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Big Data for Development: From Information- to Knowledge Societies

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The article uses an established three-dimensional conceptual framework to systematically review literature and empirical evidence related to the prerequisites, opportunities, and threats of Big Data Analysis for international development. On the one hand, the advent of Big Data delivers the cost-effective prospect to improve decision-making in critical development areas such as health care, employment, economic productivity, crime and security, and natural disaster and resource management. This provides a wealth of opportunities for developing countries. On the other hand, all the well-known caveats of the Big Data debate, such as privacy concerns, interoperability challenges, and the almighty power of imperfect algorithms, are aggravated in developing countries by long-standing development challenges like lacking technological infrastructure and economic and human resource scarcity. This has the potential to result in a new kind of digital divide: a divide in data-based knowledge to inform intelligent decision-making. This shows that the exploration of data-based knowledge to improve development is not automatic and requires tailor-made policy choices that help to foster this emerging paradigm.
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Big Data for Development:
A Review of Promises and Challenges
Martin Hilbert, University of California, Davis; hilbert@ucdavis.edu
Authors version submitted to the Journal Development Policy Review of the Overseas Development Institute
http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-7679
Abstract
The article uses a conceptual framework to review empirical evidence and
some 180 articles related to the opportunities and threats of Big Data
Analytics for international development. The advent of Big Data delivers the
cost-effective prospect to improve decision-making in critical development
areas such as health care, economic productivity, and security. At the same
time, all the well-known caveats of the Big Data debate, such as privacy
concerns and human resource scarcity, are aggravated in developing
countries by long-standing structural shortages in the areas of infrastructure,
economic resources, and institutions. The result is a new kind of digital
divide: a divide in data-based knowledge to inform intelligent decision-
making. The article systematically reviews several available policy options to
foster the opportunities and minimize the risks.
Keywords: Big Data, decision-making, innovation, ICT, digital divide, digital, international development.
Acknowledgements: The author thanks International Development Research Centre Canada (IDRC) for
commissioning a more extensive study that laid the groundwork for the present article. He is also indebted to Manuel
Castells, Nathan Petrovay, Francois Bar, and Peter Monge for food for thought, and to Matthew Smith, Rohan
Samarajiva, Sriganesh Lokanathan, and Fernando Perini for helpful comments on draft versions, and thanks the
United Nations Economic Commission for Latin America and the Caribbean (UN-CEPAL), where part of the research
was undertaken. The views expressed herein are those of the author and do not necessarily reflect the views of the
United Nations.
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Table of Contents
1. Characteristics of Big Data Analytics ................................................. 3
2. Conceptual Framework BD4D ........................................................... 7
3. Application of Big Data for Development .......................................... 9
3.1 Tracking words .............................................................................................................................. 9
3.2 Tracking locations ....................................................................................................................... 11
3.3 Tracking nature ........................................................................................................................... 12
3.4 Tracking transactions .................................................................................................................. 13
3.5 Tracking behavior ........................................................................................................................ 14
3.6 Tracking production .................................................................................................................... 15
3.7 Tracking other data ..................................................................................................................... 16
4. Digital Big Data Divide ..................................................................... 17
4.1 Infrastructure access ................................................................................................................... 17
4.1.1 Challenges .................................................................................................................................. 17
4.1.2 Options ....................................................................................................................................... 19
4.2 Generic services .......................................................................................................................... 20
4.2.1 Challenges .................................................................................................................................. 20
4.2.2 Options ....................................................................................................................................... 21
4.3 Capacities and skills .................................................................................................................... 22
4.3.1 Challenges .................................................................................................................................. 22
4.3.2 Options ....................................................................................................................................... 24
5. Policy and Strategy .......................................................................... 24
5.1 Incentives: positive feedback ...................................................................................................... 24
4.2 Regulation: negative feedback .......................................................................................................... 27
6. Critical Reflection: all power to the algorithms? ............................. 30
7. Conclusion ....................................................................................... 32
References ............................................................................................ 33
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The ability to “cope with the uncertainty caused by the fast pace of change in the economic,
institutional, and technological environment” has turned out to be the “fundamental goal of
organizational changes” in the information age (Castells, p. 165). As such, also the design and the
execution of any development strategy consist of a myriad of smaller and larger decisions that
are plagued with uncertainty. From a theoretical standpoint, every decision is an uncertain,
probabilistic
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gamble based on some kind of prior information
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(e.g. Tversky and Kahneman,
1981). If we improve the basis of prior information on which to base our estimates, our
uncertainty will be reduced on average. The better the prior, the better the estimate, the better
the decision. This is not merely an intuitive analogy, but one of the core theorems of information
theory and provides the foundation for all kinds of analytics (Cover and Thomas, 2006; p. 29; also
Rissanen, 2010).
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The Big Data
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paradigm (Nature Editorial, 2008) provides a vast variety of new
kinds of priors and estimation techniques to inform all sorts of decisions. The impact on the
economy has been referred to as “the new oil” (Kolb and Kolb, 2013; p.10). Its impact on the
social sciences can be compared with the impact of the invention of the telescope for astronomy
and the invention of the microscope for biology (providing an unprecedented level of fine-
grained detail). This article discusses its impact on international development.
1. Characteristics of Big Data Analytics
From a historical perspective, this latest stage of the ongoing Information and Communication
Technology (ICT) evolution goes back to early mass-scale computing, e.g. the 1890 punched card
based U.S. Census that processed some 15 million individual records, aimed at improving
governance (Driscoll, 2012). The often-cited difference of today’s Big Data in terms of velocity,
volume and variety of data (McAfee and Brynjolfsson, 2012; Hurwitz, et al., 2013) is due to recent
exponential increases in (a) telecommunication bandwidth that connects a network of (b)
centralized and decentralized data storage systems, which are processed thanks to (c) digital
computational capacities.
(a) Information flow: During the two decades of digitization, the world's effective capacity to
exchange information through two-way telecommunication networks grew from the
informational equivalent of 2 newspaper pages per person per day in 1986 (0.3 optimally
compressed exabytes worldwide, 20 % digitized) to six entire newspapers two decades later in
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“Models must be intrinsically probabilistic in order to specify both predictions and noise-related deviations from
those predictions” (Gell-Mann and Lloyd, 1996; p. 49).
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Per mathematical definition, probabilities always require previous information on which we base our probabilistic
scale from 0 % to 100 % of chance (Caves, 1990). In other words, every probability is a conditional probability.
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Note that we have to condition on real information (not “miss-information”) and that this theorem holds on
average (a particular piece of information increases uncertainty).
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The term ‘Big Data (Analytics)’ is capitalized when it refers to the discussed phenomenon.
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2007 (65 exabytes worldwide, 99.9 % digitized) (Hilbert and López, 2011; Hilbert, 2011a). As a
result, in an average minute of 2012, Google received around 2,000,000 search queries,
Facebook users shared almost 700,000 pieces of content, and Twitter users roughly 100,000
microblogs (James, 2012).
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This growth has occurred in both developed and developing
countries (Hilbert, 2014c; ITU, 2012, Ch.5). For example, the telecommunications capacity of
large Asian countries, like China, India and Russia, was way above what could economically
expected from these countries during the period between 1995 and 2005 (Hilbert, 2011c). The
five leading countries in terms of Facebook users in 2013 included India, Brazil, Indonesia and
Mexico (Statista, 2014), while in 2011 Kuwait and Brunei had more Twitter users per capita
than the UK or U.S., Chile more than Canada, and Brazil more than France or Germany (Mocanu
et al. 2013). In contrary to analog information, digital information inherently leaves a trace that
can be analyzed (in real-time or later on).
(b) Information stock: At the same time, our technological memory roughly doubled about every
three years, growing from 2.5 exabytes in 1986 (1 % digitized), to around 300 exabytes in 2007
(94 % digitized) (Hilbert and López, 2011; 2012). Already in 2010, it cost merely US$ 600 to buy
a hard disk that can store all the world’s music (Kelly, 2011). This increased memory has the
capacity to ever store a larger part of the growing information flow. During 1986, using all of
our technological storage devices (including paper, vinyl, tape, and others), we could
(hypothetically) have stored less than 1 % of all the information that was communicated
worldwide (including broadcasting and telecommunication). By 2007 this share increased to 16
% (Hilbert and López, 2012).
