There is a growing recognition of the role of data in agriculture and food systems including in the context of smallholder farmers from low- and middle-income countries. By transitioning to data driven agriculture, smallholder farmers can reduce the cost of cultivation, increase productivity, increase sales realisation, and reduce vulnerabilities. For service providers, shifting to data driven service delivery can enable better customer segmentation, improved product design, reduced cost of service delivery, and improved adoption of services. For stakeholders such as development funders, data can enable evidence based funding and also ease monitoring, evaluation, and learning. Consequently, today, there are multiple efforts to integrate data into agriculture and food systems. 

However, in practice, when it comes to data in agriculture, most of the efforts are in silos with each organization working on their own data collection, management, and analysis journey. This is resulting in duplication of efforts and resources, a fragmented data ecosystem, and farmer fatigue, all of which are detrimental to the data-driven agriculture movement. As a response, there has been a niche movement towards promoting collaboration in the data ecosystem through various models: co–investment in data collection, data sharing, development of digital public infrastructure, and others. These collaborations are new but hold several promises. For instance, it can be hypothesised that data sharing can reduce costs of service delivery and also provide access to larger pools of data for training algorithms and predictive models. Similarly, open data can enable better product design. Collaboration can also amplify evidence-based decision making and reduce costs of MEL of development programs. 

With this background, in this session we plan to understand what Collaborative Data Synergies for Food System Transformation means in the context of smallholder farmers from low- and middle-income countries, and how such collaboration can be seeded, supported, and scaled.