International Journal of Communication 10(2016), 1681–1699
1932–8036/20160005
The Bottom of the Data Pyramid:
Big Data and the Global South
PAYAL ARORA1
Erasmus University Rotterdam, The Netherlands
To date, little attention has been given to the impact of big data in the Global South, about
60% of whose residents are below the poverty line. Big data manifests in novel and
unprecedented ways in these neglected contexts. For instance, India has created biometric
national identities for her 1.2 billion people, linking them to welfare schemes, and social
entrepreneurial initiatives like the Ushahidi project that leveraged crowdsourcing to provide
real-time crisis maps for humanitarian relief. While these projects are indeed inspirational,
this article argues that in the context of the Global South there is a bias in the framing of big
data as an instrument of empowerment. Here, the poor, or the “bottom of the pyramid”
populace are the new consumer base, agents of social change instead of passive
beneficiaries. This neoliberal outlook of big data facilitating inclusive capitalism for the
common good sidelines critical perspectives urgently needed if we are to channel big data as
a positive social force in emerging economies. This article proposes to assess these new
technological developments through the
lens of databased democracies, databased
identities, and databased geographies to make evident normative assumptions and
perspectives in this under-examined context.
Keywords: big data, Global South, bottom of the pyramid, biometric identities, inclusive
capitalism, crowdsourcing, database, democracy
Introduction
“Big data” is a misnomer. While the field is relatively young, much thought has already been put
into critiquing the term, particularly equating size with representation. Today, it is hard to argue against
the understanding that a dataset may be impressively large, but not necessarily random or reflective of a
global and diverse public. Context continues to matter, although it is much more challenging to apply
when big data is used in varied and unpredictable ways. Power relations continue to be structured within
Payal Arora: arora@eshcc.eur.nl
Date submitted: 2015–06–26
1
I would like to thank the anonymous reviewers and editors for their thoughtful comments and
recommendations to enhance this argument. I would also like to thank Discover Society for promoting
early ideas for this article on its blog. Initial ideas for this article were communicated as keynote talks in
2015 at the Technology, Knowledge and Society Berkeley Conference, IS4IS Summit Vienna 2015, and
the Rhodes Forum.
Copyright © 2016 (Payal Aora). Licensed under the Creative Commons Attribution Non-commercial No
Derivatives (by-nc-nd). Available at http://ijoc.org.
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these databased ecologies, framed as the ‘big data divide” (Andrejevic, 2014) or what boyd and Crawford
(2012) have noted, the divide between “the big data rich” (organizations that can generate, purchase, and
store large datasets) and the “big data poor” (those excluded from access to the data, expertise, and
processing power; p. 674). While focusing on the “divide” in the big data realm is noteworthy, what is
problematic is the usurpation of the digital divide discourse to encapsulate power struggles among a
generic marginalized populace, as if we live in a global data commons.
The fact remains that while the majority of the world’s population reside outside the West, we
continue to frame debates on surveillance, privacy, and net neutrality and the demand for alternative
models and practices to sustain the digital commons by mostly Western concerns, contexts, and user
behaviors. As Udupa (2015) argues, “it is important to widen the lens of media research beyond the
western worlds,” by conceptualizing new media developments via the lens of “context, variation and
power” (p. 2). Perhaps a decade ago it was legitimate to argue that much of this marginalized
demographic was not connected to the digital realm and, thereby, could not be incorporated into the
contemporary debate, relegating them to development studies experts. Since then however, with the
exponential growth of mobile technologies in even the most disadvantaged contexts, along with
liberalization policies, and public–private sector commitments to provide connectivity to even the most
deprived areas of the Global South, this is no longer a valid argument. It is not only the usual suspects
such as China and India taking over the digital sphere, but even countries such as Saudi Arabia and
Myanmar. For instance, in Myanmar the shift has been from a mere 1% of its population being online a
few years ago to almost 50% by the end of this year (GSMA, 2014). Or, take the case of the sub-Saharan
African region where the number of mobile phone subscribers has increased at a rate of 18% annually
since 2007, reaching 253 million in 2013. In fact, it is forecasted that by 2020 the majority of geolocated
digital data will come from these emerging economies.
The majority of this population, however, continue to live below US$2 a day and come with
diverse cultural modes of being, much of which remain a black hole to Internet-savvy scholars and the
public at large. C. K. Prahalad, a neoliberal guru in business studies, coined the term “bottom of the
pyramid” (BoP) to describe this roughly four billion people (2013). He argued that it was time to reframe
this populace as “consumers” instead of “beneficiaries,” moving away from persistent colonial perspectives
driven by white guilt and paternalism. This would be a win-win solution for both the market and the state,
where common good sits side by side profit making. This viewpoint gained a further boost with the rise of
Web 2.0 technologies and the cultural shift in perceiving users as cocreators and masses with collective
intelligence and wisdom (Surowiecki, 2004). Hence, we are called on to envision the poor as future digital
data consumers and agents of change. This fits well with the current call by Couldry and Powell (2014) to
situate the “notion of voice” at the center of analyzing the cultures of datafication. The challenge in
reconceptualizing and reconfiguring new media productions driven by algorithmic power is in assessing
how the authoritarian data regimes structured from above intersect with reflexive and resistive practices
from below.
This article argues that while there is much skepticism and caution on the social impact of big
data in the West, there is a bias in framing big data as an instrument of empowerment in the Global
South. Discourses around big data projects in the Global South have an overwhelmingly positive
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The Bottom of the Pyramid 1683
connotation. For instance, India is largely celebrated for its biometric identity project that links personal
identities with welfare schemes (Parker, 2011). Ushahidi, a social entrepreneurial initiative, is seen as a
groundbreaking crowdsourcing platform, providing real-time crisis maps for humanitarian relief efforts in
Africa, Haiti, and elsewhere (Rai, 2010). And it is hard to critique Facebook as it steps up to provide free
access to select sites such as Wikipedia in response to poor children’s struggle to gain access to
knowledge in places like Nigeria (Eagle, 2015). However, if we are to channel big data as a positive social
force in these contexts, we need to ask critical questions of these projects, decoupling moral and political
economies. Hence, this article proposes to analyze these new media developments through the lens of
databased democracies, databased identities, and databased geographies to make evident underlining
normative assumptions and perspectives in these underexamined contexts. The intent here is to open up
debate in the areas of big data policy and practices in the Global South and give a more global dimension
to big data studies.
Databased Democracy
With big data projects emerging in the Global South in the name of the poor, it is worth asking
whether we are experiencing a databased democracy today. Lyon (2013) calls this “the caring
panopticon,” incorporating aims of care rather than merely discipline, allowing us to take on a broader
perspective on surveillance for the common good than the typical Foucaultian lens. Here, the state
positions itself as a servant of the public, and shared data is its democratic enabler. In contrast, we have
the governmentality of big data, establishing its power and control through these new technological
regimes of practice. While equity is at the center of these data initiatives to signal transparency and trust
among citizens, it may continue to remain a form of self-marketing for governments unless and until these
initiatives are aligned with genuine restructuring by the state for an open political process (Taylor &
Schroeder, 2014).
Currently, open data initiatives are the vogue in the Global South. Chile, Bahrain, Kenya, Brazil,
and several other countries have openly shared hundreds of datasets on demographics, public
expenditures, and natural resources for public access to foster smart and livable cities for their citizens
(Linders, 2013). However, information only becomes knowledge and power for the social good when data
is put to uses conducive to its diverse and underrepresented citizens (Hilbert, 2013). Rather than stay
suspended in the dichotomous nature of this dialogue on databased democracies, this section explores the
complexities around two big data trends that have gained tremendous attention as instruments of the
common good. They are (1) biometric identities (e.g., India) and (2) crowdsourcing apps for development
(e.g., Africa).