(c) Information computation: We are still only able to analyze a small percentage of the data
that we capture and store (resulting in the often-lamented “information overload”). Currently,
financial, credit card and health care providers discard around 80-90 % of the data they
generate (Zikopoulos, et al., 2012; Manyika, et al., 2011). The Big Data paradigm promises to
turn an ever larger part of this “imperfect, complex, often unstructured data into actionable
information” (Letouzé, 2012; p. 6). This expectation is fueled by the fact that our capacity to
compute information has grown two to three times as fast as our capacity to store and
communicate information (60-80 % annually vs. 25-30% per year) (Hilbert and López, 2011,
2012). This allows us to fight the fire of the digital information deluge with the fire of digital
computation to make sense of the data.
The quantitative explosion of these three kinds of digital capacities has led to five distinguished
qualitative characteristics in the way data is treated.
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Additional to these mainly human-generated telecommunication flows, surveillance cameras, health sensors, and
the “Internet of things” (including household appliances and cars) are adding an ever growing chunk to ever
increasing data streams (Manyika, et al., 2011).
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(i) Big data is produced anyways. The almost inevitable digital footprint in digital networks
created a plethora of opportunities to find alternative low-cost data sources. Those byproducts
of digital conduct can often be used to replace traditional data sources (like surveys) with proxy
indicators that correlate with the variable of interest. The well-known epitome is Google’s
illustrious use of the 50 million most common search terms to predict the spread of the
seasonal flu between 2003 and 2008 (Ginsberg et al. 2009; Lazer et al. 2014). The data source
(search terms) is a digital byproduct, but has the potential to replace official statistics form
disease prevention and control authorities with cheap real-time data. This also implies that
most Big Data sources are not produced as a result of a specific research question, which is
different from most traditional data sources. As such Big Data often requires post-factum, and
not ex-ante interpretation.
(ii) Big Data replaces random sampling. Being a digital footprint of what happens in the real
world, Big Data often captures all there is (sampling n = universe N). For example, with a global
penetration of over 95 % (ITU, 2014) (including 75 % access among those making US$1 per day
or less, Naef et al., 2014)), mobile phones became a universal data source. Mobile phone
records can be used to infer socio-economic, demographic, and other behavioral trades
(Raento et al., 2009). For example, it has been shown how the prediction of socioeconomic
level in a geographic region can automatically be performed from mobile phone records (Frias-
Martinez and Virseda, 2013; Martínez and Martínez, 2014). Prediction accuracy depends on the
combination of variables (for example, predicting gender from mobile phone behavior is
surprisingly tricky (Blumenstock et al., 2010; Frias-Martinez, et al. 2010)), but using data records
like call duration or frequency it is generally around 80-85% (Frias-Martinez and Virseda, 2013;
Blumenstock et al., 2010; Frias-Martinez, et al. 2010; Soto et al. 2011). Since the mobile phone
is universal in most strata, there is no need for sampling.
(iii) Big Data is often accessible in real-time. One of the most common real-time sources for big
data is the incessant chatter in online social media. This source is especially important in
developing countries, considering the acceptance of social networks in developing countries
(see above) and the wide arrange of content they provide. The language content of Twitter
microblogs has been used to approximate cultural identities, international migration and
tourism mobility, including in countries like Malaysia, the Philippines, Venezuela and Indonesia
(Mocanu et al. 2013), and it has been shown that the 140 character long micro blogs from
Twitter contained important information about the spread of the 2010 Haitian cholera
outbreak up to two weeks earlier than official statistics (Chunara et al. 2012).
(iv) Big Data merges different sources. The often messy and incomplete digital footprint left
behind by digital conduct can be compensated by data redundancy from different sources,
often referred to as data fusion. For example, Thomson Reuters MarketPsych Indices (TRMI)
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distills daily over 3 million news articles and 4 million social media sites through an extensively
curated language framework (MarketPsych, 2014). It not only assesses different emotional
states (such as confusion, pessimism, urgency etc.), but also opinions (such price forecasts etc.)
and specific topics (such as special events, etc.). As in most Big Data exercises, not one single
row of data is complete (e.g. not everybody provides social media feeds). However, data
redundancy allows to make up for this fact by the complementary treatment of different
sources. In 2013, the company provides 18,864 separate indices, across 119 countries, curated
since 1998, and updated on a daily or even minute basis. The result is a fine-grained, real-time
assessment of the local, national or regional sentiment in terms of development relevant
indicators such as wellbeing, happiness, content, and security, and even fear, stress, urgency,
optimism, trust or anger, among others. This provides a much more fine-grained and updated
picture of the current state of development than the typical coarse-grained United Nations
Human Development Index (which consists of merely four broad indicators: life-expectancy,
adult literacy, school enrollment ratio, and Gross Domestic Product per capita, UNDP, 2014).
(v) The full name of Big Data is Big Data Analytics. The notion of Big Data goes far beyond the
increasingly quantity and quality of data, and focuses on analysis for intelligent decision-
making. Independent from the specific peta-, exa-, or zettabytes scale, the key feature of the
paradigmatic change is that analytic treatment of data is systematically placed at the forefront
of intelligent decision-making. The process can be seen as the natural next step in the evolution
from the “Information Age” and “Information Societies” (in the sense of Bell, 1973; Masuda,
1980; Beniger, 1986; Castells, 2009; Peres and Hilbert, 2010; ITU, 2014) to “Knowledge
Societies”: building on the digital infrastructure that led to vast increases in information, the
Big Data paradigm focuses on converting this digital information into knowledge that informs
intelligent decisions. Returning to the example of Google’s flu-trend (Ginsberg et al. 2009; Lazer
et al. 2014), Google processed an impressive 450 million different mathematical models in
order to eventually identify 45 search terms that could predict flu outbreaks better than
traditional models. In fact, several authors define Big Data in terms of the challenge to analyze
it (e.g. Chen et al., 2012; Chen and Zhang, 2014). The result of an extensive literature review on
Big Data definitions by de Mauro, et al. (2014) concluded that a consensual definition of Big
Data would be that “Big Data represents the Information assets characterized by such a High
Volume, Velocity and Variety to require specific Technology and Analytical Methods for its
transformation into Value” (p.8). The analytics part of big data is also the main obstacle for its
application: according to a survey of more than 3,000 managers from over 30 industries in 108
countries, the primary obstacle for big data adoption was lack of understanding of how to use
analytics to improve the business, which was cited four times more than concerns with data
quality or ineffective data governance” (LaValle et al., 2011).
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This fifths characteristic of Big Data has two main implications. For one, Big Data Analytics are
different from traditional statistical analysis because the quantity of data affects the choice of
the analytical model. Machine-learning and data-mining methods, which enable algorithms to
learn from data (Shalev-Shwartz and Ben-David, 2014), have often been belittled during the
1990s and early 2000s, but have proven their point forcefully during the 2010s, once they were
applied to vast amounts of data. It is not rarely the case that more sophisticated models work
better for smaller datasets (given the small scope of the dataset, the pattern has to be partially
coded in the model, increasing the complexity of the model), while quite simple (machine
detected) models work very well for larger datasets (often even better). The classic example is
that in text prediction tasks (such as Google’s autocomplete search entries) large memory-based
model work better with datasets under 1 million words, while simple Naïve Bayes machine
learning algorithms perform better with datasets between 1 and 1,000 million words (Banko and
Brill, 2001; Halevy et al. 2009). The amount of available data determines the choice of model.
Secondly, exploratory data mining and machine-learning methods are not guided by theory, and
do not provide any interpretation of the results. They simply detect patterns and correlations.
This is often referred to as “the end of theory” due to Big Data (Anderson, 2008). For example,
machines learned from Big Data that orange used cars have the best kept engines, that
passengers who preordered vegetarian meals usually make their flights, and that spikes in the
sale of prepaid phone cards can predict the location of impending massacres in the Congo (Hardy,
2012a). The reader is kindly invited to speculate about potential theories behind these
correlations, being aware that such speculations can often turn out wrong. For example,
complementary investigations showed that investments in prepaid phone cards in the Congo
were not caused by the planning or fleeing of the massacre, but that dollar denominated prepaid
cards were used as hedges against impending inflation arising from the anticipated chaos (Hardy,
2012a). This example shows another important point. While plain Big Data correlation analysis
does not automatically reveal the bigger picture of causational theories, at the same time, more
and better data provides the potential to detect spurious confounding variables and to isolate
potential causation mechanisms better than ever before (e.g. in this case by analyzing
complementary data on people movements and inflation trends).
