DEVELOPMENT

4 The Afd2025 scenarios
BABEL 3.0
GREENING
WITHOUT STATES
Box 11
Big Data or Big Brother: a data revolution
for development?
The colossal flows of digital data via networks for telecommunication (mobile telephony,
Internet), energy transmission (smart grid), electronic payment (credit cards, mobile
phones), geolocation and satellite imaging are a massive global phenomenon. What
is more, their volume is constantly increasing: in 2013, they accounted for 98% of all
information in the world – the rest being in books, on cassettes, films…). They are highly
diverse: photos, texts, satellite images, length and location of calls, electricity consumption,
commodity prices, share prices, tweets, geolocation, vital signs (heart rate, the skin’s pH
level, etc.), DNA sequencing, sea levels, temperature, friction on the engine parts of the
UPS vehicle fleet… And storage capacities have also increased (new technologies and lower
costs), as have computational and processing performances (new algorithms and machine
learning). These three properties – Volume, Variety, Velocity – have made “Big Data”
into the new source of information to be explored, offering the promise of hefty revenues
for their producers. Big Data has transformed each of us into a “prosumer”, in other
words, both a producer and consumer of data. The quantity of data is growing at a
quickening pace thanks to the development of the “Internet of Things” technology.
The beginning of this 21st century is thus presented as the age of Big Data.
Examples of its application are many. Using geolocation, it is now possible to map, with
some precision and in near-real time, farmland characteristics (smart farming) and urban
population shifts and the way in which these are changing. Each day, research is helping
us to discover how the “data revolution” and Big Data are contributing to development
by identifying patterns in the spread of epidemics, analyzing population displacements,
eliminating traffic congestion in cities such as Abidjan, or identifying vulnerable populations
in the areas most exposed to the effects of climate disturbances in Senegal. The analysis
of Call Detail Records (CDRs) gives indications on lifestyles (differences between urban
and rural life), and makes it possible to anticipate urbanization trends. Google says that
is able to predict influenza outbreaks by analyzing searches on its search engine (GoogleFlu
project). Monitoring the patterns of how the Ebola virus spreads is also a challenge, even
if this is a far more complex and sensitive task. Finally, by studying telephone habits, call
distances and durations, their frequency, the variety of call destinations – which reflects
the abundance of social connections – it is possible for instance to develop a poverty
indicator in near-real time.
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Foresighting for Development
Development agencies, steering through future worlds. Afd2025
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