Biometric Identities, Anonymity, and Colonial Lineage
The ambitious Biometric Identity Project in India promises a unique identification number (UID)
to each of its citizens through a consolidation of 12 billion of its citizens’ fingerprints, 2.4 billion iris scans,
and 1.2 billion photographs (Sarkar, 2014). While the West already has existing biometric identity
systems, it is nowhere near India’s scale and scope. This project is the brainchild of the technology
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entrepreneur Nandan Nilekani, who cofounded and built the multibillion-dollar outsourcing company
Infosys prior his appointment by the government to head this project.
This project has received much media attention, with discourse leaning toward the empowerment
of the marginalized. R. S. Sharma, secretary of the Department of Electronics & Information Technology in
India, recently declared that “digital India is not for rich people . . . it is for poor people” (Toness, 2014,
para. 3). Credible sources such as the BBC provide further endorsement where, “[the poor] with no proof
to offer of their existence will leapfrog into a national online system, another global first, where their
identities can be validated anytime anywhere in a few seconds” (Rai, 2013, para. 2). While the West
appears to be moving away from the convergence of datasets due to privacy laws, constitutional rights,
and public concern, these very initiatives in the Global South are celebrated.
This contradictory attitude stems from the fact that the majority of India’s citizens lack passports
or other forms of identity, making it difficult to disseminate welfare benefits to the masses. Welfare
benefits valued at approximately $60 billion are siphoned off by middlemen using fake identities, leaving
the anonymous poor helpless in the face of such acts. Hence, the UID project is positioned as a crusade
against corruption. Being against such big data initiatives becomes synonymous with being against the
poor. While this project can be a democratic enabler and a force for social good, we need to ensure that
we are asking the right questions of these institutional and governing technologies.
Already, a few critical voices have come out against the project, the most prominent among them
is the Nobel Prize winner Amartya Sen. He argues that these surveillance structures come with high social
costs for vulnerable groups, as the programs entail a tremendous loss of privacy and possible
criminalization of those not conforming to the state (Sarkar, 2014). Without strong constitutional personal
data protection laws, this system can lead to a number of human rights violations. It is even more
disturbing to find that this project has its roots in a 1992 government campaign to deport undocumented
Bangladeshi immigrants through the tracking ability of the biometric identity database. Pötzsch (2015)
argues that these digital techniques of enlisting the body, or what he terms as “ibordering,” individualize
the border by attaching itself to mobile bodies through the technical and the biological. Here, the body is
the border.
Secondly, while it promises to be a voluntary act, this new digital identity is tied to welfare
benefits, participation in public religious pilgrimages, and other societal events. Clearly, people are
operating within structured power relations that they are often powerless to contest. Thirdly, there is no
such thing as infallibility in authentication. When compromised, they are even harder to re-secure
compared to digital signatures (Sarkar, 2014). For instance, using a high-resolution camera, an iris can be
captured remotely without a person’s knowledge. As for data breaches, there are few contingency plans in
spite of the fact that this is a real possibility. Recent examples reek of state vulnerability such as the U.S.
government’s experience of a breach of sensitive military data and the hacking of Israel’s citizen
database. German hackers have proved that they can deceive fingerprint scanners and even iris scanners.
Furthermore, subjects can and do subvert such programs by mutilating their bodies (Breckenridge, 2005).
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Fourthly, pertaining to such a demographic, we need to account for what Magnet calls “high tech
racism” (2011, p. 28). Certain bodies are more “unreadable” than others. For example, farmers and
construction workers often have worn out fingerprints that scanners reject due to their “low quality
attributes.” Similarly, a common malady among the poor are cataracts that leave irises not conducive to
digitization. This provides exclusion of the very subjects that these programs are intended to include. As
Van der Ploeg argues, the UID becomes “a machine-readable witness against the subject” (2005, p. 113).
It furthers the exploitation of these people through protracted bureaucratic procedures due to new layers
of denial, expanding the cycle of poverty and exclusion.
Rao (2013) conducted an urban case study on the Delhi homeless to empirically address whether
the UID project furthered the neglect or empowered this chronically invisible populace. By focusing on
implementation, Rao was able to capture how these databased tools perpetuate conventional citizen–state
relations and advance marginalization through the exercise of “class arrogance, social indifference, and
corruption” (2013, p. 72). While the datafication of “established” citizens was successfully executed, the
homeless faced numerous obstacles, including the social worker’s ability to mediate on their behalf with
the state, their access to patronage, the agreeability of their “body” to digitization due to the
consequences of living on the streets, and the cooperation of the IT specialists allowing for retrials in
scanning fingerprints and irises. What this reiterated is that this system bears “the seed for future
discrimination against all those who do not fit into the electronic mould of ‘middle-class machines’” (p.
75).
Lastly, far from the claim that these initiatives are novel and unprecedented, we must recognize
that these surveillance systems have deep roots in colonial identification practices. During the mid-19th
century, to police their colonies, the British instituted biometric surveillance through fingerprinting (Owen,
2014). The fear of an uprising was a constant motivator to identify and track their “unruly” subjects. Here,
“unquestioned authority went hand in hand with pervasive fears of being deceived by the populace”
(Sengoopta, 2003, p. 204). Interestingly, even during the colonial days, fingerprints served as proof of
identity to access services such as the pension system.
Hence, before we are quick to celebrate these initiatives, we must recognize that these databased
techniques of democracy are assemblages of institutions, policies, histories, cultural practices, and
situational contexts that play out in a complex unison to materialize and articulate plural realities of
governance. In the datafication of the body, there is an innate assumption of harmony among the state,
the data, and the body. This mythification of harmony serves as a serious barrier to thoughtful, reflexive
policy and practice. Furthermore, while several postcolonial nations have gained newfound confidence and
a strong national identity in current times, much of their contemporary institutions are products of
colonialism on which projects such as these gain a foothold. By remaining unaware of such structures, we
perpetuate colonial regimes of surveillance. Instead, this should serve as an opportunity to rethink and
restructure traditional systems, if we are to have a vibrant postcolonial democracy.
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Bottom of the Pyramid, Crowdsourcing, and Inclusive Capitalism
When analyzing the Global South, one of the most influential paradigms framing their poor is the
bottom of the pyramid (BoP) rubric. By embracing the BoP perspective of the poor as empowered
consumers, we are in fact marketizing the poor. It seems today that the pathway to democracy is through
inclusive capitalism, the extending of the market system to the poor. BoP economies are on the rise
across the Global South. By 2011, a total of 439 BoP initiatives were recorded in nine sub-Saharan African
countries, targeting the needs of people living on incomes of less than $2 a day in multiple domains,
including health and education to information technologies and energy (Blowfield & Dolan, 2014). Informal
economies of the poor are brought into the fold by this neoliberal effort. Several corporations see the
virtue of this perspective and are vigorously experimenting on “doing good,” simultaneously gaining the
first-mover advantage among this future consumer base. This entails the conflating of moral and market
objectives, where the previously “unusable” poor become a viable and virtue-laden market.
In parts of Africa a number of corporations, not coincidentally some with colonial legacies such as
Unilever and Cadbury, transform participation by the poor into a new form of market research. As a
Unilever representative remarked as the company promoted good hygiene practices in these regions, “Of
course, when we are talking about toothpaste, it happens to be Close-Up . . . when we are talking about
soap, it happens to be Lifebuoy” (Dolan & Roll, 2013, p. 14). Marketing literature has long proved that
once you shift consumers’ behavior in a particular realm, you are well placed to gain their loyalty across
an entire category of products. This is no different with the IT industry. Currently, Facebook through its
Free Basics platform has promised to provide free access to select websites to the poor in emerging
economies. In doing so, Facebook becomes the Internet to this substantial BoP user base. These are by no
means a random set of websites, but rather sites that are globally popular such as Wikipedia so that, as
Facebook states, poor children can access and learn at no cost and gain an education. Net neutrality takes
a backseat in the name of doing good and gives Facebook a unique vantage into the databased behavior
of the BoP populace. Wikipedia serves as a Trojan horse, paving the path for Facebook’s monopoly among
the Global South’s data-driven subjects. Democracy of information and select IT brands are indelibly tied
together through such efforts.