2. Conceptual Framework BD4D
In order to be able to systematically review existing literature and related empirical evidence in
the field of Big Data for Development (BD4D), we employ an established three-dimensional
conceptual framework that models the process of digitization as an interplay between
technology, social change, and policy strategies. The framework comes from the ICT4D literature
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(Information and Communication Technology for Development) (Hilbert, 2012) and is based on
a Schumpeterian notion of social evolution through technological innovation (Schumpeter, 1939;
Freeman and Louca, 2002; Perez, 2004). Figure 1 adopts this framework to Big Data Analytics.
The first requisite for making Big Data Analytics work for development is a solid technological
(hardware) infrastructure, generic (software) services, and human capacities and skills. These
horizontal layers are the requirement sine qua none (see horizontal layers in Figure 1). They can
be unequally distributed, leading to a development divide. Once available, the horizontal layers
can be employed to analyze different aspects and kinds of data, such as words, locations, nature’s
elements, and human behavior, among others (see vertical layers in Figure 1). While this set-up
of technical requirements (horizontal) and social processes (vertical) is necessary for Big Data
Analytics, it is not sufficient for development. The rejection of technological determinism tells us
that all technologies (including ICT) can be used to both foster and deprive capabilities
(Kranzberg, 1986). Making Big Data work for development requires the social construction of its
usage through carefully designed policy strategies. How can we assure that cheap large-scale
data analysis creates better public and private goods and services, rather than leading to
increased State and corporate control? What needs to be considered to avoid that Big Data will
not add to the long list of failed technology transfer to developing countries? From a systems
theoretic perspective, public and private policy choices can broadly be categorized into two
groups: positive feedback (such as incentives that foster specific dynamics: putting oil into the
fire), and negative feedback (such as regulations, that curb particular dynamics: putting water
into the fire). These are the diagonal layers of Figure 1. The result is a three-dimensional
framework, whereas different circumstances (horizontal) and interventions (diagonal) intersect
and affect different applications of Big Data Analytics (vertical).
In the following section we review some examples of applications of Big Data for development
through the tracking of words, locations, nature’s elements, transactions, human behavior and
economic production.
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After illustrating some of these ends of Big Data, we look at the means in
the subsequent section, specifically at the international distribution of hardware and software
infrastructure and analytical skills. Last but not least, we review aspects and examples of
regulatory and incentive systems to make the Big Data paradigm work for development.
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While the traditional IT4D cube framework uses social sectors, such as business, education, health, etc. (Hilbert,
2012), this current choice underlines the different kinds of data sources. However, this is a question of choice, not
substance, and we might as well analyze Big Data practices according to social sectors of their application.
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Figure 1: The three-dimensional “ICT-for development-cube” framework applied to Big Data.
3. Application of Big Data for Development
From a macro-perspective, it is expected that Big Data informed decision-making adds to the
existing effects of digitization. Brynjolfsson, Hitt, and Kim (2011) found that U.S. firms that
adopted Big Data Analytics have output and productivity that is 5 6 % higher than what would
be expected given their other investments and information technology usage. McKinsey
(Manyika, et al., 2011) shows that this potential goes beyond data intensive economic sectors,
like banking, investment and manufacturing, and that several sectors with particular importance
for social development are quite data intensive: education, health, government, and
communication host one third of the data in U.S. in 2010. The following reviews some of the
micro-level examples that lead to such aggregated macro-level effects of Big Data, including
effects on employment, crime, water supply, mining, and health.
3.1 Tracking words
One of the most readily available and most structured Big Data source relates to words. The idea
is to analyze words in order to predict actions or activity. This logic is based on the old wisdom
ascribed to the mystic philosopher Lao Tse: “Watch your thoughts, they become words. Watch
your words, they become actions…”. Or to say it in more modern terms: You Are What You
Tweet” (Paul and Dredze, 2011). Figure 2a shows that the simple number of Google searches for
words
Infrastructure
Generic Services
Capacities &
Knowledge skills
locations
nature
behavior & activity
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the word “unemployment” in the U.S. correlates very closely with actual unemployment data
from the Bureau of Labor Statistics. The latter is based on a quite expensive sample of 60,000
households and comes with a time-lag of one month, while Google trends data is available for
free and in real-time (Hubbard, 2011; for a pioneering application see also Ettredge et al., 2005).
Using a similar logic, there are several additional examples of how search term analytics was able
to reveal trends in the Swine Flu epidemic roughly two weeks before the U.S. Center of Disease
Control (O'Reilly Radar, 2011), and dengue outbreaks (Althouse and Ng, 2011). Figure 2b
visualizes how Google search word trend data is able to make future predictions on the dengue
outbreaks at times when official statistics by the Brazilian Ministry of Health are still missing.
Figure 2: Real-time Prediction: (a) Google searches on unemployment vs. official government
statistics from the Bureau of Labor Statistics; (b) Google Brazil Dengue Activities
Source: Hubbard, 2011; http://www.hubbardresearch.com; Google correlate,
http://www.google.org/denguetrends/about/how.html
The prototypical Big Data source for words is social media. Pioneering applications used online
postings in blogs, media, and web pages to predict books sales (Gruhl et al, 2005). Kalampokis et
al. (2013) investigated 52 articles from 2005 to 2012 that used social media information to make
social predictions (see Figure 3). Social media status updates from Facebook and Twitter were
the most common source, followed by blogs, review- and discussion boards. The applications of
Big Data Analytics in this are range from swine flu pandemic (Ritterman et al., 2009), to the sales
of motor vehicle parts and travel patterns (Choi and Varian, 2012). Three out of four of these
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studies showed clear evidence that supports the predictive and explanatory power of social
media data for social phenomena (Kalampokis et al., 2013).
Figure 3: Classification of 52 Big Data social media studies by source and area of application.
Source: Kalampokis, Tambouris, & Tarabanis (2013).
One limitation to social media as a data source is the potential for differences between digital
and real world behavior. In a pure Goffmanian sense (Goffman, 1959), most of us tend to do
less self-censorship and editing on Facebook than in the profiles on dating sites, or in a job
interview. Others carefully curate their profile pictures to construct an image they want to
project” (Manovich, 2012). The long-standing statistical issues of representativeness, biases and
data-cleaning subjectivity apply to Big Data the same as to all kinds of traditional data analysis.
3.2 Tracking locations
The above-cited pervasiveness of mobile telephony has provided unprecedented insights into
human mobility. In fact, it has been shown that the analysis of mobile phone call records allow
to approach the limit of extractable predictability in human mobility, being able to predict up to
95 % of people’s movements in more stable situations (Song et al., 2010; Lu et al., 2013) and even
85 % in chaotic situations, such after an earthquake (Lu et al., 2012). Geographic mobile phone
records from rural Kenya have been used to provide detailed travel and migration patterns in
low-income settings to understand the spread of malaria (Buckee et al., 2013) and infectious
diseases (Wesolowski et al., 2014); to understand population movements following an
earthquake and cholera outbreak (Bengtsson et al., 2011;, Lu et al., 2012); to study social
responses to urban earthquakes in Mexico (Moumni et al., 2013); and to obtain insights into
charity and reciprocal aid among peers in Rwanda after the strike of a natural disaster
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(Blumenstock et al., 2012). Telecom companies already sell the service of mobility analytics
obtained from mobile phones to business clients, who purchase them to gain insights into
consumer behavior in real-time (Telefonica, 2012).
While the geographical area covered by a triangulation of mobile phone base transceiver stations
ranges between 1 and 3 square km (depending on their urban or rural location), some 20-30 %
of mobile phones already have geo-located GSP capability, a fast growing trend (Manyika, et al.,
2011). Location-based services can provide much more detailed location data. In Stockholm, for
example, a fleet of 2,000 GPS-equipped vehicles, consisting of taxis and trucks, provide data in
30 - 60 seconds intervals to create a real-time picture of the current traffic situation (Biem, et al.,
2010). The system can successfully predict future traffic conditions, based on matching current
traffic and weather data to historical records. This not only saves time and gasoline, but is also
useful to constantly optimize public transportation, and the work of fire and police departments.
Police work and crime prediction is another important area of application (e.g. Toole, Eagle &
Plotkin, 2011). Chicago Crime and Crimespotting in Oakland present interactive mapping
environments that allow users to track instances of crime and police activity in their
neighborhood. Big data sources such as historical crime record, geospatial and demographic data
can be complemented with real-time social media data, such as from Twitter (Wang et al., 2012).
Adequate algorithms and visualization tools for developing countries are currently being
developed (Isafiade and Bagula, 2013).
