Developing data capability with non-profit organisations using participatory methods
In a small food charity warehouse on the outskirts of a large Australian city, volunteer workers try to tally recipients of food packages. Some of the volunteers don't like using the computer system, or don't have the skills, and every now and then the paper forms run out. Reflecting on the issue and her drive to improve data systems and practices, the manager told us “If you really need to have your data to communicate impact and it's buried in five stacks of paper with a scratched-on-tally, it's not great” (Workshop participant, 5 May 2021).
The data divide is growing. Uneven access to data is paired with deep disparities in knowledge and expertise creating inequality not only between corporate data-haves and citizen have-nots, but also across industry and organisational 'data settings'. This scenario affects who produces data in the digital society, who has control over or access to it and who has the skills to use it to derive some sort of benefit. It also affects the way data is valued. The benefit of data is usually weighed as commercial advantage. And yet it is civil society, charities, social services and community organisations that are most likely to find data’s social value.
Our wager is that supporting community sector and non-profit organisations to develop their data capability, with attention to data's social value, can help address the data divide and build data equity. As big data assets are increasingly used not only to inform decisions but also to automate them – through emerging AI and automated decision-making systems – this goal is becoming urgent.
A collaborative data capability approach
How then can data equity be improved within the non-profit sector? What can be done to build data capability both within community sector organisations and between them, increasing collaboration, community buy-in and social benefit?
In our Big Data & Society journal article ‘Developing data capability with non-profit organisations using participatory methods’ we explore the concept of data capability building on approaches to data literacy and expertise, and design new participatory data methods. We hope these methods can help other researchers engage and work with non-profit organisations to build data capability to improve data equity and as a movement toward more effective collective data action.
Data capability has both material and formal components – think hardware, storage capacity, software tools, as well as cultural, interpersonal and communicative dimensions – as in the ways we talk about or share ideas regarding the value of data and its uses, and the skills we build together in teams and sometimes in movements.
The participatory data capability methodology presented in our paper draws lessons from a range of data literacy and participatory design projects. Participatory design and co-design approaches champion inclusivity and takes stock of multiple perspectives. Above all these practices foreground collaboration as well as self-enablement.
Where we landed and next steps
Detailing the methods and results of two collaborative data projects, we describe successful processes in building data capability in non-profit contexts. This involved:
creating spaces and techniques for iterative data elicitation to explore data pain points or challenges and identify relevant datasets,
data discovery to gain responses to data visualisation using data walks, and
helping the groups work towards context-aware data storytelling and building that into organisational mission, strategy and outcomes.
Building on these methods, we also saw the need to: contextualise non-profit data practices, through engagement with a broader set of organisations and situations, and find new ways of fostering communities of practice to sustain data capability building across the sector.
Working with community sector workers over several years has shown us the benefit of participatory methods in building data capability. It also showed us the deep willingness across the sector for stepping up to the challenge of creating a culture of responsible data production and use that works outside of the commercial asset paradigm that has been ascribed to it and limited its social impact and outcomes.
The more we can articulate the pathways toward sector-wide collaborative capability the greater potential there is for creating the conditions for data for social good outcomes.