Big data has opened up new forms of social entrepreneurship in emerging markets, promising
social equity hand-in-hand with corporate profit. This technocratic strategy confronting poverty revives the
“new frontier” rhetoric of the Internet, where anyone and everyone can benefit from these opportunities—
a social, profitable, and moral digital commons (Arora, 2014). Here, “poverty capital” (Roy, 2010) goes
hand-in-hand with “big data capital.” As Dolan and Roll (2013) argue,
these initiatives create BoP economies through a set of market technologies, practices,
and discourses that render the spaces and actors at the bottom of the pyramid
knowable, calculable, and predictable to global business and . . . technologies extend
new forms of market governance over the informal poor, reconfiguring their habits,
social practices, and economic strategies under the banner of poverty reduction. (p.
124)
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The critique of these BoP models, however, confront a number of big data initiatives that have
proved to be empowering such as Ushahidi, a crowdsourcing platform (Philip, Irani, & Dourish, 2012). This
site draws data from different ICT channels into real-time crisis maps to assist humanitarian relief efforts.
In 2010 Ushahidi launched a crisis map in a mere four days after the Haitian earthquake, well before other
agencies could respond. In fact, crowdsourcing as a democratic instrument has gained prominence in the
Global South, employing collective power to address social challenges, and has become a buzzword in the
humanitarian sector (Hellström, 2015).
Several applications of big data for development have emerged since then: an application that
tracks location-based data (e.g., GPS data from mobile devices) and nature-related data (e.g., weather)
to help farmers; Nextdrop, a crowdsourcing app that, for a nominal price, alerts the poor on where to find
drinking water; GroundTruth’s Map Kibera, which enlisted residents of Nairobi’s largest slum to map their
neighborhoods and claim public services (Hagen, 2011); and Grameen Foundation’s App Lab which allows
poor Bangladeshi women to access and manage their finances (Taylor, Schroeder, & Meyer, 2014). Digital
medical diagnostic software was launched in rural areas of the Himalayas where there is a dearth of
doctors, providing an affordable service to the poor on diagnosis and treatment of medical problems
(Arora, 2012).
While these are commendable efforts, we need to recognize that these are also business models
that rest on the failings of the state. The longevity of such social entrepreneurship lies in the belief that
the state will continue to disappoint its citizens. Here, zones of marginalization become zones of
innovation. Besides, there are social values implicitly embedded in the design of these tools that influence
the outcomes. Take, for instance, the farmers’ app for agricultural guidance; it is clear that a number of
assumptions reveal the naiveté of these well-meaning initiatives:
[F]armers will not contest information that they receive online; . . . they will trust these
new computing intermediaries and old intermediaries will disappear. It is assumed that
farmers will be more receptive to agricultural information when receiving it online versus
through other traditional communication media such as the radio and television. Further,
it is believed that the dearth of relevant agricultural information is the prime reason
holding farmers back from achieving mobility. Another presupposition is that given the
access to such information, farmers will abandon their “traditional” practices for “better”
agricultural practices, where efficiency and productivity are the prime goals. (Arora,
2010b, p. 127)
A case study on the implementation of medical diagnostic software in the Himalayas found that in
surveying villagers on their health issues to populate the software, the programmed categories did not
suffice. In fact, the majority of villagers reported illnesses that fell into the “Other” category, primarily due
to social deprivations such as chronic hunger, long hours in the field, gender bias and practices like child
marriage, and patronizing the local shaman (Arora, 2010a). Hence, to more effectively instrumentalize big
data for the benefit of the poor, we need to incorporate these BoP users at the initial stages of software
design and programming. This will enable sensitization of the socioeconomic and cultural contexts at hand
and embed a more representative value system reflective of these new consumers. In tandem, we should
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institute targeted literacy programs alongside the implementation of big data projects to educate
designers and programmers on social practices that marginalize the most vulnerable segments in society
(in this case, the lower castes and girls) if we are to operationalize the “inclusive” part of the inclusive
capitalism model.
Bott and Young (2012) argue that crowdsourcing in the Global South comes with complex
challenges. First, they question the nature of the “crowd” as necessarily active and diverse. The extent of
activity depends on the degree of authoritarianism in the social fabric, allowing for participatory practices
without endangering one’s safety. The extent of “open” participation rests on the level of citizens’ trust
that their government will institute changes in policies and practices. In other words, just because
databases are “open,” they do not necessarily result in open practices. Second, regarding diversity, Bott
and Young (2012) found that often the crowd does not emerge from the bottom, but rather from the top
of the pyramid. Given the deep inequalities and often patriarchal structures in the Global South, it is not
surprising that the authors found an overrepresentation of elite, educated, young males amid the digital
crowd.
Third, much data that is valid for the poor is often nondigital, creating an obstacle to
democratization through digital databased projects. For instance, the diagnostic and treatment app for the
poor in the rural Himalayas was populated with only allopathic data (mainstream medicine practiced in the
West), disregarding indigenous medical practices such as homeopathy, Ayurveda, and oral traditions
(Arora, 2010a). Fourth, the poor have access to information via nonsmart mobile phones with limited and
often expensive data plans (Rangaswamy & Cutrell, 2013), providing a reductionist and skewed
consumption of shared intelligence. This can foster misunderstandings when readers consume only
fragments of information, weakening rather than strengthening the crowd as informants. In such a digital
climate the crowd is ripe for manipulation. Fifth, crowdsourcing allows the state to more easily target
subjects, where contributors can be incriminated for their participation. This feeds into the rich literature
on protests and digital media beyond the West, debunking the much-touted Twitter and Facebook
revolutions (Kharroub & Bas, 2015). Crowdsourcing in the hands of the state can have deadly
consequences by strengthening oppression of the masses, such as how digital technologies facilitated
political violence by armed groups across Africa (Pierskalla & Hollenbach, 2013). Lastly, there is a false
understanding that collaborative knowledge is synonymous with quality knowledge, given that these
knowledge makers are not necessarily representative or experts (Arora & Vermeylen, 2013).
An independent project evaluation of Ushahidi, the poster child of crowdsourcing in the Global
South, revealed that in spite of tremendous media attention, this program continues to face serious
barriers in adoption and use (Morrow, Mock, Papendieck, & Kocmich, 2011). It was found that the data
reporting often clashed with rigid information requirements of the international NGOs involved in disaster
relief. Poor information infrastructures such as outdated computers and browsers and limited bandwidth
contributed to low levels of use. Often, the messages from the field lacked sufficient detail, slowing reliefplanning needs. There was a significant rate of misclassification, sometimes intentional, where volunteers
would misclassify general distress messages for food or water to garner immediate attention from the
relief organizations. Also, while the strength of Ushahidi lies in its field partnerships with other crisis
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organizations, this created uncertainty in its corporate identity and, thereby, low awareness among the
targeted user base in Haiti.
To conclude, social entrepreneurial efforts using big data in the Global South can extend
democratic practice, but requires the involvement of BoP users at all steps, including software design and
implementation. To keep us from romanticizing the values of the poor, we must ensure that literacy
campaigns align with the implementation of these digital tools to guarantee they will be accessible to the
most vulnerable populations among the BoP base. Further, while crowdsourcing for development can be
instrumental in redistributing power over information and facilitating grassroots momentum, it can just as
easily become a hegemonic mechanism of control. Indeed, the social impact of big data projects can have
far more deadly consequences in the Global South, depending on the society’s degree of patriarchy and
authoritarianism. It is clear that crowdsourcing in the Global South has much overlap with the Global
North on matters of concern such as garnering quality information, distributing expertise, addressing
institutional politics in programming, and representing a diverse public. While the focus of this article is on
big data in the Global South, we need to situate this neglected arena within the larger big data discourse
to disrupt neat distinctions between the Global North and the Global South. In doing so, this opens up the
field to a more nuanced analysis of how big data practices embody a constant interaction among global
imaginations and governance models and local/national dynamics.
