Can big data fight poverty and corruption?
Big data is proving to be transformative in the private sector, though can it also help solve international development problems?
The World Bank Finances team came together with UN Global Pulse, Qatar Computing Research Institute, UNDB, UNDP, and DataKind for a Data Dive to explore practical and tangible ways to (1) demonstrate that open/big data can help improve poverty measurement, and (2) improve how organizations such as the Bank tackle fraud and anti-corruption in projects.
This talk will throw light on initial findings from the data dive, and the road ahead.
World Bank’s Big Data Dive explored several questions; the talk will introduce what we found during the exercise - including:
• Predicting Small-Scale Poverty Measures from Night Illumination - can freely available satellite imagery, showing average nighttime illumination, serve as a reasonable poverty measurement proxy?
• Measuring Socioeconomic Indicators in Arabic Tweets - can Twitter data help you understand socio-economic trends in countries?
• Social networking analysis for risk measurement - can you forecast project risk using social networking analysis tools?
• Can you use simple heuristic auditing to sniff out discrepancies in expenditure data - what do you do when you have the information but don’t know if it contains signals about potential fraud?
I am an analyst with the World Bank’s Open Finances program at Washington D.C. I design and deliver open financial data services, help study the demand for open financial data, and engage with broader open data community to create useful applications of this data. I am currently focused on the World Bank’s India program, and figuring out how open data can help there. Prior to the Bank, I have been an online journalist, a learning designer, and an advertising creative. I survived Eco (H) at DU, and then spent some quality time at the Indian Institute of Mass Communication.