The development community is awakening to the opportunity to improve decision-making by making better use of the growing amounts of data that can be obtained, collected and analyzed.
Progress depends on it, believe some observers.
“With regard to development, data is the lifeblood,” said Amir Bagherpour, chief political scientist and founding partner of GiStrat, a U.S. company that uses advanced decision analytics to help policymakers devise solutions to problems ranging from conflict in the Middle East to garbage collection in Beirut, Lebanon. “Now with the [Sustainable Development Goals] there is a massive effort to come up with better indicators to understand development and where it is going.”
According to Nicolas de Cordes, vice president of marketing anticipation at French telecom giant Orange, “any country that doesn’t invest in big data will not attain the sustainable development goals.”
Whether you call it “big data” or just plain data, there are already some good examples out there.
Empirical work to solve ‘vexing problems’
Advances over the last three decades in the efforts to reduce global hunger and child mortality “have been driven by empirical work,” said Bagherpour, especially in academia. Information with the “level of granularity” of a World Bank report has helped engender “a new capability to empirically identify what drives these vexing problems” and come up with more effective solutions.
Drilling down to more specific initiatives, the World Bank recently renewed a partnership with the European Space Agency to continue to share data pulled from ESA’s satellites and have it analyzed for the bank. One emblematic project involved tracking the expansion over decades of informal settlements in Manila, Philippines. Another crunched data about landslides in Nepal, whether sparked by earthquakes or heavy rain. The data and their analysis helped bank experts come up with recommendations for national and local policymakers — for example to move populations from vulnerable areas or build protective infrastructure.
The African Risk Capacity Insurance Company Limited, the most prominent of three “risk pooling” initiatives around the globe, hired a team of consultants to analyze decades of satellite flood data in Africa to create a model for flood insurance pricing. A specialized agency of the African Union, the ARC provides insurance to countries against the risk of natural disasters. Developed in-house by a team of consultants working with the ARC, the outputs of the crunched data will be used to try to convince reinsurers such as Munich Re to buy into the flood insurance scheme.
“It comes from an astonishing, large set of information,” said Simon Young, outgoing ARC chief executive officer. “It is pretty technical, but [as potential business partners] we need to make sure that the reinsurers understand.”
On the ground, Medic Mobile is a California-based nonprofit that provides an open-source, free software toolkit that combines smart messaging, decision support, easy data gathering and management, and health system analytics for health care workers in remote areas of countries such as Kenya and India. Using mobile phones to collect information, Medic Mobile focuses its efforts on disease surveillance, antenatal care, child immunization and drug stock maintenance. Areas “where they can make a difference,” according to Jim Fruchterman, CEO of Benetech, a nonprofit software developer for the social sector.
In spite of those and other examples, most would agree that the global development sector lags well behind the private sector.
“Living in Silicon Valley, seeing what we’re doing in terms of business and technology, we can tell that the nonprofit sector is five to 15 years behind the times,” said Fruchterman, adding that private companies can draw on “terrific data” about what works in advertising, for example. But while those data are constantly updated, “there is nothing like that in the nonprofit sector,” with some development organizations still even collecting data on paper.
Said Colleen McLaughlin, director of client delivery at BroadReach, a South Africa-based firm that provides data-related solutions in emerging markets: “What is handicapping development is the significant lag in having the tools to collect and analyze real-time data.”
Meanwhile, donors and other stakeholders are beginning to demand more and better information about the effectiveness of development organizations and their programs. Fruchterman broke it all down into three areas: How much did we spend? How much did we do? And how much did it matter?
“Donors are going to be demanding more sophisticated stories about how their dollars are showing up in the SDGs,” he said.
Bagherpour worked in the development space before launching GiStrat. While helping to devise a new software program (patent pending) to help experts in their respective fields use qualitative data and expertise to create quantitative models and scenarios, he imagined a development project manager as his ideal user. However, the company will focus its marketing efforts on the legal profession. It will be available to everyone, but development professionals “are often late adopters,” he lamented.
The gold standard for development data
The gold standard for the collection and use of data for development is being set by a Utah-based rural development organization called CHOICE Humanitarian, according to Philip Beaver, professor of business information and analytics at the Daniels College of Business of the University of Denver. “I’ve never seen anyone as good as they are with data,” he said.
CHOICE Humanitarian had used data “in fits and starts for the last 15 years,” recalled Program Director Chris Johnson. “In the last few years we hit reset and took it seriously.”
Three years ago the group launched a pilot program in the Lamjung district of Nepal by collecting data on 70 indicators through surveys of all the area’s 13,500 households. The initiative borrows from the Progress Out of Poverty Index of the Grameen Foundation, the Washington D.C.-based group best known for digital solutions to addressing poverty. “That set the groundwork for where we were going to focus,” Johnson said.
The lack of baseline information presents a huge roadblock to the effective use of data in development projects, said James Mayfield, CHOICE Humanitarian co-founder and professor emeritus in public administration and Middle East studies at the University of Utah. “Few NGOs, programs and projects will spend the money,” he noted.
The survey data collected by CHOICE Humanitarian has allowed staffers to identify what Mayfield called “the most relevant variables to causality” and to figure out “what programs and interventions will work.”
For example, when the organization provided biodigesters to supply gas for cooking to replace firewood in poor households, they discovered that a year later nearly one-third were no longer working. By tracking the characteristics of the recipient families, they were able to identify the need for a train-the-trainers program for biodigester maintenance.
Ramping up beyond the dashboard
The biodigester example fits with what BroadReach’s McLaughlin said went beyond dashboard readouts to distill insights that can help organizations make the most of often scarce resources.
“Everyone in development is trying to do more with what they have,” she said. “Analytics allows you to move from working harder to working smarter.”
CHOICE Humanitarian has plans to ramp-up its program to include all of Nepal’s 75 districts and perhaps some or all of the other half-dozen countries where it works.
“With this kind of data it is far easier to go to donors,” said Mayfield. With baseline numbers and measurements of progress, “you provide reasons for why you were successful. In my experience when you have that evidence it is easier to get support.”
This is related to changing attitudes on the data-related aspect what’s known as the “overhead myth.” Traditionally data collection, analysis and reporting have been lumped as costs under the rubric of administrative overhead. Since donors have tended to stress the need to channel outlays into programs and eschew support for overhead, data got a short shrift. Development organizations “should simply say that data is not part of overhead,” Mayfield asserted.
CORRECTION: A previous version of this article stated that Grameen Foundation is based in Bangladesh and is known for its work in microfinance. The Grameen Foundation is based in Washington, D.C. and specializes in digital solutions to addressing poverty.
What are your best-practice examples of the use of data to improve decision-making for development? Share your thoughts by leaving a comment below.
With potential to change the trajectory of crises, such as famines or the spread of diseases, the innovative use of data will drive a new era for global development. Throughout this monthlong Data Driven discussion, Devex and partners — the Agence Française de Développement, BroadReach, Chemonics and Johnson & Johnson — will explore how the data revolution is changing our approach to achieving development outcomes and reshaping the future of our industry. Help us drive the conversation forward by tagging #DataDriven and @devex.
Bill Hinchberger is Devex's Paris correspondent. In his spare time, he's a freelance writer, communications consultant and educator. A native of California, he lived in Latin America for over two decades, reporting for media such as The Financial Times and Business Week. He also served as president of the São Paulo Foreign Press Club and founded the online travel guide BrazilMax.com. Assignments have taken him to over 30 countries, from Cuba to Egypt, India, Kenya, Turkey, and beyond.
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