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

Panel 1@CrisisMappers #ICCM

What's so Big about

Big Data?

Sanjana Hattotuwa

@sanjanah

Jon Gosier

@jongos

Anahi Iayala Iaccuci

@anahi_ayala

Big Data it’s

not about the

Data

Big Data is

about the

process

Information Ecosystem

Trusted Sources

Channels

ToolsInformation Exchanged

Resilience

How Big is Big Data?

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

Big Data needs to be culturally sensitive

What happens

with

Big Data?

Big Data + Context + Action

= Information

Information saves lives

Anahi Ayala Iacucci

Senior Innovation Advisor

Internews Center for Innovation & Learning

http://innovation.internews.org

@anahi_ayala

@info_innovation

[email protected]

Emmanuel Letouzé

@Data4Dev @manucartoons

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

[email protected]

Nairobi, November 21st, 2013

5th International Conference of Crisis MappersPanel I—What is so Big about Big Data?

(Reference..)

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)

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

2. How will Big Data grow & age?

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

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?

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?

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)

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?

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?

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

Jon Gosier

@jongos

Anahi Iayala Iaccuci

@anahi_ayala

Emmanuel Letouzé

@Data4Dev @manucartoons

Thanks for participating!@CrisisMappers #ICCM

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CrisisMappers 2013