Using Data for Development

Rapid advancements in technology and open data have made more information available now than ever before. For policymakers in developing countries, information harnessed from these data can provide actionable insights for improving public health, education, governance, youth employment, food security, and more.

Data can help explain what has happened, what is happening, and what will happen, and can give policy makers sound information with which to make decisions and allocate resources. For example, in early grade education, data collected through classroom assessments can help education leaders better understand what a child is learning or not learning, and why. These insights are used to improve curricula, teaching techniques, and classroom materials based on evidence of what is likely to be more effective.

The revolution in data for development also makes it easier to track an intervention's progress and measure its effectiveness. This is important at both the micro- and macro-levels—from fighting the outbreak of a disease in a small village to measuring the progress of the full slate of Sustainable Development Goals.

While the potential of the data revolution is vast, it will take hard work and the right approach for international development practitioners to harness the value in the data and put it to work for policymakers and people around the world.

a. Helping Clients Harness the Data Revolution through Strategic Advising and Technical Tools

We are able to help clients harness the data revolution. We help achieve better program outcomes through improved needs assessment, monitoring, evaluation, program implementation, and policy analysis and consulting.

We provide strategic advising and technical tools, including:

• Applications development, rapid prototyping, and pilot projects
• Strategic advice on collecting and analyzing “little data” and “big data”
• Understanding how to use data for better policy making and decision making
• Predictive analytics to better understand sectoral challenges
• Integration of disparate data sets to better understand unit performance
• Data visualization
• Improving legacy systems.