3.3 Tracking nature
One of the biggest sources of uncertainty is nature. Reducing this uncertainty through data
analysis can (i) optimize performance, (ii) mitigate risk, and (ii) improve emergency response.
(i) The attenuation from radio signals when rain falls between cellular towers has been used as a
big data source to measure the amount of rain that falls in an area, providing crucial
information to farmers and water resource managers (Overeem et al., 2013). Analyzing rainfall
levels, temperatures, and the number of hours of sunshine, a global beverage company was
able cut its beverage inventory levels by about 5 % (Brown, Chui, and Manyika, 2011, p. 9).
Relatively cheap standard statistical software was used by several bakeries to discover that the
demand for cake grows with rain and the demand for salty goods with temperature. Cost
savings of up to 20 % have been reported as a result of fine-tuning supply and demand
(Christensen, 2012). This can make the difference between the survival of a small enterprise
and its failure.
(ii) Remote sensing have been used as early as in the late 1970s to acquire statistics on crops in
developing countries and to locate petroleum and mineral deposits (Paul and Mascarenhas,
1981). Nowadays robotic sensors monitor water quality and supply of river and estuary
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ecosystems through the movement of chemical constituents and large volumes of underwater
acoustic data that tracks the behavior of animals (IBM News, May 2009), which is the case for
the 315-mile New York's Hudson River (IBM News, 2007). Similarly, the provision and analysis
of data from climate scientists, local governments and communities is fused to reduce the
impact of natural disasters by empowering decisions-makers in 25 countries with better
information on where and how to build safer schools, insure farmers against drought, and
protect coastal cities (GFDRR, 2012). Large datasets on weather information, satellite images,
and moon and tidal phases have been used to place and optimize the operation of wind
turbines, estimating wind flow pattern on a grid of about 10x10 meters (32x32 feet) (IBM,
2011).
(iii) During wildfires, public authorities worldwide have started to analyze smoke patterns via real
time live videos and pictorial feeds from satellite, unmanned surveillance vehicles, and
specialized tasks sensors (IBM News, Nov. 2009). Similarly, in preparation for the 2014 World
Cup and the 2016 Olympics, the city of Rio de Janeiro created high-resolution weather
forecasting and hydrological modeling system which gives city official the ability to predict
floods and mud slides. It has improved emergency response time by 30 % (IBMSocialMedia,
2012).
3.4 Tracking transactions
Digital transactions are omnipresent footprints of social interaction (Helbing and Balietti, 2010).
Analytics of sales transactions are among the most the most pervasive Big Data applications
(Gruhl et al, 2005; Mayer-Schönberger and Cukier, 2013). This can go beyond the maximization
of commercial profit. Grocery and over-the-counter medication sales have been used to detect a
large-scale but localized terrorism attack, such the U.S. anthrax attacks of the early 2000s
(Goldenberg, et al., 2002). Besides, given that some 95 % of the mobile phones in developing
countries are prepaid (Naef, E. et al., 2014) and given that people put economic priority on
recharging their phone, even under economic constraints (Hilbert, 2010), tracking the level of
mobile phone recharging can provide a cheap source to measure poverty levels in real time on a
fine-grained geographic level (Letouzé, 2012).
Historic transaction data can also be used to confront systematic abuse of social conventions.
Half a century of game theory has shown that social defectors are among the most disastrous
drivers of social inefficiency. A costly and often inefficient overhead is traditionally added to
social transactions in order to mitigate the risk of defectors. Game theory also teaches us that
social systems with memory of the past and predictive power of future behavior can circumvent
such inefficiency (Axelrod, 1984). Big Data can provide such memory and is already used to
provide short-term payday loans that are up to 50 % cheaper than the industry’s average. Default
14
risk is judged via Big Data sources like cellphone bills and the click-stream generated while
applicants read the loan application Website (Hardy, 2012b).
3.5 Tracking behavior
Given the flood of behavioral Big Data, it is easy to define a whole new range of “abnormal
behavior” (defined by variances around the “average collective behavior”). As an example from
the health sector, Figure 4a presents the hospitalization rates for forearm- and hip-fractures
across the U.S. (Darthmouth, 2012). While standard deviations of hip-fractures are within
expected ranges, forearm fracture hospitalization rates are 9 times larger (30 % of the regions
with extreme values). Complementary investigations point to four general types of variations:
(i) Environmental conditions: Figure 4b shows that variations in Medicare spending are not
reduced when adjusting for differences in illness patterns, demographics (age, sex, race), and
regional prices.
(ii) Medical errors: some regions systematically neglect preventive measures, and others have an
above average rate of mistakes.
(iii) Biased judgment: the need for costly surgery is often unclear, and systematic decision-making
biases are common (Wennberg, et al., 2007).
(iv) Overuse and oversupply: The number of prescribed procedures does not correlate with
health outcomes, but with resource availability to prescribe procedures: more health care
spending does not correlate with mortality (R^2 = 0.01), nor with underuse of preventive
measures (R^2 = 0.01), but does correlate with additional days in hospital (R^2 = 0.28); more
surgeries during last 6 years of life (R^2 = 0.35); and more visits to medical specialists (R^2 =
0.46) and with the availability of ten or more physicians (R^2 = 0.43) (Darthmouth, 2012).
With Big Data, a simple analysis of variations allows to detect “unwarranted variations” like (ii-
iv), which originate with the underuse, overuse, or misuse of medical care (Wennberg, 2011).
Behavioral data can also be produced by digital applications. Examples of behavioral data
generating solutions are online games like World of Warcraft (11 million players in 2011) and
FarmVille (65 million users in 2011). Students of the data produced by multi-player online games
cannot only predict who is likely to leave the game and why (Borbora, Srivastava, Hsu and
Williams, 2012), but also psychological well-being (Shen and Williams, 2011) and educational
outcomes (Ritterfeld et al., 2009). Video games are not only used to track, but also to influence
behavior. Health insurance companies developed multiplayer online games to increase their
client’s fitness. Such games are fed with data from insurance claims and medical records, and
combine it with real-time behavioral data from the virtual world (Petrovay, 2012). Health points
15
can be earned by checking into the gym, ordering a healthy lunch or regularly taking prescribed
medicine.
Figure 4: (a) Patterns of variations in the hospitalization for forearm and hip-fracture across
U.S.; (b) Patterns of Medicare Spending among regions in the U.S.
Source: Darthmouth, 2012; http://www.dartmouthatlas.org
3.6 Tracking production
A contentious area of Big Data for development is the reporting of economic production that
could potentially reveal competitiveness advantages. An illustrative case is natural resource
extraction, which is a vast source of income for many developing countries (reaching from mining
in South America to drilling in North Africa and the Middle East), yet have been a mixed blessing
for many developing countries (often being accompanied by autocracy, corruption, property
expropriation, labor rights abuses, and environmental pollution). The datasets processed by
resource extraction entities are enormously rich. A series of recent case studies from Brazil,
China, India, Mexico, Russia, the Philippines and South Africa have argued that the publication
and analysis of data that relate to the economic activity of these sectors could help to remedy
the involved downsides, without endangering the economic competitiveness of those sectors in
developing countries (Aguilar Sánchez, 2012; Tan-Mullins, 2012; Dutta, Sreedhar and Ghosh,
2012; Moreno, 2012; Gorre, Magulgad and Ramos, 2012; Belyi and Greene, 2012; Hughes, 2012).
As of now, this interpretation is not in the mainstream. Figure 5 shows that the national rent that
is generated from the extraction of the natural resource (revenue less cost, as percentage of GDP)
negatively relates to the level of government disclosure of data on the economic production in
oil, gas and mineral industries.
16
Figure 5: Public data on natural resource extraction: Natural resource rent vs. government
data disclosure (year=2010; n=40).
Source: own elaboration, based on Revenue Watch Institute and Transparency International,
2010; and World Bank, 2010. Note: The Revenue Watch Index is based on a questionnaire that
evaluates whether a document, regular publication or online database provides the information
demanded by the standards of the Extractive Industry Transparency Initiative (EITI), the global
Publish What You Pay (PWYP) civil society movement, and the IMF’s Guide on Revenue
Transparency (www.revenuewatch.org/rwindex2010/methodology.html).
3.7 Tracking other data
As indicated by the right-side arrows in the conceptual framework of Figure 1, these are merely
illustrative examples. Additional data sources include the tracking of financial-, economic or
natural resources, education attendance and grades, waste and exhaust, expenditures and
investments, among many others. Future ambitions for what and how much to measure diverge.
