People increasingly recognize that having high-quality data as inputs to guide governments and policy makers is important to ensure that program implementation can yield highly beneficial outputs, outcomes, and impact for societies. Data-driven policy-making and monitoring also helps invigorate national and international developmental efforts. However, the budget constraints experienced by many national statistical offices (NSOs) in recent years have made it even more challenging to meet the increasing demand to produce high-quality data. Consequently, NSOs need to be more resourceful in producing such outputs, including maximizing the use of all available data.1 The importance of high-quality data was further magnified with the global adoption of the Millennium Development Agenda in 2000, and the adoption in 2015 of the 2030 Agenda for Sustainable Development, which has four times as many indicators as its predecessor. Progress on the Millennium Development Goals (MDGs) and the SDGs have been monitored using corresponding indicators for each goal and target.

It is imperative that the data used in these indicators are of good quality as they can help shape essential policies.

Countries gradually moved from setting their development priorities independently to integrating their national development strategies with the global development agenda. National statistical systems were prompted to strengthen their capacity and methodologies and ensure that they adhere to international statistical standards for compiling development data. This further cemented the important role of data in socioeconomic development and underlined development challenges such as lack of reliable and internationally comparable data that can undermine governments’ ability to set goals, optimize investments decisions, and measure progress.