Cross posted on “Voices”, the World’s Bank blog.
Last week I was invited to speak at the Annual Conference of the World Economic Forum in Davos, Switzerland.
There were more than 2800 official attendees, convening 1,500 business leaders from more than 100 countries, around 40 heads of state and 300 members of government and international institutions, 14 Nobel Laureates and 800 participants from academia, NGOs and civil society. An overwhelming number of people for a data geek like me, but the potential to maximize stakeholder engagement and forward looking conversations. With more than 300 sessions, it was a perfect landing space to grow and test the network we need for my new role as a data scientist at the World Bank Group’s Innovation Labs.
Data is a fundamental source of insight, monitoring and accountability. It helps us to know what is needed, what is working and what is not and identify gaps in our understanding. For the World Bank Group, innovations in big data and technologies provides new opportunities with which we can better measure our twin goals of reducing extreme poverty and boosting shared prosperity, and making progress on the sustainable development goals.
Today, we have an increasing range of new sources of data on population, space and time. They are cheaper than traditional approaches like household surveys which are however, richer in focused information. New sources of data like aerial images of road networks, crops, digital health records, phone call records, night time lights from satellites, social media sentiment, and environmental sensors provide more frequent data flows that can be used to complement official statistics, empower citizens, and provide better monitoring and feedback loops.
One of our goals, eradicating extreme poverty, is hard to measure. The lack of access to welfare systems like basic healthcare, financial tools, or shelter also means there are fewer digital sources. With new types of data, we can combine and analyze them in detail so we can, for example, understand geographical data gaps that can provide much needed information on school dropouts or profile risks and opportunities within and around slums.
When we aim to boost shared prosperity we focus on the bottom 40% of the population. With the help of all these new dimensions of data, we can help double their profits per acre of crops, we can optimize distribution networks and focus on improving the weakest links.
Climate change was the cross-cutting theme at Davos. The effects of climate change will disproportionately impact the poor. Extreme events, like floods, droughts, heat waves and cold storms can quickly disrupt the fragile livelihoods of those who can’t afford to save money, who can’t afford to wait for the next harvest, or can’t afford to get a warmer shelter. Climate change action needs data – big data. Data to measure and monitor carbon emissions on the mitigation side; data to understand environmental vulnerabilities and adaptation needs; and data to understand how to increase the resilience of communities and livelihoods to current and future climates.
Capacity building,was another explicit and recurrent discussion topic during my time at Davos. Properly generating and processing Big Data is a core activity at Innovation Labs. We just closed our first internal competition to support, with $1.5M the most promising Big Data related projects already ongoing at the World Bank. More than 100 projects were submitted, from a wide range of regions, sectors and units, which clearly shows the upcoming trend, and highlights the need to build capacity to absorb the opportunities, in our client countries.
As a scientist and technologist, I also had a very grounding experience at Davos. We geeks tend to focus on interesting problems like deep neural networks to recognize high levels of abstraction. There is certainly a huge value to, for example, recognizing geographic and temporal patterns of call records and financial transactions to infer wealth, corruption or infrastructure bottlenecks. However, sometimes interesting problems are not the most important ones. Sometimes basic capacity building is far easier to do, and has much greater impact, like streamlining the input of digital health forms, mapping current project locations, or flagging data gaps. A lot of new data work in this revolution will come from finding out upstream where existing jobs can fix existing sources of data, and publishing them properly. We will need to work on interoperability, on harmonization across time and regions, on validations and feedback channels, and moving from exploratory case studies to operational capabilities.
As it was clear and explicit at Davos, 2015 is a very important year for data driven development. Big Data will grow into more sectors, more applications, and more volumes of data with a growing potential to reach our mission. We have clear goals, and we have plenty of data and space to innovate, build capacity and optimize our resources.comments powered by Disqus