Annie Kilroy
Connect with Annie
Annie serves as DG’s technical advisor for research design, indicator development, methodological guidance, and strategies for increasing the use of data for decision-making. She works to synthesize learnings across DG’s portfolio into comprehensive processes, methodologies, and best practices on using data and evidence for decision-making. Annie brings six years of experience of research methods in social science and public policy, focusing on issues of public policy, governance, and monitoring and evaluation.
She has a Masters of Public Affairs from The University of Texas at Austin, and Bachelors degrees in both Psychology and International Relations from North Carolina State University.
Recent Posts
Farmer-Centric Data Governance Models: Protecting Farmers and Food Systems Today and Tomorrow
As farmers become more reliant on AgTech, they may find that the AgTech providers controlling these technologies (i.e., companies, nonprofits, and governments) are more integrated than ever before, resulting in a few organizations having unprecedented access to and control of farmers’ data. This dynamic results in positive and negative outcomes for farmers. Therefore, farmers face the paradox of using AgTech and adding value to their work, communities, and food systems while giving large amounts of data to AgTech companies that have, at best, limited plans for protecting farmers’ data. In this blog, we identified recommendations and next steps for AgTech providers on how to ensure that their technology benefits smallholder farmers.
Measuring digital transformation? Get real.
Senior Associate Annie Kilroy explores the limitations in how digital transformation has been measured and outlines recommendations for how to better assess the value and impact of digital transformation.
Designing Data Visualizations: Merging Best Practices and Design Thinking
DG has been co-designing data visualizations with partners and stakeholders for over a decade. Thinking about the ways people process information is crucial to developing easy-to-understand data visualizations. In this post, we examine best practices for incorporating user-centered design into our data visualization outputs.