Transforming agri-food data into actionable insights

Background To initiate agency and action in agri-food supply chain transition, data must be translated into information and then actionable insights. This complex data-insight translation process requires an iterative, self-learning approach from problem definition through to problem resolution. Scope and approach The aim of the research is to narratively explore this process of transitioning data into actionable insights in the context of the agri-food supply chain, develop a conceptual framework to visualise the processes required and critique the socio-technical and governance challenges that inform actionability. Key findings and conclusion Opacity and bias, arising from weak algorithmic design, inappropriate data acquisition, inconsistent data quality, poor system operationalisation, and human cognitive factors, can undermine technical performance as well as user trust and agency. Addressing these challenges is therefore essential. The case study examples demonstrate that the transition from problem definition to problem resolution is inherently complex, iterative, and socially situated, reinforcing the need to treat actionability as a dynamic process rather than a linear outcome. This research has developed a conceptual framework to explore how to translate agri-food data into actionable insights and effective operationalisation of technology. The eight elements of the framework are: problem definition, data acquisition, data analysis, information legitimisation, creation of knowledge through relativisation, participation (agent engagement), operationalisation (agent response) and effective resolution. This research is of interest to those involved in academia, industry and policy seeking to drive greater levels of engagement and actioned response to the sharing of agri-food-related data and technology adoption within agri-food supply chains.