African Students using Open Data, Social Impact Bonds, ‘Digital Smoke Signals’ & the Data Dilemma #datadigest
This week we learn about the Resilient Africa Network (RAN), which is helping African students use open data for disaster relief. There is also some interesting insight into the link between open development and social impact bonds, while the data dilemma is brought to the fore. Other articles speak to the use of ‘Digital Smoke Signals’ generated from big data for improved development projects and policies, as well as the need to look at not just big data but small data too.
Data for Public Service Delivery
Lindsay Read an AidData Summer Fellow talks about Resilient Africa Network (RAN) which is run by Makerere University and 19 other African universities in partnership with USAID’s Higher Education Solutions Network. It harnesses locally based solutions to decrease the effects of disasters and rehabilitate communities in disaster-prone areas. At the launch of RAN, innovative projects designed by students to enhance resilience pointed to the tremendous importance of accessible, transparent information for increased disaster resilience and development outcomes. RAN is establishing Resilience Innovation Labs within participating universities throughout Africa, coupled with online laboratories and a new online science library to help with its work.
This Guardian article by Mark Herringer explains how incentivising investment and opening information can help local entrepreneurs fill service delivery gaps. He speaks about the challenges that development organisations face in trying to deliver effective services with limited resources to underserved parts of Africa because of difficulty in gaining local knowledge and insight. There are also few incentives for citizens to create social enterprises that can support NGOs with service delivery and not enough openly available data on relevant solutions. He says that “by combining the concepts of open development and development impact bonds – a financing model based on payments-on-results – change becomes possible and can come from the grassroots.” He cites Owen Barder and the Centre for Global Development’s creation of an outcomes-based bond, which invites public and private sector actors to agree on a social outcome they want to achieve and gives some examples of how social enterprises can improve healthcare service delivery in their communities.
Elizabeth Good Christopherson, uses past examples of the use of data for public services in the US to highlight some of its constraints. For example, the City of Boston used the motion-sensing capabilities of smart phones, through encouraging voluntary download of a Street Bump app to automatically send the city information about the condition of the streets they’re driving on. The research found that there were more potholes reported in wealthy areas of the city than in poor ones, but this was only because wealthy people were far more likely to own smart phones and to use the Street Bump app. While noting that the use of data today is unavoidable and important some suggestions for responsible use are given.
Big Data for Development
This New York Times article by Steve Lohr discusses the United Nations Global Pulse entrepreneurial initiatives on “Big Data for development”, which aims to improve development and aid programs and policies through real-time monitoring and prediction. Research has found for instance that analyzing Twitter messages can give “digital smoke signals of distress”, an early warning of a problems to come such as spikes in unemployment, price rises and disease. Global Pulse also promotes the concept of “data philanthropy” and the creation of a public “data commons,” in which companies contribute large anonymised customer data sets for development research. They are building a network of Pulse Labs to this end, the first of which operates in Jakarta Indonesia.
Chris Albon reviews a post in TechPresident by Jeffrey Warren of Public Lab who says that the big data risks empowering the powerful much more. He says that most data science requires only a simple understanding of some basic methods of data collection and analysis, which he calls a “localbrew data”: data made by locals, for locals, to solve local issues through using data first aid kits. These include free tools that allow exploration of your data without a data scientist.