Databased Identity
In the section on databased democracy, the focus is on analyzing how big data is designed and
implemented to foster an inclusive society by enhancing access to social systems. Here, identity is framed
by the notion of citizenship. In this section we delve into how big data enables the identification,
organization, and classification of groups and individuals, particularly those on the margins, and the
consequences of such algorithmic structures. Clearly, both sections are related, and yet, identity does
require going beyond the citizen to a more complex self.
The Postmodern Self, Politics of Algorithm and Cosmopolitanism from Below
Groups that are the most marginalized and vulnerable are often the most typecast, with group
identities imposed on them. In the forgoing section, case studies underlined how the homeless, the
migrants, the rural villagers, and women and girls become subservient to the politics of information
infrastructures in their numerous attempts at databased legitimation. In this section, we reflect on how
manufactured social narratives infuse databased structures and reinforce staid identities beyond the
notion of citizenship. Numerous studies have been done on this subject such as the decades of
institutionalizing black pathology as normative of African-American mental health (Williams & WilliamsMorris, 2000), framing gender identities as “a cultural category and a material institution that uses
biological differences to construct the sexual division of labour” (Castells, 2011, p. xxix), to the influential
“culture of poverty” worldview, that argued that the values of poor communities perpetuated their state of
poverty (Lewis, 1959). In the context of the Global South, the ground-breaking work by Narayan (2000)
alerted development practitioners and scholars alike on the fact that few knew much about the target
groups that they spent years researching and serving. She reported that the poor cared about much of the
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same things as the rest of us: happiness, children, peace, dignity, safety, respect. Over the years rich and
thoughtful critiques have emerged on framing the identities of the poor in these emerging economies.
Sustained ethnographies have revealed the multidimensional nature of the poor, including their agency,
their plurality and the “cosmopolitanism from below” (Appadurai, 2013).
New technologies have a way of promising social disruption in the form of allowances for multiple
identities. In the early years of the Internet, there was much enthusiasm for virtual worlds of fantasy,
where one could break free from social identities through plural selves of our own making:
In the MUDS2, the projections of self are engaged in a resolutely postmodern context.
Authorship is not only displaced from a solitary voice, it is exploded. The self is not only
decentered but multiplied without limit. There is an unparalleled opportunity to play with
one’s identity and to ‘“try out’” new ones. MUDS are a new environment for the
construction and reconstruction of self. (Turkle, 1994, p. 158)
Since then, there has been a substantive critique of this so-called freedom of identity, arguing
instead how social and political institutions, values, and rules of governance of the material world continue
to affect the digital world (Dawson & Cowan, 2013; Gal, Shifman, & Kampf, 2015; Nakamura, 2013).
Hence, we see racism, sexism, and other forms of power play continue online, with little escape from our
enforced identities.
With every new technological innovation come new promises, new euphoria, and a regurgitation
of past hopes and aspirations. Big data is no different. Technology becomes ahistorical—again. Jos de Mul
(2015), a prominent philosopher of technology, celebrates the liberating aspect of big data, bringing back
to life Turkle’s postmodern self. To illustrate the power of big data in freeing up identity as opposed to the
traditional database, he equates the relationship between big data and traditional databases to that of
interculturalism and multiculturalism. This metaphorical argument underlines how the traditional database
is much like multiculturalism, where different cultural identities (datasets) coexist, but do not interact with
one another. In contrast, big data, much like interculturalism, is about intersectionality, interactivity, and
play in infinite ways, revealing a more multiplexed identity. This allows the liberation of the postmodern
self from her traditional codes. Big data speaks against stereotypes, allowing for what de Mul (2009 calls
the “exhibition value” being replaced by “manipulation value” (p. 98). From digital reproduction, we are
now in the age of digital recombination, a new database ontology. De Mul employs another metaphor to
highlight big data’s capacities: the gene versus the meme. Traditional data has been treated much like
genes, permanent and unchanging, while big data allows data to transform into memes, dynamic,
temporal, and noncommittal, with a possibility of infinite recombination. At last, de Mul proclaims, “In this
digital era, tradition has become a commodity rather than an existential choice” (2009, p. 18). To
illustrate this computational identity further, he brings up an example meant to underline the celebratory
aspect of big data’s “intercultural” dimension:
2
Multi-User Dungeon or “MUD” is a multiplayer real-time virtual world.
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I saw an amusing example of this [interculturalism] last spring on a metro station in
Rotterdam when a young, veiled Muslim girl came towards me: She was on rollerskates,
wearing a Smiley T-shirt and talking, via her mobile phone, to a girlfriend in a
remarkable mixture of Dutch and Arabic. (2015, p. 97)
Implicit here, however, is the pre-social sorting of these datasets, where, clearly, the dataset of
“muslim girl” (religion and gender), “rollerskates” (sports), and “mobile phones” (technology) belong to
different databases. The point of departure starts with sorting the datasets according to predisposed
beliefs that view these categories as normatively separate. This privileged and problematic worldview is
often programmed into the design of digital architectures, reproducing existing prejudices as a social fact.
Hence, we need to pay greater heed to where the values in digital design emerge and who dictates these
information infrastructures if we are to secure open-ended databased identities.
Stereotyped identities become overt on search engines, reenacting, reproducing, and reinforcing
prevailing cultural codes. Take the autocomplete feature offered by search engines such as Google.
Algorithms shaped by users’ aggregated search activities on the Web dictate every search query. Far from
providing us with infinite combinations and new associations with identities, it was found that search
queries underlined and exacerbated racism, sexism, and political difference (König & Rasch, 2014). Hence,
it is impossible to separate online and offline values, social practices, and power relations as they mutually
reconstitute each another.
Anish (2015) extends this discourse with big data developments in the Global South by
problematizing how datafication is being framed as a way to replace “social identity” (e.g., face-to-face
conversations, village-based kinship, place-based kinship) with “system identity”:
While social identity is an identity continually renegotiated through linguistic interactions
and social performances, bureaucratic identity—glimpsed in passports, driver’s licenses,
and other identity cards—is a construction of fixed personhood for the purposes of
modern organizational needs, ensuring that the member has remained essentially the
same despite changes in personality, body, and behavior. With the spread of information
technologies, however, there has emerged a new variation of identity—system identity,
which represents persons as dynamically forming clouds of data. While system identities
can serve the bureaucratic need for identifying members, their role far surpasses the
functional necessities of inclusion and exclusion. (p. 42)
There is the perception that as you scale up, you trade identity for inclusion. The reductionist
view of village life and even disdain toward the poor are reminiscent of the culture-of-poverty worldview
that often become inscribed in the design of big data architectures. The postmodern self is a fiction and
fantasy that resurfaces with each new technological innovation. It is equated with a democratic state of
being. While this argument has played out numerous times in the past, big data seems to have inspired an
encore. Features such as autocomplete reproduce stereotypes by reinforcing negative associations.
Clearly, social identities are curated online as commercial interests, state agendas, and powerful cultural
groups exercise their agency in this identity game. Unlike the Global North where policies and laws are
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International Journal of Communication 10(2016)
being put in place to protect individuals and organizations from damaging associations (e.g., the right to
be forgotten), much of the Global South has yet to prioritize the regulation of big data to protect its
citizens from harmful digital practices. Hence, transparent and open data curation of group and individual
identities can lead to more thoughtful policies and practices in these emerging digital economies.