Hardy (2012c, p. 4) reports of a data professional who assures that “for sure, we want the correct
name and location of every gas station on the globe not the price changes at every station”;
while his colleague interjects: “Wait a minute, I’d like to know every gallon of gasoline that flows
around the world … That might take us 20 years, but it would be interesting”.
17
4. Digital Big Data Divide
Having reviewed some illustrative social ends of Big Data, let us assess the technological means
(the “horizontal layers” in Figure 1). The well-known digital divide (Hilbert, 2011b) also
perpetuates the era of Big Data.
4.1 Infrastructure access
4.1.1 Challenges
ICT access inequality affects Big Data in two ways. One concerns skewed data representativeness
stemming from unequal access, the other unequal access to Big Data.
(i) Big Data is still based on samples. While it is the ambition of Big Data to dispense with the
need of random sampling techniques by collecting ‘everything there is’, students of the digital
divide are very aware that the online world is only a subsample of everything there is. “Twitter
does not represent ‘all people’, and it is an error to assume ‘people’ and ‘Twitter users’ are
synonymous: they are a very particular sub-set” (boyd and Crawford, 2012; p. 669). The intensity
of the bias is dictated by the intensity of the digital divide. Blumenstock and Eagle (2012) showed
that with low penetration rates (such as in Rwanda in 2005-2009, a period during which mobile
phone penetration rose from merely 2 % to 20 %), phone users are disproportionately male,
better educated, and older(p.2). Figure 6a shows that the mobile phone population is quite
distinct from the general population, being biased toward the privileged strata. Frias-Martinez
and Virseda (2013) used mobile phone data from a more advanced “emerging economy in from
Latin America” with a mobile phone penetration of around 60-80% at the time of the study.
Figure 6b shows that the Big Data sample matches official census data impressively well.
18
Figure 6: Representativeness of Big Data: comparison of mobile phone subscribers and
population at large. (a) Rwanda 2005/09, with mobile phone penetration of 2-20%; (b) Latin
American economy 2009/10, with mobile phone penetration of 60-80%;.
Source: (a) Blumenstock and Eagle (2012); (b) Frias-Martinez and Virseda (2013). Note: in (b)
income, the larger extreme values (segments A/B and E) are an artifact caused by the employed
mapping methodology of Frias-Martinez and Virseda (2013).
(ii) Continuously unequal access. Economic incentives inherent to the information economy,
such as economies of scale in information storage and short product lifecycles (Shapiro and
Varian, 1998), increasingly concentrate information and computational infrastructure in the
“cloud”. While in 1986, the 20 % of the world’s largest storage technologies were able to hold
75% of society’s technologically stored information, this share grew to 93 % by 2007. The
domination of the top-20 % of the world’s general-purpose computers grew from 65 % in 1986,
to 94 % two decades later (Hilbert, 2014a). Figure 7 shows this increasing concentration of
technological capacity among an ever smaller number of ever more powerful devices in form of
the Gini (1921) measure. Naturally, the vast majority of this Big Data hardware capacity resides
in highly developed countries.
19
Figure 7: Gini measure of the world’s number of storage and computational devices, and their
technological capacity (in optimally compressed MB, and MIPS), 1986 and 2007 (Gini = 1 means
total concentration with all capacity at one single device; Gini = 0 means total uniformity, with
equally powerful devices).
Source: own elaboration, based on Hilbert (2014a).
The fundamental condition to convert this increasingly concentrated information capacity among
storage and computational devices (“the cloud”) into an equalitarian information capacity among
and within societies lies in the social ownership of telecommunication access.
Telecommunication networks provide a potential technological gateway to the Big Data cloud.
Figure 8 shows that this basic condition is ever less fulfilled. Over the past two decades, telecom
access has ever become more diversified. In the analog age of 1986, the vast majority of telecom
subscriptions were fixed-line phones, and all of them had the same performance. This resulted
in a quite linear relation between the number of subscriptions and the average traffic capacity
(see Figure 8). Twenty five years later, there’s a myriad of different telecom subscriptions with
the most diverse range of performances. Not only are telecom subscriptions heterogeneously
distributed among societies, but the varied communicational performance of those channels has
led to an unprecedented diversity in telecom access. Far from being closed, the digital divide
incessantly evolves through an ever changing heterogeneous collection of telecom bandwidth
capacities (Hilbert, 2014c).
4.1.2 Options
One way for developing countries to confront this challenge is to create local hardware capacity
by exploiting the decentralized and modular approach inherent to many Big Data solutions.
Hadoop, for example, is prominent open-source top-level Apache data-mining warehouse, with
a thriving community (Big Data industry leaders, such as IBM and Oracle embrace Hadoop). It is
built on top of a distributed clustered file system that can take the data from thousands of
20
distributed (also cheap low-end) PC and server hard disks and analyze them in 64 MB blocks,
which allows it to grow with demand while remaining economical at every size(Shvachko et
a;., 2010). With respect to cost-effective distributed computational power, clusters of videogame
consoles are frequently used as a substitute for supercomputers in Big Data Analytics (e.g.
Gardiner, 2007; Dillow, 2010). Some 500 PlayStation 3 consoles amount to the average
performance of a supercomputer in 2007, which makes this alternative quite price competitive
(López and Hilbert, 2012).
Figure 8: Subscriptions per capita vs. Capacity per capita (in optimally compressed kbps of
installed capacity) for 1986 and 2010. Size of the bubbles represents Gross National Income
(GNI) per capita (N = 100).
Source: own elaboration, based on Hilbert (2014b).
4.2 Generic services
Additional to the tangible hardware infrastructure, Big Data relies heavily on software services to
analyze the data. This includes both financial and human resources.
4.2.1 Challenges
Figure 9 shows the shares of software and computer service spending of total ICT spending
(horizontal x-axis) and of software and computer service employees of total employees (vertical
y-axis) for 42 countries. The size of the bubbles indicates total ICT spending per capita (a basic
indicator for ICT advancement). Larger bubbles are related to both, more software specialists and
more software spending. In other words, those countries that are already behind in terms of ICT
spending in absolute terms (including hardware infrastructure), have even less capabilities for
software and computer services in relative terms. Envisioning Big Data capabilities in every
enterprise, organization and institution of a country, illustrates that it makes a critical difference
21
if 1 in 50 or 1 in 500 of the national workforce is specialized in software and computer services
(see Finland vs. Mexico in Figure 9), especially when trying to adopt and fine-tune technological
solutions to domestic requirements in developing countries (Romijn and Caniëls, 2011).
Figure 9: Spending (horizontal x-axis) and employees (vertical y-axis) of software and
computer services (as % of respective total). Size of bubbles represents total ICT spending per
capita (n=42 countries).
Source: own elaboration, based on UNCTAD, 2012.
4.2.2 Options
There are two basic options on how to obtain Big Data services: in-house or outsourcing. Many
organizations opt for a hybrid solution and use on-demand cloud resources to supplement in-
house Big Data deployments (Dumbill, 2012), as in-house solutions are alone are notoriously
costly (examples coming from large firms like Tesco, Target, Amazon or Wal-Mart). Outsourcing
solutions benefit from the extremely high fix-costs and minimal variable costs of data (Shapiro
and Varian, 1998): it might cost millions of dollars to create a database, but running different
kinds of analysis is comparatively cheap. This economic incentive leads to an increasing
agglomeration of digital data capacities in the hands of specialized data service provider (among
the largest being Acxiom, Experian, Epsilon, and InfoUSA). They offer historic voting behavior of
politicians, evaluations of customer comments on social ratings sites like Yelp, insights obtained
from Twitter and Facebook, on-demand global trade and logistics data, and information about
traffic patterns and customer mobility (Hardy, 2012b, 2012c). Given their continuous borderline
22
dance with the limits of the law and moral practice, they came under the scrutiny of policy makers
(U.S. Senate, 2013). In one emblematic case, a Big Data Analytics provider classified the business
attitude of millions of elderly Americans into groups like “Elderly Opportunity Seekers: looking
for ways to make money, “Suffering Seniors: cancer or Alzheimer”, and “Oldies but Goodies:
gullible, want to believe that their luck can change”, and sold it to known lawbreakers who then
emptied several savings accounts (Duhigg, 2007). Such obvious crimes are only the pique of an
increasing ice-berg of potential discrimination due to Big Data transparency (ranging from
personal credit ratings to school acceptance).
