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presentation for MIT University’s Entrepreneurial Programming and
Research on Mobiles, 2008,
often, the data used in international development
decisions are stale. The information base we rely
on needs to be bolstered by building bridges with
new sources and data streams.
Opening up proprietary private-sector data
for use in international development will be
a game-changer in the coming years. To date,
public institutions have been leading on open
data (for instance, under the purview of the Open
Government Partnership). But it is the private
sector, the main repository of “big data,” that is
the holy grail. If you total all the data collected by
the U.S. Library of Congress (one of the largest
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terabytes as of April 2011. Walmart processes and
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4
The Analytics Layer:
Virtualization, Visualization
Driving Democratization
High-power analytics revolutionized the commer-
cial sector and can now do the same in the social
sector. At its core, analytics is about understanding
relationships and patterns. Analytics helped retailers
profit from unlikely trends, and it can do the same
for complex social systems. Bringing this capacity
to bear on development challenges, such as food
security and urbanization, is just the beginning.
The explosion of mobile sensors—especially
in the developing world—is facilitating a transfor-
mation. Mobile-phone subscriptions have grown
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ʇɼʃʃɼʐʌ
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developing countries) at the start of the 2000s to
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x
Lɼʃʃɼʐʌ
{
ÌɼʇiÃ
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countries as in the developed world today). About
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ʃɼÛi
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4 Abhishek Metha, “Big Data: Powering the Next Industrial Revolu
tion,” Tableau White Paper,
-
www.tableausoftware.com/learn/whitepa-
pers/big-data-revolution, accessed March 29, 2012.
day.
x
The developing world is the leading driver of
mobile big data. Voice, text, transactional, loca-
tional, and positional information can be overlaid
with the base data layer described earlier (income,
health, education, and other indicators generated
by official sources) to produce new insights into
real behavior and complex incentive structures.
Take the example of the Engineering Social
Systems lab.
6
Coupling terabytes of mobile-phone
data with Kenyan census information, the lab is
modeling the growth of slums to inform urban
planners about where to locate services such as
water pumps and public toilets. In Uganda, the
same group is developing causal structures of food
security, and in Rwanda, they used big mobile-
phone data and a random survey to model how
different people react to economic shocks. This con-
stitutes a fundamental shift from theoretical models
to models informed and built on real networks.
Development analysis has long been limited
to correlations and inferences based on correla-
tions. For the first time, big data coupled with
high-power analytics are opening up the possibility
of, if not entirely causal dynamics, then at least
more robust inferences. Our traditional methods
of inquiry have conditioned us to think in terms
of generalizing on the basis of random sampling.
But for the first time, the proliferation of mobile
sensors is making possible highly targeted yet
nonintrusive inquiry.
The rapid emergence of new data streams
has kept pace with the development of analyti-
cal capacity to draw useful inference out of them.
Twitter, for instance, generates information about
x
assets.en.oreilly.com/1/event/20/txteagle_
%20Crowd-Sourcing%20on%20Mobile%20Phones%20in%20the%20
Developing%20World%20Presentation.pdf, accessed March 29, 2012.
6 “Big Data for Social Good,” Engineering Social Systems collaboration,
ess.santafe.edu/bigdata.html, accessed December 21, 2011.
154 | USAID FRONTIERS IN DEVELOPMENT
73
the size of the entire U.S. Library of Congress
in two weeks and, together with Facebook, has
already shown its efficacy during the Arab upris-
ings. At the heart of this evolution, open-source
software systems and tools allow the simultaneous
collection, categorization, and analysis of vari-
ous data types—from Twitter hashtags, to videos,
to positional data and machine IDs. Swift River,
developed by Ushahidi, is an example of a free
open-source platform that enables rapid simulta-
neous filtering and verification of real-time data. It
also visualizes the information in dashboards that
the average user can understand.
This is particularly powerful for monitor-
ing immediate post-crisis developments when the
information flow suddenly increases, but it is also
only useful if immediately analyzed. Similar appli-
cations were successfully implemented and yielded
important insights on population movements both
in the aftermath of the earthquake in Haiti as well
as flooding in Pakistan.
Virtualization (of platforms) and visualiza-
tion (of large complex information to make it
engaging for the average user) inspire the com-
munity- or crowd-driven problem solving that
advances democratization of analytics. For example,
Data Without Borders, a pro-bono data scientist
exchange, organizes “data dives” to help leverage the
potential of information that NGOs, civil-society
organizations, and others possess but do not have
the time, capacity, or inclination to process.
We at The North-South Institute are playing
our own small part in comprehensively visualizing
Canada’s engagement with developing countries
on aid, trade, investment, migration, and a range
of other flows through the recently launched
Canadian International Development Platform.
7
7 Canadian International Development Platform, hosted by The North-
South Institute, www.cidpnsi.ca.
The Feedback Layer: Deep
Context, Complex Microsystems,
Real-time Loops
The efficacy of the feedback layer is also new.
Targeted crowdsourcing has already come a long
way. The Ushahidi experience in Kenya, for
instance, also worked for monitoring elections
in Afghanistan. Mobile-phone SMS platforms
have been adapted to make participatory budget-
ing more inclusive in hard-to-reach areas, such as
conflict-affected South-Kivu in the Democratic
Republic of the Congo—and results have been
encouraging. The experience of the Development
A “statistical tragedy”: Most
of Africa’s countries still use
a 1960s method of accounting
to generate fundamental data,
such as GDP.