Databased Geography
Prior sections bring to the surface deep-seated biases on how big data is framed, designed, and
instrumentalized in the regions of the Global South. This article argues that big data projects in these
marginalized
contexts
are
predominantly
driven by
the
rhetoric
of
democracy,
inclusion,
and
empowerment of the poor, subjecting these seemingly altruistic efforts to far less scrutiny than they
deserve. By offering a conceptualization of databased democracy and databased identity, the intent here
is to critique the extent to which traditional social practice is redefined and transformed with the rise of big
data. However, it is not sufficient to look at the process of change that big data may or may not engender,
but also the nature and digital locatedness that these enactments emerge from and occupy. Hence, this
section on databased geography argues that decades of development projects in the Global South have
led us to analyze BoP users’ digital behavior as utilitarian and, thereby, not representative of this rich and
diverse public.
Leisure Commons and the Playful Poor in Emerging Economies
It can be argued that the digital commons is today’s leisure commons (Arora, 2014). At this
stage, we have much evidence that the most frequented sites in the Global North as well as the Global
South are leisure oriented. Primarily, people go online to romance, game, be entertained, consume media,
view pornography, and share their personal thoughts and feelings. It is the arrival of a new kind of
movement, a novel way to experience, produce, and consume leisure: “whether desired or not as part of
any “official” history of this currently central cultural medium, online recreation or “virtual leisure” has
been positioned among the dominant elements within the Internet’s development” (Weiss, Nolan,
Hunsinger, & Trifonas, 2006, p. 961). Hence, digital geographies such as Facebook, Twitter, YouTube, and
other networking sites are prime social geographies within which people and organizations interact,
protest, and commercialize their engagements. There is tremendous research being done on how big data
within these terrains is harnessed for multiple purposes from the personal, the commercial, and the
political, immersing ourselves in debates on privacy, surveillance, digital labor, and prosumption (Postigo,
2014).
When we shift our attention to the Global South and the BoP users, their new media practices are
predominantly framed as instrumental and utilitarian. This is partly because development agendas drive
this research with a strong historical bias for the socioeconomic (Arora & Rangaswamy, 2013). For
instance, there is more emphasis on farmers checking crop prices online than, for instance, watching
pornography on their mobile devices. Growing scholarship, however, underlines digital leisure practices
among the poor in recent years: how slum youths use mobile phones to romance girls in India
(Rangaswamy & Cutrell, 2013), play games in LAN houses in Brazil (Kolko & Racardio, 2014), access
entertainment via mobile banking apps in Kenya (Gajjala & Tetteh, 2014), and generate political jokes in
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the Twittersphere and blogosphere in China (Yang & Jiang, 2015). These new media cultures underline the
importance of desire as an aspirational construct, making visible the diversity of this emerging public
(Udupa, 2015).
Clearly, a mental shift is needed for how to approach this populace and their user practices if we
are to gain a nuanced and comprehensive understanding of the social impact of big data in their daily
lives. This article argues that to understand the nature of big data in the Global South, we need to first
recognize that digital life is lived within digital leisure geographies for the most part. As emerging
economies globalize and urbanize exponentially and their BoP users, or “prosumers,” become more critical
consumers and creative contributors of digital content and, arguably, free laborers instead of classic
development beneficiaries, this paradigm shift will provide an open-ended, explorative, and pluralistic
perspective on big data research.
Conclusion
There is no doubt that the data deluge produced by the bottom-of-the-data-pyramid users will
have a major impact on the future of the Internet. The question remains - How do we treat this rising
populace as culturally diverse and yet refrain from exoticizing them? We must be careful not to
romanticize the poor, as they are a pluralistic group that at times perpetuates social values harmful to
certain segments of society based on, for instance, caste, tribal affiliation, and gender. Digital literacy
campaigns should accompany big data projects in the design and implementation, negotiating human
rights with culturally sensitive practices. While we give due recognition to big data as an empowering tool
within emerging economies, we must attend to simultaneous efforts to strengthen institutions that will
protect individual and group privacy. In this pursuit, alternative modes of inclusivity should be sought
beyond the default neoliberal approach on the marketization of the poor through inclusive capitalism.
Current studies on the “big data divide” (Andrejevic, 2014; Boyd & Crawford, 2012) make
peripheral the dichotomies of the West and developing nations to the more central focus on systemic
inequality between those who have access to and control over data and those who do not. This article
argues that while these studies tackle big data through the lens of power relations on a globalized stage,
we need concerted and sustained scholarship on the role and impact of big data on the Global South. After
all, the technocratic approach of the state through ICT-oriented development projects has a distinct
colonial legacy in much of the Global South. Currently, user behavior and institutional practices of the
Global North disproportionately represent and influence our understandings on this matter, which can
serve as a genuine barrier to thoughtful, indigenous design of big data applications for emerging
economies.
The coupling of the term “databased” with democracy, identity, and geography is deliberate as it
allows us to focus on expectations, assumptions, and prevalent policies and practices, instigated by the
rise of big data. Here, big data promises to foster a new type of inclusion, personhood, and sense of place.
This article analyzes this essentializing of technology and joins a rich scholarship on the problematizing of
utopian claims of past mass communication technologies, including the radio, television, and computer.
Unfortunately, as this article discusses, these discourses are persistent and appealing and have gained a
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International Journal of Communication 10(2016)
new lease on life in the age of big data. This work illustrates the challenges of reifying this idyllic notion by
questioning the premise that these surveillance architectures empower the poor. Anonymity has served as
a powerful instrument of activism in these suppressed contexts and continues to this day as witnessed in
the uprisings in the Middle East and elsewhere. Thereby, the pretext of associating anonymity with
vulnerability negates larger understandings and histories of identification as a technology of control.
Hence, we need to disrupt the innate belief in the harmony of the state with that of data of the citizen.
While the governmentality of big data can produce major efficiencies for the poor such as the
disseminating of welfare benefits through the biometric identity project, it comes at the astounding price
of privacy. While several studies today indicate that people in the West attribute high value to privacy,
there is a dearth of studies on how marginalized populations in the Global South view, construct, and
practice privacy. Thereby, the trade-off is made on their behalf, with little involvement of this substantial
public. While several countries such as The Netherlands, United Kingdom, and France are declaring data
consolidation unconstitutional in order to protect their citizens, in the Global South this trend is moving in
the opposite direction. Wide-sweeping and wretched poverty provide the urgency and excuse to distance
events and policies of the Global South from those of the Global North. Guilt politics and moral blackmail
interfere in the democratic shaping of these systems, building on the rich and global learnings of
instituting digital mechanisms for equitable and fair practice. Hence, the dichotomy of the Global North–
South, while artificial, serves to identify and dismantle exceptionalism and exoticism prevalent in the big
data efforts in the Global South.
When it comes to BoP economies of doing good and making a profit, marketizating the poor is
positioned as an innovative effort to include the marginalized as consumers and agents of change. Again,
trade-offs are made; through Facebook’s Free Basics platform, net neutrality is sacrificed to give the poor
free access to certain Internet sites. Is net neutrality a privilege for only consumers in the Global North?
These short-term measures fortify Facebook’s dominance in the digital market of the Global South,
delivering exclusive insights into the databased behavior of the BoP demographic. However, it is common
knowledge that monopolies rarely operate for the common good, often reversing initial benefits as power
becomes concentrated in select entities.
Our point of departure on today’s information economy should be empirically driven instead of
ideological. We have amassed much evidence to date on how racism, sexism, and other forms of power
play continue online, reinforcing stereotypes. The Internet has not and will not naturally manifest in a
virtual community of self-organized equality as envisioned in the early years. Clearly, social identities are
curated online as commercial interests, state agendas, and powerful cultural groups exercise their agency
in this identity game. As the residents of the Global South move exponentially online, particularly the
disenfranchised, they intersect with local and global politics and economies. Their digital activity requires
much-needed scrutiny to gain a globalized worldview on big data. We need to provide institutional,
financial, and social encouragement to grassroots activism, where online representation of the currently
invisible and vulnerable is brought to the fore. The “cosmopolitanism from below” (Appadurai, 2013)
should find its way to the big data stage. We must pay more attention to where the values in digital
design emerge and who dictates these information infrastructures to create allowances for a richer
databased identity. The Global South should be actively engaged with current debates—such as the right
International Journal of Communication 10(2016)
The Bottom of the Pyramid 1695
to be forgotten—as multinational IT companies confront national sentiments, values, and institutions,
illustrating how context continues to matter.
