Social movements are adapting to datafication in diverse and complex ways. This paper refines existing conceptualisations of datafied social movements by analysing how two organisations within Ireland's housing and just-city movements navigate datafication. Through a longitudinal comparative study of the Dublin Democratic Planning Alliance, an expert-driven advocacy network, and the Community Action Tenants Union, a grassroots, mass-membership union, we demonstrate that movement actors engage with datafication along a spectrum ranging from strategic integration to reluctant encounters. Drawing on interviews, participant observation, workshops and document analysis, we examine how organisational structure, strategic orientation, and political values shape data practices across three dimensions: (a) data access, quality and infrastructure, (b) strategic mobilisation and epistemological ambiguities, and (c) organisational capacity. Our findings reveal significant gaps requiring greater nuance. First, both organisations engage with data in marginal, partial, and sporadic ways, subordinating it to their primary objectives rather than making it central. Data work is limited to small subsets of participants, ranging from reluctant use to formalised but peripheral practices. Second, political orientation fundamentally shapes data deployment. The Dublin Democratic Planning Alliance relies on specialised professional expertise to produce counter-data from public datasets for institutionally oriented advocacy, while the Community Action Tenants Union collectively negotiates data practices as resistance tools for grassroots mobilisation. Both leverage state-produced data's authority while critiquing its inadequacies. We identify a critical ‘data work versus groundwork’ tension, wherein technical practices risk creating expertise hierarchies contradicting movements' democratic values. This analysis advances understanding of how datafied social movements comprise heterogeneous actors with diverse, locally embedded practices.
Davret et al. (Mon,) studied this question.