Data for Development
Unleashing the power of data for sustainable development
About a decade ago, I was working with the United Nations Development Programme (UNDP) to help evaluate the econometric models used to measure the number of people in the world living below $1 USD a day. Halving the portion of people living below this poverty line by 2015 was at the core of the Millennium Development Goals (MDGs) that were adopted in 2000.
Quantitative targets such as this were a cornerstone of the ground-breaking MDGs. However, what people quickly realized was that the challenge wasn’t just in meeting the goals, but also in getting the data to measure the MDGs.
This week marks the final week of the 73rd United Nations General Assembly. Three years ago, the Sustainable Development Goals (SDGs) were adopted to replace the MDGs. The SDGs are even more ambitious and wide-reaching. They tackle everything from poverty and hunger to gender inequality and climate change. Like the MDGs, there are quantitative targets, but this time there are 232 of them!
Data and the SDGs
Data plays a fundamental role in achieving the SDGs by 2030. Data drives the measuring and monitoring of progress, insights to inform strategy, and now, almost two decades since the introduction of the MDGs, the community is increasingly looking towards data to bolster implementation itself.
The good news is, the data and techniques exist. Here are some examples:
Survey data: Satellite imagery and other GIS data can be used to fill gaps in survey data where such gaps exist. The gaps could be in-between time periods or in geographic coverage and are at times necessitated by a need to lower the cost of data collection. Indicators measured can be anything from smallholder farmers’ land use to gender inequality.
Social media data: Social media provides both a wealth of new data for analysis as well as a powerful channel for mobilizing action. Sentiment analysis can gauge things like mental health or attitudes towards immunization. The UN Global Pulse Lab in Jakarta has even used social media to map hotspots for air pollution using computer vision on posted photos. Ixio is hosting a Zindi competition to use machine learning to predict the number of retweets a tweet will get. #SDGs #GlobalGoals #Act4SDGs
Natural language processing: With hundreds of thousands of the actors around the globe tackling 232 targets, how can we ensure that the information is getting to the right people at the right time? Devex is hosting a Zindi competition that will help Devex automate the process of sifting through thousands of documents, news articles, tenders, and other information and categorizing them by SDG target. Devex then distributes the content to the relevant organizations around the world that are implementing programmes towards the SDGs.
End hunger (SDG Goal 2): Innovative companies such as Farm Pin, Wazihub, and Aerobotics help farmers monitor their crops and land more efficiently using satellite, sensor (Internet of Things - IoT), and drone data, respectively.
Access to transportation (SDG target 11.2): Companies such as Mobiticket and WhereIsMyTransport are using data to improve the efficiency, accessibility, and transparency of formal and informal public transportation systems around the world. Mobiticket has partnered with Uber to host a Zindi competition that uses Uber Movement data and machine learning to predict demand for public transport routes.
Last month saw the launch of Zindi, a data science competition platform. Zindi, will help to grow the number of use cases where data is employed to help meet the SDGs. We, as a global community, have come a long way since 2000, and it is only through collaboration and innovation that the goals will be met by 2030. Please contact Zindi to explore other development focused data use cases