
|Executive summary||Download (PDF 0.2MB)|
|Recommendations for policy/practice||Download (PDF 0.2MB)|
|Full report||Download (PDF 2.88MB)|
The research’s fifteen recommendations for policy and practice (PDF 0.2MB) have informed Scottish local government’s strategic planning related to data access, sharing, and reuse within the public sector and across sectors. Each of the recommendations have been discussed by the Data Taskforce of the Digital Office, and in collaboration with the researchers during a series of meetings. Additionally, the team presented the project findings and recommendations at the Data & Intelligence Network Transformation Board led by the Scottish Government (31, August 2021).
As a result of these activities, the Data Taskforce devised an action plan looking at data-related practices and policy for 2022. This includes the ongoing development and strengthening of specific working groups and themes (e.g. around open data, the use of IoT data, and data related training), the identification of relevant private sector data for local government use and related procurement strategies, as well as directed efforts to coordinate data practices and policy at national level (e.g. via the Data & Intelligence Network, Scottish Government’s Data and Digital Identity, and COSLA). The recommendations - on data literacy and harnessing the potential of novel data - have also been fed in the ongoing creation by the Digital Office of a data playbook for Scottish local authorities.
Following on from this, the project team has been invited to a panel session discussion organised by Socitm (March 2022), a leading membership organisation of more than 2,500 professionals helping to shape and deliver place-based public services. The panel will discuss the research recommendations and reflect on the learning from the development and ongoing implementation of the action plan. The research team will also deliver a briefing to the working group for the development of Scotland’s first Health and Care Data Strategy (due to be delivered in March 2022). The collaboration with the Digital Office is ongoing and further work will take place in the coming months to widen and assess the impacts of the research.
Additionally, the project team has been approached to give a series of talks at key national and international events, shaping UK and European debates on data access, sharing and governance, smart data, and data ethics. This included:
- Taking part in a roundtable discussion on 'Data Driven Economic Recovery Beyond COVID-19' organised by Cities Today (3, June 2021) and attended by city leaders (incl. Dublin, Edinburgh, Gdansk, Gothenburg, Las Palmas, Lisbon, London, New York City, Milan, Prague, Reykjavik, Rome, Rotterdam, Tampere). The discussion has led to a media piece drawing on lessons learnt by cities during the pandemic and how to move forward.
- Presenting the research findings and recommendations at the Action Cluster Workshop on ‘Integrated Infrastructures & Processes’ (10, June 2021) as part of the European Commission’s Smart Cities Marketplace Forum and the EU Green Week. The research presentation was delivered alongside a presentation from George Crooks (CEO, Digital Health Scotland) as a Scottish case study focusing on ‘Covid-19 and Data from pan-Local government Research and Digital Health in Scotland’.
- Participating in a panel discussion on how to secure innovation through better data sharing organised by Centre for Data Ethics & Innovation as part of Leeds Digital Festival (28, September 2021). Alongside the researcher, the panel included an Information Governance and DPO expert working at the Greater Manchester Combined Authority and was chaired by the director of the London Office of Technology and Innovation.
Dr Justine Gangneux is a Research Associate at the Urban Big Data Centre, University of Glasgow. Her research interests sit at the intersection of digital sociology, critical data studies, and urban studies. Her work has been published in international journals including Big Data & Society; Information, Communication & Society, the Journal of Urban Technology; the Journal of Youth Studies; and Sociological Review.
Professor Simon Joss is Professor of Urban Futures at the University of Glasgow, and associate director of the Urban Big Data Centre where he leads the workstream on urban governance. He has a background in policy analysis with special focus on urban technologies. He is a member of the British Standards Institution’s committee on smart and sustainable cities and communities (BSI SDS/2).
The research was supported by the Economic & Social Research Council (grant ES/S007105/1).