Given the commoditization of data, data also becomes subject to existing economic divides. With
a global revenue of an estimated US$ 5 -10 billion in 2012/2013 (Feinleib, 2012), the Big Data
market has already become bigger than the size of half of the world’s national economies in its
first years. Creating an in-house capacity or buying the privilege of access for a fee “produces
considerable unevenness in the system: those with money or those inside the company can
produce a different type of research than those outside. Those without access can neither
reproduce nor evaluate the methodological claims of those who have privileged access” (boyd
and Crawford, 2012; p. 673-674). In the words of fifteen leading scholars in the field:
Computational social science is occurringin Internet companies such as Google and Yahoo,
and in government agencies such as the U.S. National Security Agency. Computational social
science could become the exclusive domain of private companies and government agencies.
Alternatively, there might emerge a privileged set of academic researchers presiding over private
data… Neither scenario will serve the long-term public interest” (Lazer et al., 2009). The existing
unevenness in terms of economic resources leads to an uneven playing field in this new analytic
divide. Relevant policy options include financial incentives and open data policies, as discussed
in the chapter of Policy and Strategy.
4.3 Capacities and skills
4.3.1 Challenges
Case studies on the use of Big Data applications in development project show that adequate
training for data specialists and managers is one of the main reasons for failure
(Noormohammad, et al., 2010). Manyika, et al. (2011) predict that by 2018, even the job magnet
United States will face a shortage of some 160,000 professionals with deep analytical skills (of a
total of 450,000 in demand), as well as a shortage of 1.5 million data managers that are able to
make informed decisions based on analytic findings (of a total of 4 million in demand). This shows
that there is a global shortage, with a disproportional negative effect on developing countries
and the public sector disproportionally (Borne, 2013; Freedman Consulting, 2013). Figure 10
shows that perspectives in this regard are actually mixed for different parts of the developing
world. Some countries with relatively low income levels achieve extremely high graduation rates
23
for professionals with deep analytical skills (high up on the vertical y-axis in Figure 8). In general,
countries from the former Soviet bloc (e.g. Romania, Poland, Latvia, Lithuania, and Bulgaria)
produce a high level of analysts. The world’s large developing BRIC countries (Brazil, Russia, India
and China) produce 40 % of the global professionals with deep analytical skills, twice and many
as the university power-hose of the United States (far to the right on the x-axis in Figure 10).
Traditional leaders of the global economy, such as Germany and Japan, are comparatively ill-
equipped to satisfy domestic demand with internal sources. This leads to the long-standing and
persistent discussion about brain drain and possible brain circulation in a global diaspora of data-
savvy managers, analysts, statisticians, and computer scientists.
Figure 10: Graduates with deep analytical training: total (horizontal x-axis), per 100 people
(vertical y-axis), Gross National Income (GNI) (size of bubbles).
Source: own elaboration, based on Manyika, et al., 2011 and World Bank, 2010. Note: Counts
people taking graduate or final-year undergraduate courses in statistics or machine learning (a
subspecialty of computer science).
It is not only the quantity, but also the quality of analytical skills that matters. An inventory of 52
social media studies from 2005-2012 revealed that more than one third of the exercises that
claimed to prove the predictive power of social media data did not even run any explicit
predictive analytics (but mere explanatory statistics, such as R^2 correlation analysis)
(Kalampokis et al., 2013). Considering this systematic misuse of statistical techniques in the social
sciences, one can only speculate about the quality of much of the Big Data research done in small
enterprises, public administrations, and social institutions.
24
4.3.2 Options
Most policy implications in this challenge are similar to the well-studied implications of science
and technology education and the notorious brain-drain / brain-gain from previous technological
revolutions (e.g. Saxenian, 2007). One innovative way of dealing with the shortage of skilled
professionals are collective data analysis schemes, either through collaboration or competition.
Even the most advanced users of Big Data Analytics recur to such schemes: a survey of leading
scientists suggests that only one quarter of scientists have the necessary skills to analyze available
data, while one third said they could obtain the skills through collaboration (Science Staff, 2011).
Collaborative setups include Wikis to collectively decode genes or analyze molecular structures
(Waldrop, 2008), and aid in the classification of galaxies GalaxyZoo (galaxyzoo.org) and complex
protein-folding problems (folding.stanford.edu). The alternative to collaboration is competition.
During 2010-2011 the platform Kaggle attracted over 23,000 data scientists worldwide in data
analysis competitions with cash prizes between US$ 150 and US$ 3,000,000 (Carpenter, 2011).
While the Netflix Prize of US$ 1,000,000 is the much-cited epitome of such analytics competitions
(Bell et al., 2010), a more common example includes 57 teams (among others from Chile, Antigua
and Barbuda, and Serbia) who helped an Australian to predict the amount of money spend by
tourists in a specific area (a valuable insight for a mere US$ 500 cash price) (Hyndman, 2010).
5. Policy and Strategy
No technology, including Big Data, is inherently good or bad for development (Kranzberg, 1986).
The maximization of opportunities and the minimization of risks is a process of proactive social
construction, with its main societal tools being public policies and private strategies. In a
development context, this starts with a certain awareness about the importance of data analytics
and the urgency to undertake required adjustments. The magnitude of the challenge becomes
clear when considering that Ghana’s statistical authorities took 17 years to adopt the UN system
of national accounts from 1993. In 2010 the surprised statisticians found that Ghana’s GDP was
62 % higher than previously thought (Devarajan, 2011). It also implies considering that the
ongoing transition does not occur in a vacuum, but within existing societal structures, which can
result in reverse effects of well-intended efforts. Emblematic is the case of the digitization of
twenty million land records in Bangalore, which created a big data source aimed at benefiting 7
million small farmers from over 27,000 villages (Chawla and Bhatnagar, 2004). Contrary to
expectations, the usual dominant players were in a much better position to exploit the provided
data, resulting in a perpetuation of existing inequalities (Benjamin et al., 2007).
5.1 Incentives: positive feedback
One kind of intervention consists in positively encouraging and fostering desired outcomes. Two
of the most common ways consists in providing funds for data and in providing data itself.
25
(i) Financial incentives and subsidies. As so often, money is not the sole solution, but it makes
things easier. Two examples from the U.S. include the earmarked US$ 19 billion of the American
Recovery and Reinvestment Act to encourage U.S. physicians to adopt electronic medical
recordkeeping systems (Bollier, 2010), and the US$ 700-800 million subsidies of the Office of
Cyberinfrastructure (OCI) of the U.S. National Science Foundation (NSF), to be partially invested
into large-scale data repositories and digitized scientific data management systems(NSF,
2012). Part of the ambition to bring Big Data to the general public consists in investments into
data visualization (Frankel and Reid, 2008) (see Figure 11 for a simple example). NSF and the
Journal Science have invested into data visualization competitions for over ten consecutive
years (Norman, 2012; Science Staff, 2014).
Figure 11: Word cloud of this article: one simple and quick way to visualize Big Data.
Source: The full text of this paper; world cloud created using www.Wordle.net ; i.e.
http://www.wordle.net/show/wrdl/8112509/BigData4D_article
In developing countries, small financial incentives can already make a large difference. Eagle
(2013) reports that a decentralized short-message-system (SMS) to collect blood bank
inventory data from Kenyan hospitals initially failed because it did not consider the the cost of
SMS-texting (initially covered by individual nurses). By adjusting the billing system of mobile
phone operators, a 1 cent subsidy was given for each SMS text message received, summing up
to a total cost of USD 240 to maintain this life-saving service for 24 Kenyan hospitals.
(ii) Open data: Another incentive for Big Data consists in providing open data to the public. This
approach sees data as a public good. For example, geospatial data
7
(Vincey, 2012) and weather
and climate data (GFDRR, 2012) are among the most widely published public data. The ongoing
7
Geospatial data represents 37 % of the datasets of the U.S. open data initiative (Vincey, 2012).
