THEORY OF CHANGE

DataShift’s Theory of Change shows how we aim to build the capacity of citizens and their organisations to create and use citizen-generated data to identify their sustainable development priorities, monitor progress and hold governments to account (PDF version here).

WE IMPLEMENT THESE ACTIVITIES

Learning Zone

Thematic Forums

Datashift Dashboard

Direct Support

Datashift Fund

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

INCREASED COVERAGE

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

MORE CREDIBILITY

citizen‑generated data is widely considered legitimate and reliable

DATA-DRIVEN CAMPAIGNING

citizen‑generated data is used in civil society campaigning

WHICH SUPPORT THIS OBJECTIVE

Civil society organisations can effectively produce and use citizen-generated data to monitor sustainable development progress, demand accountability and campaign for transformative change.

PEOPLE-POWERED ACCOUNTABILITY DRIVES PROGRESS ON SUSTAINABLE DEVELOPMENT

ASSUMPTIONS & RISKS

The DataShift has been designed based on a set of assumptions about the relationship between data, citizens, accountability and transformative change. We also know that a number of risks could impede our capacity to contribute to the changes we want to see.

THE VALUE OF CITIZEN-GENERATED DATA

ASSUMPTIONS: Citizen-generated data is a valuable complement to official sources of data for monitoring development progress because it is timely, nuanced, fills data gaps and empowers citizens and their organisations to hold decision-makers to account because of their direct involvement in generating and curating the 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

ASSUMPTIONS: There is a demand from civil society organisations to improve the way they generate and use citizen-generated data in their programming, advocacy and accountability efforts. Governments and key decision makers can be convinced of the value of citizen-generated data, are open to using it to inform their decision making and are looking to invest in it.
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

ASSUMPTIONS: There is an openness to brokering relationships and collaboration between organisations with a strong track record in citizen-generated data with organisations that want to learn how to more effectively use citizen-generated data in their advocacy and programming.
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.