THEORY OF CHANGE
TO SUPPORT THESE CHANGES
Increased CAPACITY of CSOs to produce and use citizen-generated data
More opportunities for civil society organisations to CONVENE, COLLABORATE, EXPERIMENT AND INNOVATE with citizen‑generated data
Improvements in the HARMONISATION AND COORDINATION between citizen‑generated data
Enhanced connection between LOCALLY-DRIVEN, citizen-generated data initiatives and GLOBAL POLICY processes and accountability frameworks
WHICH RESULT IN THESE OUTCOMES
more citizen‑generated data initiatives across the world, particularly in the Global South
ENHANCED COMPLEMENTARITY AND COMPARABILITY
improved complementarity, comparability and harmonisation of citizen‑generated data
citizen‑generated data is widely considered legitimate and reliable
citizen‑generated data is used in civil society campaigning
WHICH SUPPORT THIS OBJECTIVE
PEOPLE-POWERED ACCOUNTABILITY DRIVES PROGRESS ON SUSTAINABLE DEVELOPMENT
ASSUMPTIONS & RISKS
THE VALUE OF CITIZEN-GENERATED DATA
RISKS: Inadequate coverage of citizen-generated data initiatives and/or highly contextualised nature of citizen-generated data initiatives undermines comparability, harmonisation and aggregation efforts and ultimately, its value in global SDG monitoring.
THE DEMAND FOR CITIZEN-GENERATED DATA
RISKS: Civil society organisations do not have the capacity, infrastructure (including ICT) or civic space to meaningfully benefit from the DataShift. Citizen-generated data is sidelined, or at worst, ignored in the SDG monitoring and resourcing frameworks, with big data and official statistics being prioritized. The bottom‑up, demand-driven approach of the DataShift cannot move quickly enough to keep up with global policy and decision making processes.
THE DEMAND FOR COLLABORATION AND LEARNING
RISKS: The resources it takes to effectively connect these two types of organisations detracts from other components of the DataShift and/or the DataShift is crowded-out by other initiatives focussed on data use, technology and data literacy at the global and country levels that are less bottom-up in approach and can therefore move more quickly.