Just because we have good data does not mean it will be used for decisions
by Vanessa Meadu
“Better data for better decisions” – what does that mean? Since the first Livestock Data for Decisions (LD4D) meeting in 2017, our network has been focused on improving data and analysis for better decisions in the livestock sector, especially in low- and middle-income countries. Our challenge is twofold: improving livestock data and ensuring that this data can support better decisions. The second part of this task is very different from the first and involves becoming more demand-driven. But how do we know what livestock decision makers need?
In 2021 the LD4D Steering Committee asked for a study to help our network better understand livestock decision makers’ data needs. Today, we are pleased to release a new guide that shares insights from our study and offers a step-by-step guide to anyone who wants to undertake a similar study. The study was conducted by the Busara Center for Behavioral Economics, and the guide was prepared by the LD4D secretariat at SEBI-Livestock.
The guide outlines two distinct phases of work, each broken down into practical steps that anyone can follow.
Phase 1 – Who are the LD4D decision makers?
The study team, which was made up of behavioural scientists and livestock experts, developed a definition of decision maker as “an individual or an organisation that uses livestock-based data to make implementation-based decisions.”
With a general focus on low- and middle-income countries, we undertook a rigorous prioritisation exercise to narrow our scope. Criteria included livestock contributing substantially to a country's economy, representation from different regions, and countries that are priority investment areas for our funders. After several iterations, we agreed to focus on Ethiopia, Nigeria, South Africa and India.
Throughout the study, the team relied on advice from the LD4D Steering Committee. They helped us identify Key Informants who had inside knowledge about the main players in the livestock sector. Through interviews, the Key Informants helped us narrow down thematic areas within the country’s livestock sector, data sources, and areas for investment.
We found that decision makers naturally fell into two main categories. Doers: decision makers operating at grassroots level who are directly impacted by livestock policies; and Influencers whose actions influence the decisions of the Doers.
In particular, the Key Informants highlighted the importance of public sector decisions and decision makers, and the challenges in making evidence-based decisions in the public sector.
The final output of this phase was a living map of livestock decision maker groupings and the kinds of decisions being made within each level which could require data and evidence (Figure 1). We believe this map is a useful starting point for the LD4D network to focus its efforts on decision makers.
Phase 2 – What are livestock decision makers’ data needs?
The study identified public sector policy and strategy as an area where evidence-based decision making remains a challenge, and we chose to use Livestock Master Plans as an entry point for understanding decision making.
We assembled country focus groups made up of professionals who could provide insights into the use (or non-use) of livestock-based data to make implementation-based decisions. Our focus groups included policy influencers and policy implementers. Policy influencers are employed by the Ministry of Agriculture and Livestock and help develop national livestock policies and strategies and investment plans. Policy implementers typically work in government departments or in the private sector, providing services to farmers or monitoring policy implementation. Through our interactive discussions, we encouraged participants to reflect on the different sources of data they use for a range of decisions. We asked them about the role of data in decision making, data sources and gaps, frequency of data use, and data flows.
These discussions helped us form insights about which data sources are valuable for different kinds of decisions and revealed different habits in decision making. For example, decisions made under uncertainty tend to draw upon different (and more) data sources than routine decisions. We also learned that different factors influence the way people use data. Policy designers choose data based on convenience, data quality and accessibility, while policy implementers are more influenced by budgets, institutional hierarchies, and the need to present a consistent narrative.
This study sheds some light on who the key decision makers are in our study area and reminds us that decision makers rely upon different sources of data depending on the type of decision(s) they need to make. Decisions can be informed by multiple data and information sources – including intuition and experience – which raises questions about which sources of data LD4D can influence and how.
The LD4D case study only covered a small part of the livestock sector and could be expanded to cover other areas. For example, the LD4D Secretariat are planning to use this method to better understand decision making around climate finance for the livestock sector, as the first step towards forming a working group on this topic. There are many things we could do differently, which we outline in the guide. For example, we would be more proactive in addressing issues around social inclusion and we would ensure participation from more community-level actors.
A resource for the LD4D network
If you are interested in better understanding the needs of livestock decision makers, we encourage you to use the guide and test this approach. We would be interested in refining the approach based on your feedback, and potentially developing some case studies showing how the method can be applied. We see this as a shared and living resource by and for the LD4D network and we look forward to continuing the conversation!
Vanessa Meadu is the Communication and Knowledge Exchange Specialist for SEBI-Livestock, which convenes the Livestock Data for Decisions (LD4D) Network.