4. Common issues

The 15 breakout sessions covered a diverse range of different topics, but some clear common themes emerged. These include the following.

1. The need to enhance agility and flexibility in response to a rapidly changing world.
2. Take advantage of technological advances, such as AI. AI could be applied in various roles, such as improving analytical pipelines, automation (of data capture and elsewhere), coding and coding checking, and so on.
3. Make more use of administrative data, explore how to continue to gain insights from surveys, and in general actively look for alternative sources of data in order to meet user needs.
4. Focus attention on the quality and consistency of administrative data.
5. The merits of automating data capture as much as possible, via automatic surveys and direct access to business and other administrative data. This will require the involvement of statisticians in the design of data architectures and training for frontline people involved with data capture.
6. Accelerate and improve the capacity for the linkage of data, and improve coherence and compatibility. Ensure consistent data standards and definitions across departments, regions, and nations for effective comparison and integration.
7. Improve the understanding of what data are needed at a local level, and improve its quality in granularity and timeliness.
8. Work to improve data capture for under-represented groups and build a consensus on how to represent ethnicity in data.
9. Model changing work-patterns and develop more holistic measures of work, including volunteering, carers, and others.
10. Ensure a respectful, constructive and engaged feedback loop between producers and users to address transparency and need.
11. Improve access to micro data for research purposes.
12. Improve cross-governmental data sharing.
13. Better integration across the GSS. For example, some departmental statistics could benefit from ONS input and vice versa. This would also materially aid standardisation.
14. Develop effective performance metrics so that policies can be monitored, including for public sector and user engagement.
15. Enhance transparency and take a more active role involving users and producers to address public concerns about data sharing, trustworthiness, and ethical use and to make the case for the value of data. In part, demonstrating trustworthiness means producing statistics that are more relevant to individuals and developing measures that connect to public perceptions (for example, on crime and economics).
16. Improve user and public engagement and communication. Among other things this means broadening the groups of users that are routinely engaged, effectively communicating research findings to decision-makers and the public to inform decisions and demonstrating to the public the value driven by their data.