Welcome to the Data in Action Toolkit

Data in Action

Background

Today, it is common to use big data sources like mobile, satellite, and text for development insights in World Bank research and development operations. And there is growing use of open data science tools and practices, making data innovation in the development community more collaborative, reusable, and standards-based. Along the way, we've learned many lessons.

The Data in Action Toolkit is a collection of materials to support the process of designing and delivering data products in the context of international development. The toolkit is not comprised of official World Bank project templates, but provides informal materials that have been used in dozens of multi-stakeholder workshops with leading experts from a diverse range of organizations in the development data community, including: SecondMuse, UN Global Pulse, Data-Pop Alliance, Giant, Amazon Web Services, LinkedIn, Facebook, MIT, University of Chicago, Vital Wave, USAID, Digital Impact Alliance, the National Science Foundation, and the Global Partnership for Sustainable Development Data (GPSDD).

The Data in Action Framework

Data is not the same as knowledge, and many capacities are required to generate actionable insights. Often, this process is cyclical and interactive. For example, an analysis may shed light on new data needed, or the very act of consolidating data may reveal insights which inform an analysis or the need to access data in a different way. In any case, all phases of the cycle inform each other through a rich process of learning.

Data in Action Framework Source: Big Data in Action for Development. Washington, DC. World Bank. 2014.

The Journey: What to Expect

Practitioners that pursue a large data development program typically follow three phases.

1. Discovery 2. Incubation 3. Scale
  • Surfance and prioritize problem(s) to solve
  • Define a Theory of Change
  • Develop prototype(s) of possible solutions
  • Conduct pilots, field experiments, and user research to hone hypotheses
  • Iterate on pilots to refine solutions
  • Validate solutions with end users in the field
  • Productize solutions with well documented code, tutorials and marketing
  • Share learnings
  • Identify talent, technical, and financial resources needed to extend solution to new use cases, markets, etc. and sustainable funding
  • Implement project governance (program reviews, ethics/data privacy, etc.)
  • Operationalize with support and maintenance plans

In the rest of this toolkit, we delve deeper into this framework and many others. Read on to learn more.