Building Statistics and Data Science Capacity for Development

Development is considered a sustainable action that positively impacts society, and it is achieved through scientific and research innovations, creating jobs for sustainable activities, and implementing effective policies. Statistics and data science serve as the bridge to understanding data, allowing people to make scientifically sound decisions and generate development benefits. Local challenges require local solutions, and local experts are best positioned to collect or produce data, analyze it, and craft and implement policy innovations. However, policy making and decision making often ignore the need for rigorous statistical analyses, and the simplistic perception is that only two actors are necessary for data-based decision making: data producers and data decision makers. In reality, at least four components are required: domain expertise, high-quality data, appropriate statistical analyses, and the power to make and implement a decision. A new model for building statistics and data science capacity is proposed, which helps statisticians and data scientists in developing countries build their own capacity to engage in data-driven development. This model involves creating statistics and data science collaboration laboratories, or "stat labs," that work in the intersections of data-driven development by collaborating with data producers and data decision makers to transform evidence into action. Stat labs are not just rooms full of computers but rather teams of statisticians and data scientists empowered to collaborate with domain experts to ask relevant questions, produce high-quality data, analyze and interpret data, and transform that evidence into action for development. The stat lab model is based on the International Statistical Institute's strategic priority of building statistical capacity in developing countries. Historically, this has focused on building the capacity of developing countries to produce state-sponsored data, but this only addresses one of the potential gaps in data-driven development. The stat lab model addresses all four gaps: local expertise to frame development questions, consistent production of high-quality data, technical ability in statistics and data science, and transforming evidence into action. Stat labs are engines for development, providing a mechanism for increasing collaboration between statisticians and researchers, business professionals, and development policy actors. They have three main objectives: to train statisticians to have a collaborative, evidence-to-action mindset, to teach researchers and others to use data and statistical analysis, and to provide a collaborative space for creating data-driven innovations and solutions. The stat lab model has been successfully implemented in the LISA 2020 Network, which supports stat labs around the world and has contributed to improved infrastructure, business development, education, agricultural growth, and human rights issues in developing countries.