Loop initiative has already shown that, with cre-
ative use of available technologies and committed
partners, it is possible to obtain direct feedback
from intended recipients of interventions.
To understand how powerful the feedback
layer can be, consider the experience of the Mobile
Accord. At the initiative of the World Bank’s
World Development Report 2011, the Accord
ran Geo Poll, an SMS-based targeted polling in
the Democratic Republic of the Congo. The poll
asked 10 questions that included sensitive topics,
such as sexual violence against women. The survey
produced 1.2 million text responses, and the
outputs were turned into the video “DRC Speaks,”
TECHNOLOGY AND SERVICE DELIVERY | 155
81
which captured people’s responses to questions
about their experiences in their own words. This
ended up being one of the largest surveys ever
conducted in the country.
8
Some of the most valuable data in develop-
ment come from surveys, including household, labor
market, living standard, and other social surveys. But
there are two key problems with such surveys: time
(they take time to implement and can only be done
infrequently) and high costs. Mobile technology is
helping get around these issues. The World Bank is
piloting an interesting initiative in Latin America
called “Listening to LAC” (L2L)
9
where several types
of mobile technologies are being deployed to con-
duct real-time (higher frequency) self-administered
surveys, to generate panel data on key questions
pertaining to vulnerability and coping strategies.
While still in a pilot phase, this is the first time such
information is being collected near real-time and
with lower costs than large national surveys.
There is a pattern here. In the base layer, more
and more data are opening every day. In the analyt-
ics layer, experimental ideas are leaving the lab for
real-world application. Virtualization and visualiza-
tion are helping foster new communities geared
toward collaborative problem solving. Similarly,
in the feedback layer, tools are also democratizing.
Ushahidi created an easy-to-use version of their
implementation, called CrowdMap. Anyone who
knows how to set up an email account can use
the tool to set up their own incident mapping of
whatever trend, alert, or issue on which they are
interested in getting feedback from the crowd.
8 The World Bank, “DRC Speaks,” World Development Report 2011
multimedia library, wdr2011.worldbank.org/media-library, accessed
March 29, 2012.
9 “Getting the Numbers Right: Making Statistical Systems a Real Plus
for Results,” The World Bank: IBRD Results, March 2010, siteresourc
es.worldbank.org/NEWS/Resources/Gettingthenumbersright4-19-10.
pdf
-
, accessed March 29, 2012.
At sunset, a young girl tests out a new seesaw on
a playground built by the Elizabeth Glaser
Pediatric AIDS Foundation at the Mkhulamini
Clinic in Swaziland. This year, the Foundation
will launch a USAID-funded, ve-year program
to expand services preventing mother-to-child
transmission of HIV. | Photo: Jon Hrusa, Elizabeth
Glaser/Pediatric AIDS Foundation
Looking Ahead
How we think about data and analysis in the field
of international development is changing rapidly,
and faster than many organizations that “do devel-
opment” are prepared for.
The open-data movement has widened access
to a broad range of basic contextual information.
A similar push is needed to open private-sector
big data in the service of social good. Powerful
analytical tools and collaborative platforms are
dramatically changing what is possible for even
the most intractable challenges like understanding
socioeconomic risks and responses, dealing with
urban planning, and better preparing for emergen-
cies. For the first time, we have a feedback layer,
which has made possible deep and near real-time
awareness of what is working or not working,
where, and why. Together, big data, democratized
analytics, and the ability to tap deep contexts will
change the way we think and do development in
the coming years.
Aniket Bhushan is a Senior Researcher at The North-
South Institute and leads the Canadian International
Development Platform. The views expressed in this
essay are his own, and do not necessarily represent
the views of the United States Agency for International
Development or the United States Government.
156 | USAID FRONTIERS IN DEVELOPMENT
1
TECHNOLOGY AND SERVICE DELIVERY | 157
51
Cory O’Hara
Developing-country Producers and
the Challenge of Traceability
A
t the opening of the last century the
introduction of mass production tech-
niques (the assembly line, specialization,
and replaceable parts) fostered unprecedented
expansion of consumer goods through the produc-
tion and distribution of identical goods at increas-
ingly lower unit costs. In the early decades of the
21st century, the basic concept of a commodity as
a mass-produced unspecialized product is evolv-
ing with the growing recognition that every unit
of product has uniquely identifiable traits that can
be tracked from origin to consumption and that
confer different market value.
The implications of product traceability, or
tracking products from origin to consumption
(“farm to fork”), affects virtually all development
sectors—agriculture (food safety), health (coun-
terfeit pharmaceuticals), security, the environ-
ment (carbon footprint), governance (diversion of
commodities), and the application of technology.
While the impact of traceability is most immedi-
ate for goods entering developed country mar-
kets, traceability will increasingly be adopted in
developing countries, particularly given the rise of
a growing consumer middle-class and the relatively
higher levels of fraudulent or dangerous products
entering those markets.
Governments and donors are implementing
numerous programs that seek to expand opportu-
nities for developing-country producers, particu-
larly in the agriculture sector, to export directly
to developed country markets. The benefits are
obvious—higher prices and improved quality
standards. The costs related to implementation
of traceability, however, will require substantial
investment, and will be especially challenging for
the small-scale agricultural producers targeted by
assistance programs. The challenges for integrat-
ing women producers will require special atten-
tion, given that female smallholder farmers are
generally both less capitalized and have less access
to new technology.
As developing countries seek to diversify
their economies beyond exports of primary agri-
cultural commodities and integrate into global
manufacturing supply chains, the challenges
158 | USAID FRONTIERS IN DEVELOPMENT
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