With 258 million children out of school and 617 million children and adolescents in school but not achieving minimum proficiency levels in reading and math, we are rapidly running out of time to achieve Sustainable Development Goal (SDG) 4 by 2030: ensuring quality education and lifelong learning opportunities for all children.

The global education sector faces the dual challenge of improving learning at scale and measuring whether we’re on track. To address the scope of the learning crisis, we must identify innovations that have the potential to improve learning in a manner that is sustainable, equitable, and cost-effective, and then understand how to scale these innovations in a given context.

For several reasons, data is a crucial piece of the puzzle. First, data can provide insights on program effectiveness, allowing governments and donors to identify, finance, and scale the most effective interventions. However, data is not just about final project outcomes: Collecting information about implementation in real-time, and creating feedback loops to decisionmakers, allows for adaptation and improvement. In the global education sector, we tend to spend resources on trying to perfect our innovations before they’re carefully evaluated, which can mean that the most powerful insights come far too late. Instead, stakeholders need to use data and insights to iterate and pivot quickly—highlighting the importance of collecting the right kinds of data.

We at the Center for Universal Education (CUE) and STiR Education heavily rely on data to improve learning at scale and measure progress in our areas of research.

While we use data in distinct ways, our work showcases the need to think differently about monitoring, evaluation, and learning, and to integrate real-time data into decisionmaking.

Read the full article about how effective data can improve learning for all children by Reinier Terwindt, Emily Gustafsson-Wright, and Jenny Perlman Robinson at Brookings.