26
discussion about the openness of digital data moves along different dimensions (Figure 12a). It
includes the use of technical standards of the provided data (e.g. impractical PDF files vs.
structured Excel spreadsheets vs. machine-readable “linked data”, Berners-Lee, 2006), its
accessibility through the web, and legal questions like the copyright and copyleft standards
(Abella, 2014). The case of Spain shows that different kinds of data tend to be ‘more open’ with
regard to one dimension or the other (Figure 12a). Data held and produced by the natural quasi-
monopoly of the public sector is a natural place to push for the public provision of data, a
discussion which often runs under the heading of “open governments” (Lathrop and Ruma,
2010; Kum, Ahalt and Carsey, 2011; Concha and Naser, 2012; WEF and Vital Wave, 2012). It is
estimated that each organization of the U.S. government is estimated to host some 1.3
Petabytes of data, compared with a national organizational mean of 0.7 PB, while the
government itself hosts around 12 % of the nationally stored data, and the public sector related
sectors of education, health care and transportation another 13 % (Manyika, et al., 2011). In
other words, if data from the public sector would be openly available, around one quarter of
existent data resources could be liberated for Big Data Analytics. While government
administrators often do not feel pressure to exploit the data they have available (Brown, Chui,
and Manyika, 2011), several initiatives have pushed governments around the world to “commit
to pro-actively provide high-value information, including raw data, in a timely manner, in
formats that the public can easily locate, understand and use, and in formats that facilitate
reuse (Open Government Partnership, 2014)
8
. Portals like datos.gob.cl in Chile,
bahrain.bh/wps/portal/data in Bahrain, or www.opendata.go.ke in Kenya to provide hundreds
of datasets on demographics, public expenditures, and natural resources for public access. Also
international organization, like the World Bank (data.worldbank.org), regional governments,
like Pernambuco in Brazil (dadosabertos.pe.gov.br) or local governments, like Buenos Aires in
Argentina (data.buenosaires.gob.ar) provide databases about education, housing, highway
conditions, and the location of public bicycle stands. The potential for development is
illustrated by the fact that the existence of an open data policy does not seem to correlated
strongly with the level of economic well-being and perceived transparency of a country (Figure
12b). Several low income countries are more active than their developed counterparts in
making databases publicly available (see e.g. Kenya, Russia and Brazil), while other countries
with traditionally high perceived transparency are more hesitant (e.g. Chile, Belgium, Sweden).
8
By mid-2014, some 64 countries have signed the Open Government Declaration from which the quote is taken.
27
Figure 12: Open data: (a) Classification of type of public sector information information in Spain
along three open data dimensions; (b) Number of datasets provided on central government
portal (vertical y-axis, logarithmic scale), Gross National Income per capita (horizontal x-axis),
Corruption Perception Index (size of bubbles: larger, more transparent) (year=2011; n=27).
Source: own elaboration, based on (a) Abella, 2014; (b) 27 official open data portals; Revenue
Watch Institute and Transparency International, 2011; and World Bank, 2010. Note: The
Corruption Perception Index combines the subjective estimates collected by a variety of
independent institutions about the perceived level of transparency and corruption in a country
(since corruption is an illegal, subjective perceptions turn out to be the most reliable method)
4.2 Regulation: negative feedback
The other option to guide the Big Data paradigm into the desired development direction consists
in the creation of regulations and legislative frameworks.
(i) Control and privacy. Concerns about privacy and State and corporate control through data are
as old as electronic database management. Fingerprinting for the incarcerated, psychological
screening for draft inductees, and income tax control for working people were among the first
databases to be implemented in the U.S. before the 1920 (Beniger, 1986) and inspired novelists
like Huxley (1932) and Orwell (1948).
9
Big Data has taken this issue to a new level. To say it in
the words of the editor of the influential computer science Journal Communications of the
ACM”: “NSA [U.S. National Security Agency], indeed, is certainly making Orwell’s surveillance
9
“By comparison with that existing today, all the tyrannies of the past were half-hearted and inefficient” (Orwell,
1948; 2, 9).
28
technology seem rather primitive in comparison” (Vardi, 2013; p.5). Fact of the matter is that
digital information always leaves a potential trace that can be tracked and analyzed (Andrews,
2012) and that “any data on human subjects inevitably raise privacy issues (Nature Editorial,
2007; p. 637). One common distinction is whether or not the tracked data is generated actively
or passively, and voluntarily or involuntarily (King, 2011), leading to a 2x2 matrix. Examples
include the passive but voluntary data provision to online retailers and search engines, or the
passive and involuntary data provision through mobile phone locations (Andrews, 2012). Active
data provision occurs via online user ratings, Facebook posts, or tweets, etc.
With regard to regulation of one or the other kind of data, a 2014 White House report suggest
to basically give up on the futile attempt to regulate which data may and may not be collected:
Policy attention should focus more on the actual uses of big data and less on its collection and
analysis. By actual uses, we mean the specific events where something happens that can cause
an adverse consequence or harm to an individual or class of individuals” (White House, 2014a;
p.xiii). Those adverse consequences would then be severely punished (such as in the above-
cited example of discrimination and criminal acts against elderly (see Duhigg, 2007)). This is in
line the legal philosophy of not regulating the possession of something, but their illegal usage.
For example, the case of fire arms in the United States follows this legal philosophy. Besides
foregoing practical challenges in the regulation of possession, the advantage of permitting
possession consists in assuring not to curb any of the potential benefits of its usage. This well-
known discussion about benefits, dangers and practicality is currently being held with regard
to Big Data.
The currently existing legal grey zone is exemplified by the fact that several Big Data provider
have opted to obtain the assurance of the customer not to abuse the provided data. For
example, the company Instant Checkmate provides information on individuals drawn from
criminal records, phone and address registries, professional and business licenses, voter
registration, marriage records, demographic surveys and census data, etc. Figure 13 shows that
before obtaining the purchased report, the consumer has to click to consent that the
information will not be used to “make decisions about consumer credit, employment,
insurance, tenant screening” and not to “spread gossip” or “harass people whose criminal
records appear on this site” (Figure 13). It seems difficult to control for such commitments
without more binding legal rules or control. At the same time, the self-reported data abuses of
the NSA (U.S. Government, 2012) demonstrated that even when rules and regulations are in
place (incomplete as they might be), the benefits of breaking those rules are so tempting that
governments have admittedly undertaken illegal steps to take advantage of it. These issues are
much less regulated in developing countries, be it in academia, the private sector or
government agencies.
29
Figure 13: Checkmate: screenshot of consumer consent to obey privacy norms.
Source: http://www.instantcheckmate.com .
(ii) Regulating openness. Open government data does not need to rely on voluntary and
incentivizing projects, but can also be prescribed by law. So-called freedom of information
legislation aims at the principle that all documents and archives of public bodies are freely
accessible by each citizen, and that denial of access has to be justified by the public body and
classified as an exception, not the rule. As of 2012, roughly 70 countries passed such legislation
(FOI, 2012). Additionally, many developing countries have adopted transparency laws
(Michener, 2009). In theory, such legislation should be the ideal catalyzer for the provision of
open government data. The devil in this matter is in the implementation. Figure 14a shows that
at least four countries in Latin America count with such legislation, but not with any active
authority or collaboration that pushes the implementation of open data forward. On the
contrary, Costa Rica and Colombia do not count with such legislation, but with concrete
institutional projects. Figure 14b illustrates that the pinpointed assignation of a leading
authority can accelerate the implementation of open government directives. The specialized
Ministry of Modernization of the city government of Buenos Aires has prepared and published
the lion’s share of the available databases (18 of 67), while it then provides them to other
authorities which present them on their webpages.
30
Figure 14: Open data theory and praxis: (a) Existence of open data legislation and
implementation in Latin America; (b) Number of datasets prepared and presented by
government authorities in Buenos Aires.
Source: own elaboration, based on (a) Jadue (2012); (b) Belbis (2012).
(iii) Interoperability. While one of the main opportunities of Big Data is data fusion (see above),
bringing data from different sources together is also one of its main challenges. Large parts of
valuable data lurk in data silos of different departments, regional offices, and specialized
agencies. Manyika, et al. (2011) show that the data landscape in development relevant sectors
like education and health tends to be more fragmented than those of banking or insurance
services, whose databases are standardized. The regulation of data interoperability standards
has become a pressing issue for the Big Data paradigm in both developed (NSF, 2007), as well
as in developing countries (UN-ECLAC, 2007; de la Fuente, 2012).
6. Critical Reflection: all power to the algorithms?
In the past, the vast majority of information processing was executed by managers, analysts, and
human data crunchers (Nelson, 2008)
10
. This has changed, as human evaluators have been
overtaken by machines in many fields. Only a decade ago we would have still be surprised if
somebody would have told us that artificial intelligence diagnostics outperform human
aneurysms radiologists with a success rate of 95% versus 70 % (Raihan, 2010). We got used to it
10
In 1901, William Elkin expressed a view typical of the time when referring to women as measurers and
computers(Nelson, 2008; p. 36)
31
quickly. When fostering this kind of approach, we inevitably give a lot of power to algorithms
(Baker, 2008). Per definition, algorithms can only execute processes that are programmed into
them on the basis of data that is given to them. Unfortunately, both the programming of
algorithms and the nature of the data have short-comings.
