Data Science for Global Development
By: Nimu Sidhu
Tags: Data Science, Systems Thinking, Sustainable Development, Analytics, MEL

Data science is the transformation of data into powerful information. If you had powerful information, what would you want to achieve? Or rather, what change do you wish to see in the world?

Image from Influencive.com

I entered the field of data science because it has tremendous potential to advance global development.

A former chemical engineer and biophysicist, I had always been interested in the exploration, generation, and application of knowledge and tools to solve complex problems. However, as a Peace Corps Volunteer and later as an international development consultant, I was challenged to locate and utilize the most up-to-date knowledge and tools to more effectively and efficiently create impact. I consistently saw everyday change-makers, development practitioners, and entire institutions reinvent the wheel or make poor decisions impacting hundreds of people or even an entire region — simply because data was not readily accessible for either direct application or context-specific translation.

I began to wonder how the social and public sectors might collect, organize, and analyze important information to make data-driven decisions benefitting those at the bottom of the pyramid.

Over the past several months, I was opportune to see the status of data utilization in global development up close. From organizing the collection of data on sustainable agriculture programs to designing open government data challenges, I better understood obstacles and opportunities alike. Below, I outline a few of the keys areas in which I see data science playing a large role toward development goals.

Stakeholder Inclusion and the Program Cycle

Data Collaboratives for Local Impact (DCLI), a federal program created by PEPFAR and MCC, emphasizes stakeholder empowerment through data:

“A community empowered to capitalize on information can more effectively define its problems and work toward innovative solutions… fundamental to engaged, sustainable, and equitable economic development.”

DCLI organizes an annual conference called DataRev to share and promote the use of data in development programs around the world. At DataRev, you would find that both data use and stakeholder inclusion are common across the development space. While this ideal approach is a stretch for many organizations, there is certainly an intuitive space to start: program evaluation.

Many development programs include a component called monitoring, evaluation, research, and learning (MERL). MERL serves as feedback loop to improve a current program by collecting information on program outcomes against targets. It also informs new iterations of similar programs (often within the same organization). Naturally, MERL is the development sector’s data science foothold.

Provided a decade of technological improvements conducive to data collection, MERL Tech is a rapidly growing field. Efforts in this space could be applied throughout a project or program cycle, such as in the design phase or during implementation and adaptation. For example, data can be collected directly from stakeholders to understand a dynamic system, its primary actors, and actor/actor and actor/resource linkages. Once the system is understood, a root problem (or several) can be detangled and corresponding program objectives defined. MERL Tech could be used to conduct and validate systems research in the same way that feedback would eventually be collected.

Translating Program Learnings Across Different Contexts

One of my motivations to enter the field of international development was to aggregate and translate findings across borders. Analytics could inform concerted efforts on the part of all UN members, for example, to design improved programs, policies, and long-term strategies for development. Global Pulse, an initiative of the United Nations Secretary-General, is a network of innovation labs applying big data to development. Provided the network is able to successfully coordinate information, connect it to needs, and offer suggestions for translation, it has tremendous potential to affect positive change.

Digital Technology Geared Toward Development Applications

A somewhat obvious application for data science is in “digital development”: the design and application of digital technology to address development challenges. From agri-tech to financial inclusion, digital technologies can now reach millions if not billions more people than it could ten years ago. The nine “living” principles of digital development unify guidance originally issued by international NGOs and multilateral agencies such as the Bill and Melinda Gates Foundation and UN Development Program. As digital technologies are often developed as a context-specific solution, you’ll notice that the principles touch on the stakeholder inclusion point discussed on the first section of this post. Notable actors in this space include:

Increasing Data Access to Enact All of the Above

In support of global progress, organizations such as the Open Data Institute (ODI) are especially focused on building an open, trustworthy data ecosystem. Open data is critical for transparent and accountable governance, and it is unethical to monopolize data affecting the ability of individuals to survive. ODI in particular has my attention because it regularly shares updates about what different governments and organizations are up doing, living up to its ideals.

The topic of open data is somewhat sticky, playing a very different role across the public, nonprofit, and private sectors, and I plan to delve into this issue in another post sometime. Meanwhile, I’ll draw your attention to an alternative:

Private company Premise has an interesting data collection model wherein those in developing contexts collect and report data using mobile phones. Individuals are compensated for tasks such as taking pictures, logging their locations, and completing simple surveys in a multitude of languages. This model of knowledge generation in and of itself creates socioeconomic impact in a sustainable way!

In closing, key applications of data science in global development include:

Understanding the problems we seek to solve

Designing more relevant, desirable solutions

Improving adaptation during solution implementation

Unveiling learnings from different programs targeting related challenges

Identifying resources and collaboration needs for new programs

Performing meta-analyses for policy design

Creating long-term strategies that account for country and regional variations

I’m excited to learn what else is happening in global development and social impact. Please do share your favorite organizations and resources!