ICCM 2013 Panel 1: What's so Big about Big Data?
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Transcript of ICCM 2013 Panel 1: What's so Big about Big Data?
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Panel 1@CrisisMappers #ICCM
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What's so Big about
Big Data?
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Sanjana Hattotuwa
@sanjanah
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Jon Gosier
@jongos
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Anahi Iayala Iaccuci
@anahi_ayala
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Big Data it’s
not about the
Data
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Big Data is
about the
process
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Information Ecosystem
Trusted Sources
Channels
ToolsInformation Exchanged
Resilience
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How Big is Big Data?
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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
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Big Data needs to be culturally sensitive
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What happens
with
Big Data?
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Big Data + Context + Action
= Information
Information saves lives
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Anahi Ayala Iacucci
Senior Innovation Advisor
Internews Center for Innovation & Learning
http://innovation.internews.org
@anahi_ayala
@info_innovation
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Emmanuel Letouzé
@Data4Dev @manucartoons
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4 questions on the Big Data-Rich Future of Humanitarian Assistance
Emmanuel LetouzéFellow, Harvard Humanitarian Initiative
PhD Candidate, UC Berkeley
Non-Resident Adviser, International Peace Institute
Nairobi, November 21st, 2013
5th International Conference of Crisis MappersPanel I—What is so Big about Big Data?
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(Reference..)
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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)
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1. What is Big Data about—and not about?
Movement of an individual in Rwanda over 4 years (Source J. Blumenstock)
• 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
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2. How will Big Data grow & age?
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Stock of world data, circa 2020?Stock of world data, circa 1980 (assume)
2. How will Big Data grow & age?
1 10 1000 10000
50 years
90 days
Unknown data
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① Descriptive analysis (e.g. maps)② Predictive analysis (proxying vs. forecasting)③ Diagnostics (causal inference)
3. How has / may it be used for humanitarian assistance purposes?
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Source: Letouzé and Prydz, 2013
Pattern recognition + anomaly detection: Violent event in ACLED data vs. cellphone call volume in Ivory Coast
3. How has / may it be used for humanitarian assistance purposes?
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NationalStatistical
Institutes carryout surveys
Telefonica teamused their data to‘predict’ SELs fromCell Phone Usage
Predict the present(SELs for non-
surveyed regions) and monitor the
future (trackchanges over time)
Survey from “a major city in Latin
America”
Example: “Prediction of Socio-Economic Levels Using Cell-Phone Records” (Telefonica research, 2011)
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i. Main risks are① Creation of a ‘new’ digital divide
=>Recentralization of decision-making, reversing recent trends/efforts
② Dehumanization / de-democratization of decision-making (cf drones, killer-robots)
③ Confidentiality / security: e.g. CDRs de-anonymization and identification
4. What are the traps and priorities ahead?
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ii. Main challenges/questions are① 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 data3. Models of human response to emergencies 4. Causal inference
4. What are the traps and priorities ahead?
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All data collectedall data shared
Extreme societal considerations / Open Data
society
All data collected,No data shared
Extreme commercial considerations / surveillance
No data collected,No data shared
Extreme individual consideration / Full privacy
% PERSONNAL DATA COLLECTED
% PERSONNAL DATA SHARED
Right Balance?Right Balance?
%personal data shared
%personal data collected
Source: Letouzé and Vinck, 2013
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Jon Gosier
@jongos
Anahi Iayala Iaccuci
@anahi_ayala
Emmanuel Letouzé
@Data4Dev @manucartoons
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Thanks for participating!@CrisisMappers #ICCM
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Many thanks to our sponsors!
CrisisMappers 2013