A method for understanding livestock decision makers' data needs

Governments and other groups that work on livestock development in low- and middle-income countries need good data. They need data to make key decisions such as where to set up a project, which diseases to tackle, and which interventions to implement. Science-based organisations aspire to generate data that can be used for these kinds of decisions. But producing data is usually not enough to influence change. To close the gap between data and decisions, we must begin by understanding the needs and behaviours of decision makers.

Livestock Data for Decisions (LD4D) is a network of people working with data in livestock development. Its mission is to drive ‘better data for better decisions’. To achieve this, we needed a better understanding of who the livestock decision makers are within low- and middle-income countries, and what their data needs are. This can ultimately inform which challenges the network can address, and ensure we produce useful and impactful data and evidence.

To this end, we conducted a study through the Busara Center for Behavioral Economics. The first goal was to understand the overall landscape of livestock decision makers. The second goal was to explore the way that data and evidence are used in decision making and to assess decision maker data and evidence needs. This study was unique to the LD4D network, which targets a broad range of problems and potential decision makers.

This guide distils the key lessons learned from this process and aims to help individuals and organisations who are planning to undertake a similar study. Phase 1 is for those who are aiming to identify decision makers, while Phase 2 helps you investigate the behaviours and data needs of those decision makers. For each Phase, we present a series of steps, describing the methodological process followed by the outcome. Each step requires detailed thought and knowledge, with many sources of information that cannot all be covered in this brief summary. Finally, while our study may not have addressed issues around social inclusion from the outset, it has been considered retrospectively, and we attempt to highlight it in this summary guide.