First, the programmer rarely is able to consider all the intricate complexities of an environment
that consists of a large number of interdependent parts that pursue different goals. While some
of the results of imperfect algorithms are rather amusing (such as a book on flies that was offered
for US$ 23 million on Amazon.com by competing algorithms that forecasted supply and demand
patterns, Slavin, 2011), others can have disastrous consequences that affect the stability of entire
economies. A well-known example is “black-box” trading (or algorithmic trading) (Steiner, 2012).
Almost non-existent in the mid-1990s, algorithmic trading was responsible for as much as 70-75
% of trading volume in the U.S. in 2009 (Hendershott, Jones and Menkveld, 2011) and has
triggered several unreasonable sell-offs at stock markets (triggering a so-called “flash-crash”)
(Kirilenko, et al., 2011, Steiner, 2012).
Second, all statistics are informed by data, which is inevitably from the past (in the best case, the
‘real-time past’, which turns from present to past through the recording processes). As such,
future predictions based on past data work fine as long as the future and the past follow a similar
logic. If significant changes occur in the modus operandi of the system, past data does not reflect
the future. Development policies aim at creating changes with the purpose to create a future
that is different from the past. This limits the insights obtained from Big Data (Hilbert, 2014d;
2014e). To predict a future that has never been, theory-driven models are necessary. These allow
variables to be adjusted with values that have never existed in statistically observable reality. As
discussed in the introduction, data mining and machine-learning methods do not aim at providing
such theories, they simply predict. The flood of data has made this an explicit strategy in
resource-scarce developing countries, like Chile: “here we don’t have the luxury to look for
people who ask why things happen, we need people who detect patterns, without asking why”
(Abreu, 2013). Since explaining and predicting are notoriously different (Simon, 2002; Shmueli,
2010), blind prediction algorithms can disgracefully fail if the environment changes (Lazer et al.,
2014), since the insights are based on the past, not on a general understanding of the overall
context.
This underlines the importance of creating more flexible models that allow to explore theoretical
scenarios that never existed before and therefore have no empirical basis. A developed Africa
will not simply be a statistically extrapolated version of Europe’s past development trajectory.
Past data alone cannot explain what it would be like to live in a world without pollution, without
hunger, without wars. A developed Africa and a sustainable world only exist ‘in theory’, not ‘in
data’ (Hilbert, 2014e). The good news is that the digital age does not only change empirical data
32
science, but also theory-driven modelling that allows to explore scenarios that never existed, for
example through computer simulation. The most powerful candidate are so-called agent-based
models (e.g. Epstein and Axtell, 1996; Bonabeau, 2002; Gilbert and Troitzsch, 2005; Farmer and
Foley, 2009). The combination of theory-driven simulation models and big data input to calibrate
those models is becoming the new gold standard of so-called computational social science
(Hilbert, 2014b). It reminds us that Big Data by itself is limited by the same limitations as all
empirical work: it is exclusively post-factum.
7. Conclusion
In a 2014 White House report of office of President Obama underlined that Big Data leads to
“vexing issues (big data technologies can cause societal harms beyond damages to privacy, such
as discrimination against individuals and groups)”, while at the same time emphasizing the
“tremendous opportunities these technologies offer to improve public services, grow the
economy, and improve the health and safety of our communities” (White House, 2014b). A
review of over 180 pieces of mainly recent literature
11
and several pieces of hard fact empirical
evidence has reconfirmed that the Big Data paradigm holds both promises and perils for
development. On the one hand, an unprecedented amount of cost-effective data can be
exploited to inform decision-making in areas that are crucial to many aspects of development,
such as health care, security, economic productivity, and disaster- and resource management,
among others. The extraction of actionable knowledge from the vast amounts of available digital
information seems to be the natural next step in the ongoing evolution from the “Information
Age” to the “Knowledge Age”. On the other hand, the Big Data paradigm is a technological
innovation and the diffusion of technological innovations is never immediate and uniform, but
inescapably creates divides while the diffusion process lasts. This review has shown that the Big
Data paradigm currently runs through such unequal diffusion process that is compromised by
structural characteristics, such as the lacks of infrastructure, human capital, economic resource
availability, and institutional frameworks in developing countries. This inevitably creates a new
dimension of the digital divide: a divide in the capacity to place the analytic treatment of data at
the forefront of informed decision-making and therefore a divide in (data-based) knowledge.
These development challenges add to perils inherent to the Big Data paradigm, such as concerns
about State and corporate control and manipulation, and the blind trust in imperfect algorithms.
This shows that the advent of the Big Data paradigm is certainly not a panacea. It is indispensable
to accompany and guide this transition with proactive policy options and pin-pointed
development projects.
11
Given the size of the phenomenon, the literature review is non-exhaustive. It has been realized during the period
of 2012 2014, mainly by the identification of academic articles and reports through the specialized online search
engine Google Scholar.
33
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SWG-IMGeneral-EB-0508
... HEIs have harped on IR (Institutional Research) to holistically link educational quality assurances practices to planning underpinning students' performance and effectiveness through the wealth of data crunching and mining critical to the performance management for the colleges/programs/HEIs (Dawson, et.al., 2016;Hrabowski, Suess & Fritz, 2011a,2011bPicciano, 2012;Brudan and Zarnecu, 2015). These existing IR practices and performance management can be enhanced and expanded through an integrated electronic system that seamlessly extracts data from student, faculty, infrastructure and resources administrative and academic silos channeled towards the educational applications and colleges/programs/HEIs management and their re-invention (Clarke, Nelson & Stoodley, 2013;Hilbert, 2013). ...
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... In the 4IR, there are six C's that underpin big data and big data analytics (Hilbert, 2013). These include cloud computing, content and context, community data sharing and collaborative partnerships among key stakeholders, the connection between networks and sensors, cyber which deals with the system properties, and customization which deals with the translation of data to achieve results (Hilbert, 2013). ...
... Big Data potentials to turn complex, often unstructured data into tortious information. (Hilbert, 2013) pointed out that Big Data transports a cost-effective prospect to advance decision making. In order to systematise the available literature and develop a research design to help edging set of approaches for investigation, (Daniel and Butson 2013) proposed a conceptual framework to describe Big Data in higher education along four components [see Figure 1]. ...
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This article defines the concept of an information measure and shows how common information measures such as entropy, Shannon information, and algorithmic information content can be combined to solve problems of characterization, inference, and learning for complex systems. Particularly useful quantities are the effective complexity, which is roughly the length of a compact description of the identified regularities of an entity, and total information, which is effective complexity plus an entropy term that measures the information required to describe the random aspects of the entity. Mathematical definitions are given for both quantities and some applications are discussed. In particular, it is pointed out that if one compares different sets of identified regularities of an entity, the ‘best’ set minimizes the total information, and then, subject to that constraint, minimizes the effective complexity; the resulting effective complexity is then in many respects independent of the observer.
Book
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines "noise" as the incompressible part in the data without useful information. Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few. The author is an Honorary Doctor and Professor Emeritus of the Technical University of Tampere, Finland, a Fellow of Helsinki Institute for Information Technology, and visiting Professor in the Computer Learning Research Center of University of London, Holloway, England. He is also a Foreign Member of Finland's Academy of Science and Letters, an Associate Editor of IMA Journal of Mathematical Control and Information and of EURASIP Journal on Bioinformatics and Systems Biology. He is also a former Associate Editor of Source Coding of IEEE Transactions on Information Theory. The author is the recipient of the IEEE Information Theory Society's 1993 Richard W. Hamming medal for fundamental contributions to information theory, statistical inference, control theory, and the theory of complexity; the Information Theory Society's Golden Jubilee Award in 1998 for Technological Innovation for inventing Arithmetic Coding; and the 2006 Kolmogorov medal by University of London. He has also received an IBM Corporate Award for the MDL and PMDL Principles in 1991, and two best paper awards.
Article
The term Big Data is applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, currently ranging from a few dozen terabytes to many petabytes of data in a single data set. This chapter addresses some of the theoretical and practical issues raised by the possibility of using massive amounts of social and cultural data in the humanities and social sciences. These observations are based on the author’s own experience working since 2007 with large cultural data sets at the Software Studies Initiative at the University of California, San Diego. The issues discussed include the differences between ‘deep data’ about a few people and ‘surface data’ about many people; getting access to transactional data; and the new “data analysis divide” between data experts and researchers without training in computer science.