Skill sets and mind sets – using data to embed wellbeing in Northern Ireland
November 15, 2018
by Lauren Pennycook, Senior Policy and Development Officer
Data is powerful. Robust, reliable, and at the right level, data can tell us which groups in society need our help. Used to inform policymaking, data can help to break down silos, deliver convenient and co-ordinated public services, and prevent societal problems before they start. But do all levels of government have access to the data that they need in order to prioritise resources and improve wellbeing? And if not, how do we address the difficulties of incomplete, inaccurate or ineffectual data?
It has long been recognised that life is local, and that the availability of good quality data at local government level is critical for delivering services at the heart of our wellbeing, such as health and social care, education, and housing. However, as the Carnegie UK Trust has discovered through its Embedding Wellbeing in Northern Ireland project, the need for data at a local level is not always matched by its availability or accessibility.
1 April 2015 marked a once in a generation transformation of local government in Northern Ireland. With the reduction in the number of local authorities from 26 to 11 came new powers and responsibilities – most notably, in Community Planning, in which local Community Planning Partnerships had a duty to develop and monitor the progress towards Community Plans which would improve the wellbeing of citizens within the new local government areas.
In the Expressions of Interest received from the 11 Community Planning Partnerships to take part in the Trust’s Embedding Wellbeing in Northern Ireland programme, the use of data was a common challenge faced by all Partnerships in implementing the Community Plans. These challenges ranged from the extremes of a lack of data, with some key data sets only being available at an all-Northern Ireland level, to too much data – with large volumes of data on some thematic areas, making prioritisation in policymaking difficult. They centred around skills sets – a lack of analytical capacity at local government level; a lack of confidence in the robustness of internal evaluations; or apparent confusion between population indicators and performance management data. And they centred around mind sets – the lack of awareness of the importance of good quality data for the accuracy of decision-making by those with the power to approve resources for data analysis. They included visualisation – how to communicate the data already held in a way that tells the story of process and achievements to date in an inspiring and compelling way to the general public, largely unfamiliar with aggregation, algorithms, and anonymization.
Given the long-term nature of the Community Plans, with some setting the strategic direction for the local areas as far into the future as 2032, and aspirations for the Plans help shift public services to a preventative approach, good quality, robust data is essential. This is why the Trust is seeking to clarify the challenges experienced by the Community Planning Partnerships in collecting, analysing and incorporating data, and accompanying ‘what works’ evidence, into policy. Last week, we brought the three project participants together with representatives from the Department for Communities; the Northern Ireland Statistics Research Agency; and Nesta, to tackle what is possible, what is available, and what is good practice regarding the use of data at the local level.
As part of the project’s commitment to policy learning across the Community Planning network in Northern Ireland, we are now exploring how can support awareness raising of the importance of data and evidence to those with the power over the purse strings; its use in policymaking; and the presentation of data for communicating progress. We’re doing so to ensure not only what we measure is, in fact, what we treasure, but also to develop policy to deliver effective services, and communicate effectively about what is core to our wellbeing.