Lastly, we must recognize the cultural character of the digital sphere in which the poor live and
act. Amassed evidence over the decades reveals that what users primarily do online is characterized by
entertainment, romance, gaming, and socializing. We need to insert the notions of leisure and desire into
our analytical framework when approaching the social impact of big data produced and consumed by this
rising public. Databased geographies are leisure geographies, even for the poor. This opens fruitful
avenues of scholarship, bridging leisure studies with new media studies, which is not the prerogative only
of the Global North. Yet, most studies of new media practices among the poor in emerging economies
continue to focus on the instrumental aspects. Hence, a critical reconsideration of embedding the
aspirations, desires, values, and behaviors of this largely unexamined populace into big data
infrastructures is the pathway to a democratic digital sphere.
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Christian Bischof
Daniela Wilfinger
https://doi.org/10.21278/TOF.43206
ISSN 1333-1124
eISSN 1849-1391
BIG DATA-ENHANCED RISK MANAGEMENT
Summary
Today’s global and complex supply networks are susceptible to a broad variety of
internal and external risks. Thus, comprehensive and innovative approaches to risk
management are required. This paper addresses the question of how Big Data can be used for
the implementation of an advanced risk management system. A conceptual framework
covering three major dimensions of Big Data-driven risk management, i.e. type of risk, risk
management phases and available technology, is introduced. Additionally, selected
application examples for early detection, assessment, mitigation and prevention of risks in
supply networks are provided.
Key words:
Risk Management, Supply Networks, Digitalisation, Big Data
1. Introduction
Global supply networks are the basis for gaining access to raw materials, suppliers, and
markets. Although these global networks provide substantial advantages and potentials for all
companies involved in the supply chain, they also contain substantial risks mainly coming
from a high degree of vulnerability and exposure of intra- and inter-company production and
logistic processes to unforeseen events. [1] Thus, real-time reactions to disturbances,
especially of time-critical processes, become increasingly important. Accordingly, John
Chambers, the chairman and chief executive officer of Cisco Systems, made the following
remark after the earthquake in Japan: “In an increasingly networked world, supply chain risk
management is top of mind in global organizations as well as a key differentiator for leading
value chain organizations”. [2]
As a consequence, innovative approaches to supply management have gained
importance in recent years. Generally, supply chain management is a widely accepted concept
for the target-oriented management of (global) supply chains and networks. In today’s
complex and dynamic environment, risks in the supply chain constantly endanger the
profitability of the companies involved. Therefore, approaches like risk-adjusted supply chain
management including structured risk management and an early-warning system can
potentially avoid or mitigate risks, leading to a further improved financial performance and
competitive advantages. [3]
TRANSACTIONS OF FAMENA XLIII-2 (2019)
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C. Bischof, D. Wilfinger
Big Data-Enhanced Risk Management
Recent developments in the field of digital technology have improved support for
approaches like risk-adjusted supply management. The application of innovative technologies,
in-memory computing, the Internet of Things (IoT), and Radio Frequency Identification
(RFID), can facilitate establishing globally interconnected manufacturing and logistics
systems. This leads to greater transparency and agility, and consequently to the ability to
manage the constantly increasing dynamics and complexity of global supply networks. [4]
2. Research Purpose and Methodology
This paper addresses the question of how Big Data potentially affects risk management
in supply networks. For this purpose, the currently ongoing transformation of supply chains
into supply networks is outlined first. This sets a basis for the analysis of major risks lurking
in such networks. Furthermore, a risk management cycle is introduced as the methodological
basis representing one dimension of the framework for Big Data-enhanced risk management
developed subsequently. In contrast to existing approaches, this framework aims at providing
a comprehensive view of risk management by integrating two additional dimensions: the type
of risk and the technology that can either avoid or minimize risk.
3. Transformation of Supply Chains into Supply Networks
One of major challenges facing global companies are planning, managing and
controlling their supply chains. [5] So far, the global division of work processes has shaped
modern supply chains, leading to economic benefits – especially for industrialized countries.
In this context, the supply chain is considered as a linear chain in which the material,
information and finances flow linearly in one direction from suppliers to producers, retailers
and end customers. This process is ongoing as companies are still deciding on whether an
intensified participation in global supply chains may increase their profitability. Today,
discussions and decisions in supply management mainly take place in the context of “make,
cooperate or buy”. Companies do not act as a self-contained construct anymore, but rather
consider collaborative partnerships as a sustainable competitive advantage. [6]
Previously, supply chain professionals managed the “Four V’s - Volatility, Volume,
Velocity, and Visibility” in order to optimize objectives, such as total cost or service quality.
[7] Although still valid, these objectives are currently challenged as digital technologies
enable innovative approaches to the setup, organization, management, and hence to the
performance of global supply chains. [8] Originally, the main driver for the development of
inter-organizational supply networks was the application of internet technologies for
information allocation. [9] Recent developments in digitalisation and digital technologies
have accelerated the transformation of supply chains into complex and globally interrelated
supply networks with numerous interfaces and channels. The horizontal integration of these
supply networks has to consider a vast number of requirements of systems, processes and
cultures coming from different companies and locations. [10] Since the external environment
of such networks has also become more complex and increasingly volatile, the planning and
management efforts together with the inherent risks of global supply networks have grown
significantly.
4. Risk Management in Global Supply Networks
Today, companies are exposed to a large number of risks resulting from market
globalisation, reduced product lifecycles, complex networks of international partners,
unpredictable demand, uncertain supply, cost pressure, necessity to be lean and agile,
increasing outsourcing and off-shoring, and dependency on suppliers [11]. In the specific
context of logistics and supply chain management, risks may arise from the complexity of the
74
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Big Data-Enhanced Risk Management
C. Bischof, D. Wilfinger
market characterized by the shortage of suppliers, replacement products and technologies.
[12] More comprehensively, the supply chain risk can be considered as any risk to the flows
of information, material and product from the original suppliers to the delivery of the final
product to the end customer. [13] According to March and Shapira, the supply chain risk is “a
variation in the distribution of possible supply chain outcomes, their likelihood, and their
subjective values”. [14]
Merging the two definitions stated above and considering the different types of flows
generated between the cooperating companies, the supply chain risk is considered in this
paper as an adverse effect on the flows between various elements of a supply network.
Variability may affect the flow of information, goods/materials and/or financial resources.
Therefore, the following risk assessment is based on three risk segments representing these
flows within a supply network:
a) The physical movement of materials and products from suppliers to customers is
referred to as material flow. Even in a small and structured supply chain, there is a
certain risk that the material is not provided on time, in the expected amount and
quality at the designated place/company. Due to greater interconnectivity and the
influence of the bullwhip-effect in supply networks, this risk increases
exponentially. Additional risks to the material flow in supply networks are related to
global sourcing. The extensive application of this sourcing strategy leads to
significantly longer transport distances and times and consequently to a greater
susceptibility of the entire network to unforeseen events, such as political crises,
terrorist attacks or natural disasters. Other elements of material flow risk that need to
be considered are single sourcing risk, sourcing flexibility risk, supply capacity risk,
supply selection and outsourcing risk, production risk, operational disruption,
demand volatility, etc. [15]
b) Financial impacts of variations and/or disruptions in the material flow consist of lost
turnover, increased cost due to higher inventories, penalties, and lower cash flows.
