ICCM 2013 Panel 1: What's so Big about Big Data?

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Slides from the 2013 International Conference of Crisis Mappers in Nairobi, Kenya. Learn more at crisismappers.net

Slides from the 2013 International Conference of Crisis Mappers in Nairobi, Kenya. Learn more at crisismappers.net

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  • 1. Panel 1 @CrisisMappers #ICCM
  • 2. What's so Big about Big Data?
  • 3. Sanjana Hattotuwa @sanjanah
  • 4. Jon Gosier @jongos
  • 5. Anahi Iayala Iaccuci @anahi_ayala
  • 6. Big Data it’s not about the Data
  • 7. Big Data is about the process
  • 8. Trusted Sources Resilience Channels Information Ecosystem Information Exchanged Tools
  • 9. How Big is Big Data?
  • 10. The (forgotten) Humanitarian Crisis in 2013 Yemen = 10.5 m Chad = 1.8 m Afghanistan = 5.7 m DRC = 2.6 m Somalia = 3.8 m CAR = 4.6 m
  • 11. Big Data needs to be culturally sensitive
  • 12. What happens with Big Data?
  • 13. Big Data + Context + Action = Information Information saves lives
  • 14. Anahi Ayala Iacucci Senior Innovation Advisor Internews Center for Innovation & Learning http://innovation.internews.org @anahi_ayala @info_innovation aayala@internews.org
  • 15. Emmanuel Letouzé @Data4Dev @manucartoons
  • 16. 5th International Conference of Crisis Mappers Panel I—What is so Big about Big Data? 4 questions on the Big Data-Rich Future of Humanitarian Assistance Emmanuel Letouzé Fellow, Harvard Humanitarian Initiative PhD Candidate, UC Berkeley Non-Resident Adviser, International Peace Institute eletouze@berkeley.edu Nairobi, November 21st, 2013
  • 17. (Reference..)
  • 18. 1. What is ‘Big Data’ about—and not about? ① Big Data as data == “traces of human actions picked up by digital devices” (Letouzé, Meier and Vinck) 1. “Digital breadcrumbs” (Sandy Pentland) 2. Open web data (social media, online news..) 3. Sensing (satellite, meters..) ② Big Data as data is not ‘about’ size—it’s a primarily qualitative shift ③ Big Data is “not about the data” (Gary King)
  • 19. 1. What is Big Data about—and not about? • • • Big Data as data doesn’t have to be big to be different Big Data as data is about very many very small data produced by / about connected individuals (big data is small data—it can also be slow data) Big Data takes intent and capacities Movement of an individual in Rwanda over 4 years (Source J. Blumenstock)
  • 20. 2. How will Big Data grow & age?
  • 21. 2. How will Big Data grow & age? Stock of world data, circa 1980 (assume) Stock of world data, circa 2020? 90 days 50 years 1 0 Unknown data 1 1000 0 1000
  • 22. 3. How has / may it be used for humanitarian assistance purposes? ① Descriptive analysis (e.g. maps) ② Predictive analysis (proxying vs. forecasting) ③ Diagnostics (causal inference)
  • 23. 3. How has / may it be used for humanitarian assistance purposes? Pattern recognition + anomaly detection: Violent event in ACLED data vs. cellphone call volume in Ivory Coast Source: Letouzé and Prydz, 2013
  • 24. Example: “Prediction of Socio-Economic Levels Using Cell-Phone Records” (Telefonica research, 2011) National Statistical Survey from “a Institutes in Latin major citycarry out surveys America” Telefonica team used their data to ‘predict’ SELs from Cell Phone Usage Predict the present (SELs for nonsurveyed regions) and monitor the future (track changes over time)
  • 25. 4. What are the traps and priorities ahead? i. Main risks are ① Creation of a ‘new’ digital divide =>Recentralization of decisionmaking, reversing recent trends/efforts ② Dehumanization / de-democratization of decision-making (cf drones, killer-robots) ③ Confidentiality / security: e.g. CDRs deanonymization and identification
  • 26. 4. What are the traps and priorities ahead? ii. Main challenges/questions are ① Political: Engaging with & empowering at-risk / affected people and communities for community resilience, feedback loops, agile response..(urgency vs. sustainability?) ② Legal-institutional: Devising principles and frameworks for ‘responsible’ data sharing and analysis (D4D team) ③ Theoretical-methodological: further research / progress to take place on 1. Sample bias correction 2. Privacy: erasable future, noise in data 3. Models of human response to emergencies 4. Causal inference
  • 27. % PERSONNAL DATA SHARED %personal data shared All data collected all data shared Extreme societal considerations / Open Data society Right Balance? Source: Letouzé and Vinck, 2013 %personal DATA COLLECTED % PERSONNAL data collected No data collected, No data shared Extreme individual consideration / Full privacy All data collected, No data shared Extreme commercial considerations / surveillance
  • 28. Jon Gosier @jongos Anahi Iayala Iaccuci @anahi_ayala Emmanuel Letouzé @Data4Dev @manucartoons
  • 29. Thanks for participating! @CrisisMappers #ICCM
  • 30. CrisisMappers 2013 Many thanks to our sponsors!