Besides these derivative monetary effects, one major financial flow risk is the
exchange rate risk. In global supply networks, exchange rates have a significant
influence on companies’ after-tax profit, supplier selection, market development,
and other operational decisions. [15] Price and cost risks may also arise from the
scarcity of raw materials or fluctuations on international commodity markets. [16]
Another major element of the financial flow risk is the financial strength of network
partners. The financial weakness of a network member may easily affect the entire
supply network not only in terms of financial but also of material flows. [15]
c) The high complexity and dynamics of global supply networks require multiple
information flows in parallel to the material and financial flows. These information
flows are not only needed for the collaborative organization and coordination of the
network in day-to-day operations, but also for having the necessary strategies for
managing risks and disruptions. Information technology is widely perceived as an
important facilitator in the collaborative (risk) management of supply networks. [17]
Therefore, the information accuracy risk is a major threat to the information flow as
it hinders or prevents the prompt interchange of relevant information among
network partners. Considering the current state in business practice, this risk is still
present because a variety of information systems and the current information
technology operated by supply networks often negatively affect interoperability and
information flows. [18] Another element of the information flow risk is the
information system security and disruption, which may be caused by the lack of
professional IT-management, hackers, or natural disasters. [15]
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Due to a high degree of integration and interdependency, every stage or process within a
supply network carries an inherent risk potentially affecting the entire collaboration. Hence, a
comprehensive risk management framework is required. The objectives of such risk
management are to identify possible risk sources within and outside the supply network and to
develop appropriate action plans. Therefore, the risk management cycle consisting of three
different stages is applied in this study in order to keep the influencing risks on a low level.
Risk Mitigation
and Prevention
Risk Identification
and Monitoring
Risk Assessment
Fig. 1 Risk management cycle
a) Risk Identification and Monitoring: This initial stage of the risk management
process identifies risks and their potential causes. Common risks together with extra
risks have to be determined. Hence, the main focus of risk identification is to
recognize future uncertainties to enable proactive management of risk-related issues.
Risk factors, such as suppliers, countries, and transportation systems, are monitored
and evaluated by various key performance indicators (KPIs). The level of an instock inventory, production throughput, capacity utilization, and delivery lead times
are some of the KPIs that can be applied to identify an abnormal situation that may
involve a potential risk. If there is a significant deviation from the standard in a KPI,
the alarm is triggered, establishing an early-warning-system. [17, 19]
b) Risk Assessment: After identification, an assessment is required in order to
prioritize risks and take specific management actions. Probability of occurrence, risk
level and risk impact are criteria that are widely used for differentiating risk.
Consequently, the use of probability functions and historical data is necessary. Due
to a relative lack of data (data are often available for risks such as currency rate and
lead time but are usually rare and insufficient for events like earthquakes and
terrorism), risk assessment can be rather difficult. Moreover, risk impact is usually
expressed in terms of cost, but performance loss, physical loss, psychological loss,
social loss, and time delay also have a significant impact on the company and
therefore on the supply network. [20]
c) Risk Mitigation and Prevention: Based on the data collected and assessed in the
previous stages, risk mitigation aims at the elimination of identified risks or the
reduction in the degree of their probability of occurrence and/or their impact. For
this purpose, risk mitigation strategies are implemented by adopting two
conventional approaches. In the reactive approach, no action is taken before the risk
has materialised but there is a strategy in place to lower the impact. In this strategy,
no plan to reduce the probability of occurrence is considered. In the proactive
approach, strategies are implemented to mitigate the risks before they materialise.
Proactive mitigation plans are implemented in order to decrease the probability of
occurrence rather than to reduce a potential impact of a materialised risk. [20]
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Applying Big Data to traditional risk management approaches may result in potential
enhancements of the process and consequently in a significant reduction in the level of the
overall risk to the entire supply network.
5. The Concept of Big Data
Although there is no universally accepted definition, a common understanding of Big
Data has been established in science and practice. This understanding is based on the major
characteristics of Big Data – the four V’s. [21]
Volume is the most visible aspect of Big Data. Currently, data volumes in the range
of gigabytes and terabytes can be processed. As the range will continue to increase,
the data unit of a petabyte will be widely available in the foreseeable future.
Variety describes the availability of data in many different forms, ranging from
machine data to relationship data. This data can be structured, partly structured or
completely unstructured and can be generated by internal as well as external data
sources.
Velocity means the real-time acquisition, transformation, processing and analysis of
streaming data generated by sensors and embedded systems or data coming from the
web. This can be obtained by applying in-memory computing.
Veracity describes the confidentiality of captured data, which depends, for instance,
on the quality of data collection and secure transmission channels.
Thus, Big Data is able to combine different data sources and various types of structured
and unstructured data. Additionally, it allows for the analysis of large amounts of data in real
time, which has not yet been possible.
6. Big Data-Enhanced Risk Management Framework
In order to assess the potential implications and benefits of Big Data for the risk
management in supply networks, a methodological framework needs to be developed. For this
purpose, a three-dimensional approach will be applied covering the two major aspects of risk
management in supply networks: the type of risks as one dimension and the phases of the risk
management cycle as the other. Additionally, a third dimension is to be introduced addressing
the major technologies in the area of Big Data (Fig. 2).
Fig. 2 Big Data-enhanced risk management framework
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The main technologies that are discussed extensively in the context of Big Data are inmemory computing, advanced analytics, the Internet of Things, and blockchain.
In-memory computing is an approach where data is no longer stored on hard disks
but constantly provided in the random-access memory of information systems. This
allows more rapid access to this data and its real-time processing. [22]
The concept of advanced analytics is largely based on in-memory platforms aiming
at integrating and analysing large amounts of various data coming from different
internal and external sources. Thus, advanced analytics allows constant monitoring
of relevant data sources leading to an event-triggered analysis and timely reactions
to internally and externally induced variations in the material, financial, and
information flows. Additionally, advanced analytics can be utilised to predict future
developments and risks by a statistical analysis of historical data. [23]
The Internet of Things (IoT) is a new paradigm which can be defined as a dynamic
global network infrastructure consisting of a variety of devices or things such as
RFID tags, sensors, mobile phones, and actuators, which are able to interact with
each other through addressing schemes and self-configuring capabilities or
collaborate with their neighbours to reach common goals. [24, 25] In the context of
supply networks, this concept not only increases transparency but also allows for
innovative, decentralised planning and coordination approaches for global material
flows.
A promising technology for solving security-related risks is the blockchain
technology as it constitutes the foundation of trust-free economic transactions based
on its unique technological characteristics. [26] This innovative technology refers to
a distributed database for capturing and storing a consistent and immutable event log
of transactions between network actors. In a supply network, all actors have access
to the history of transactions. As the current blockchain technology not only
processes financial transactions but can also ensure compliance of transactions with
programmable rules in the form of smart contracts, a high potential for risk
prevention in supply networks may be assumed. [27]
The following discussion of the impacts of these technologies on the risk management
in supply networks and their benefits follows the phases of the risk management cycle. Based
on these phases and considering the different types of risks in supply networks, selected
application examples are discussed in detail.
Risk Identification and Monitoring
Identification and monitoring of material flow risk can be enhanced by using sensors,
RFID tags and embedded systems in order to continuously track materials and products
within the supply network. Additionally, data from external service providers like insurance
companies need to be integrated because external events, such as political unrests, disasters or
strike calls, may significantly disturb or even disrupt the material flow in global supply
networks. These sources generate a vast amount of streaming data that needs to be monitored,
processed and shared in real time throughout the supply network. This can be achieved by
utilising in-memory computing systems that are capable of constant monitoring of data
streams based on certain rules or even complex algorithms. In case of an exception, an alert
can be issued or certain processes within the supply network can be initiated. [28]
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Financial flow risk may be divided into a systematic and an unsystematic risk. The
systematic financial flow risk is driven by generic external factors outside the network (e.g.
economic fluctuations and crisis). Thus, monitoring this risk requires the consideration of
external data coming from different sources like financial markets, banks, and rating agencies.
Due to the fluctuating dynamics of systematic financial flow risk, a fast identification of
relevant risks is crucial, requiring processing of this data in real time. Unsystematic financial
flow risk refers to the partners engaged within the supply networks. Changes in their financial
stability and performance need to be detected early by monitoring data coming from credit
rating agencies but also additional data, e.g. the data from social media. By implementing
statistical algorithms, early warnings on the financial perspective can be generated. [29]
Information flow risk threatens the key capability of a supply network to deliver the
relevant information to respective decision makers on time due to delayed or disrupted data
transmission. An early detection of missing or distorted data may be achieved by using inmemory-based streaming analytics. Data streams coming from different sources inside and
outside the networks are processed and verified in real time based on rules or concepts such as
machine-learning. [30]
Risk Assessment
Traditional approaches and IT-systems cannot be used in in the suggested enhanced risk
management framework due to the volume, variety and velocity of data that needs to be
considered. Rather, innovative Big Data technologies need to be implemented in order to
increase effectiveness and efficiency of risk management in supply networks by calculating
automatically the probability of occurrence, the impact and the level of risks.
This is achieved by combining data-oriented stochastic approaches and deterministic
models. The stochastic prediction is based on data acquired from various internal and external
sources by applying statistical and mathematical algorithms in order to assess the risk
probability. These results are then processed in a driver-oriented model that calculates the
impact of the risk on certain key performance indicators, such as delivery time, credit score,
and return on investment, based on defined cause-effect chains. This leads to a comprehensive
risk level assessment. [31]
By applying in-memory computing, processing of data streams can be automated and
executed in real time. This allows immediate reactions to identified high-level risks.
Additionally, a sensitivity and what-if analysis can be carried out leading to a more thorough
and in-depth risk assessment.
Risk Mitigation and Prevention
After having identified and assessed potential risks in the supply network, Big Data can
help to eliminate or reduce the probability of occurrence and/or the impact of an identified
risk.
In-memory computing significantly reduces a material flow risk by enabling the fast
execution of the Manufacturing Resource Planning (MRP) process. Previously, the necessary
calculations and process steps had to be done using batch processing in an enterprise resource
planning system or an advanced planning system. With the application of in-memory
computing, the MRP run can be triggered without time delays, potential material flow risks
coming from e.g. a changing sales forecast; in addition, irregularities or interruptions in the
material flow can be better managed. [32] Mobile devices together with advanced analytics
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allow timely recognition of disturbances or disruptions in the logistics chain and consequently
preventive or corrective actions to secure supplies, e.g. by redirecting transport flows. Thus,
rapid reactions to deviations or failures in every single stage of the supply chain are possible.
[15]
Advanced analytics can also help mitigate or prevent a financial flow risk as it uses
statistical methods and machine-learning techniques to support decisions on accepting or
rejecting a customer or a partner, increasing or decreasing the loan value, interest rate or term.
The speed and accuracy of decisions represent a major benefit of innovative approaches to the
management of financial flow risk. Additionally, these approaches and techniques can also be
used for automated risk mitigation actions like forward exchange transactions.
Mitigation and prevention of information flow risk can be achieved by using IoT-based
standard services. Other than traditional interfaces, these standard services and protocols
allow the flexible integration of different IT-systems within the supply network. By
combining the IoT services with in-memory systems, information can be shared in real time
among all relevant partners. Real-time monitoring of the data flows within the network allows
us to prevent and mitigate information flow risk.
Security aspects, such as unauthorized access and manipulation of data within the
network, can be addressed with the application of blockchain technology. Typical use cases
for this technology are smart contracts which use computerised transaction protocols that
execute the terms of a contract. With this type of technology, common contractual conditions
and minimized exceptions, both malicious and accidental, can be achieved without the
support of trusted intermediaries. As no human interaction is needed, the information flow
risk can be significantly reduced. Other benefits of this technology are the reduction in fraud
losses, arbitration and enforcement costs and other transaction costs. [33, 34]
Incorporating these use cases and potential benefits into the theoretical framework
results in a comprehensive and detailed picture of how Big Data technologies can be applied
in order to enhance risk management in supply networks:
Fig. 3 Use cases for Big Data-enhanced risk management
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7. Factors Influencing the Use of Big Data Technologies
Having discussed potential benefits of Big Data technologies in the area of risk
management, factors that may influence or challenge the application of these technologies
also need to be considered. Although the nature and extent of these factors are highly specific
to the respective use case, some general aspects are discussed below as they are relevant for
the implementation in business practice.
a) Big Data usually leads to a massive and continuous data flow. However, the purpose
of Big Data-enhanced risk management is not to end up with the largest mass of
data. Rather, it aims at utilizing data to improve and accelerate decisions as well as
processes in the entire risk management cycle. Therefore, the company-specific
objectives for Big Data-enhanced risk management have to be defined first.
Subsequently, concrete use cases need to be derived which then determine the
analytical requirements such as the amount and type of data and consequently, the
required data sources. Also, technical aspects like data storage or questions like how
often data should be loaded or whether it is necessary to have all data available in
real time can be addressed adequately. [35]
b) Additionally, achieving and maintaining the quality of required data is becoming a
constant challenge. This applies especially to external data which can be incorrect,
inconsistent, redundant, distorted or biased. Traditionally, many companies have
tried to improve data quality by establishing a golden record representing a single
source of truth. However, most of the time this is too difficult or time-consuming in
a Big Data environment. Thus, companies need to apply the notion of “fit for
purpose” as the overarching principle with regards to data quality. Based on this
principle, they can determine which data quality processes to run and whether to
apply processes for data validation, enhancement or enrichment as well as the tools
used in these processes (e.g. statistical methods or artificial intelligence techniques).
[36]
c) Big Data-enhanced risk management potentially involves hundreds of variables and
parameters. Incorporating all data into the driver-oriented predictive models for
assessment, mitigation and prevention of risk would lead to overfitting and result in
a bad prediction performance. A possible solution consists of applying sensitivity
analysis in order to evaluate the susceptibility of the applied models and the
resulting key performance indicators to changes in input parameters. Based on the
results of the sensitivity analysis, the weighting factor for the most influential
parameters is increased while irrelevant data is removed from the models, resulting
in gradually refined models, and consequently in an enhanced quality and accuracy
of the generated predictions. [37]
d) Costs are another factor influencing the successful application of the Big Dataenhanced risk management framework introduced in this paper. Today, the required
technologies have reached a high degree of maturity and are commonly available at
a constantly decreasing cost. Thus, the hardware and software components are no
longer the main cost drivers. The major cost, however, is incurred by the operation
and overall management or the integration of Big Data into an existing IT
ecosystem. [38] Big Data has to be considered as an investment, therefore a critical
analysis of its benefits and corresponding costs is crucial.
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8. Conclusions
Rapid advancements in digital technologies have led to a constantly increasing rate of
digitisation in supply chains, supporting the establishment of complex and highly
interdependent supply networks. Besides the significant and widely undisputed advantages of
such collaborative approaches, supply networks are exposed to a broad and growing range of
internal and external risks. Thus, classical risk management approaches need to be enhanced.
Big Data technologies can set the basis for innovative approaches. In this paper, we have
examined the potential applications, benefits and major challenges of Big Data in the specific
context of risk management. For this purpose, we developed a conceptual framework that
addresses three major dimensions of Big Data-enhanced risk management: risk segments, risk
management phases, and technologies.
A detailed analysis followed the phases of the risk management cycle. For each phase,
possible application scenarios were identified and potential benefits were derived. Our
analysis revealed that Big Data technologies, especially in-memory computing, advanced
analytics, IoT-technologies, and blockchain have the potential for significant support for the
entire risk management process. However, this requires the comprehensive application of
these technologies throughout the entire network, which is still a long way to go in practice.
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