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22 computer Published by the IEEE Computer Society 0018-9162/13/$31.00 © 2013 IEEE
GUEST EDITORS’ INTRODUCTION
Katina Michael, University of Wollongong
Keith W. Miller, University of Missouri–St. Louis
We can live with many of the uncertainties
of big data for now, with the hope that its
benefits will outweigh its harms, but we
shouldn’t blind ourselves to the possible
irreversibility of changes—whether good
or bad—to society.
It’s no secret that both private enterprise and govern-
ment seek greater insights into people’s behaviors and
sentiments. Organizations use various analytical tech-
niques—from crowdsourcing to genetic algorithms to
neural networks to sentiment analysis—to study both
structured and unstructured forms of data that can aid
product and process discovery, productivity, and policy-
making. This data is collected from numerous sources
including sensor networks, government data holdings,
company market lead databases, and public profiles on
social networking sites.
Although data mining in one form or another has
occurred since people started to maintain records in the
modern era, so-called big data brings together not only
large amounts of data but also various data types that
previously never would have been considered together.
These data streams require ever-increasing processing
speeds, yet must be stored economically and fed back into
business-process life cycles in a timely manner.
NEW OPPORTUNITIES
Since the Internet’s introduction, we’ve been steadily
moving from text-based communications to richer data
that include images, videos, and interactive maps as well
as associated metadata such as geolocation information
and time and date stamps. Twenty years ago, ISDN lines
couldn’t handle much more than basic graphics, but
today’s high-speed communication networks enable the
transmission of storage-intensive data types.
For instance, smartphone users can take high-quality
photographs and videos and upload them directly to social
networking sites via Wi-Fi and 3G or 4G cellular networks.
We’ve also been steadily increasing the amount of data
captured in bidirectional interactions, both people-to-
machine and machine-to-machine, by using telematics
and telemetry devices in systems of systems. Of even
greater importance are e-health networks that allow for
data merging and sharing of high-resolution images in
the form of patient x-rays, CT scans, and MRIs between
stakeholders.
Advances in data storage and mining technologies
make it possible to preserve increasing amounts of data
generated directly or indirectly by users and analyze it
to yield valuable new insights. For example, companies
can study consumer purchasing trends to better target
marketing. In addition, near-real-time data from mobile
phones could provide detailed characteristics about
shoppers that help reveal their complex decision-making
processes as they walk through malls.1
Big Data: New
Opportunities
and New
Challenges
JuNe 2013 23
Big data can expose people’s hidden behavioral patterns
and even shed light on their intentions.2 More precisely,
it can bridge the gap between what people want to do
and what they actually do as well as how they interact
with others and their environment.3 This information is
useful to government agencies as well as private compa-
nies to support decision making in areas ranging from
law enforcement to social services to homeland security.
It’s particularly of interest to applied areas of situational
awareness and the anticipatory approaches required for
near-real-time discovery.
In the scientific domain, secondary uses of patient
data could lead to the discovery of cures for a wide range
of devastating diseases and the prevention of others.4
By revealing the genetic origin of illnesses, such as
mutations related to cancer, the Human Genome Project,
completed in 2003, is one project that’s a testament to
the promises of big data. Consequently, researchers are
now embarking on two major efforts, the Human Brain
Project (EU; www.humanbrainproject.eu/vision.html)
and the US BRAIN Initiative (www.whitehouse.gov/the-
press-office/2013/04/02/fact-sheet-brain-initiative), in
a quest to construct a supercomputer simulation of the
brain’s inner workings, in addition to mapping the activity
of about 100 billion neurons in the hope of unlocking
answers to Alzheimer’s and Parkinson’s. Other types of
big data can be studied to help solve scientific problems
in areas ranging from climatology to geophysics to
nanotechnology.
NEW CHALLENGES
While big data can yield extremely useful information,
it also presents new challenges with respect to how much
data to store, how much this will cost, whether the data
will be secure, and how long it must be maintained.
For example, both companies and law enforcement
agencies increasingly rely on video data for surveillance
and criminal investigation. Closed-circuit television (CCTV)
is ubiquitous in many commercial buildings and public
spaces. Police cars have cameras to record pursuits and
traffic stops, as well as dash-cams for complaint handling.
Many agencies are now experimenting with body-worn
video cameras to record incidents and gather direct
evidence from a crime scene for use in court, obviating
the need for eyewitness versions of events.5 Taser guns
also now come equipped with tiny cameras. Because all
of these devices can quickly generate a large amount of
data, which can be expensive to store and time-consuming
to process, operators must decide whether it is more cost-
effective to let them run continuously or only capture
selective images or scenes.
Big data also presents new ethical challenges.
Corporations are using big data to learn more about
their workforce, increase productivity, and introduce
revolutionary business processes. However, these
improvements come at a cost: tracking employees’ every
move and continuously measuring their performance
against industry benchmarks introduces a level of oversight
that can quash the human spirit. Such monitoring might be
in the best interest of a corporation but is not always in the
best interest of the people who make up that corporation.
In addition, as big multimedia datasets become
commonplace, the boundaries between public and private
space will blur. Emerging online apps will not only enable
users to upload video via mobile social networking but
will soon incorporate wearable devices in the form of a
digital watch or glasses to allow for continuous audiovisual
capture. People will essentially become a camera.6 This
publicly available data will dwarf that generated by today’s
CCTV cameras.
However, unlike surveillance cameras, smartphones
and wearable devices afford no privacy protection to
innocent bystanders who are captured in a video at the
right place at the wrong time. For example, in the wake
of the recent Boston bombings, images of several people
photographed at the scene were mistakenly identified as
suspects on social media sites.
In fact, one of the major challenges of big data is
preserving individual privacy. As we go about our everyday
lives, we leave behind digital footprints that, when
combined, could denote unique aspects about ourselves
that would otherwise go unnoticed, akin to digital DNA.7
Examples include our use of language and punctuation
in blog and forum posts, the clothes we wear in different
contexts, and the places we frequent—do we spend our
Sunday mornings outdoors playing sports, indoors online,
visiting friends, attending religious services, or cruising a
bad part of town? Something as innocuous as when and
how we use energy in our homes reveals many details
about us.8 Outside our homes, drones could well be used
for ad hoc monitoring, spotting unusual changes in land
use patterns and feeding data back to operation centers
about emergencies.
Big data analytics will draw on aspects of our home,
work, and social lives to make assumptions beyond typical
“market segmentations” and delve deep into ontological
questions such as, “Who are you?” This has metaphysical
implications. For example, people will consciously alter
their online activity, and will modify their behavior in
surveilled spaces, to protect their privacy. Big data will
change how we live in both small and large ways. Are
we on a trajectory toward an uberveillance society?
Big data will change how we live in both
small and large ways.
24 computer
GUEST EDITORS’ INTRODUCTION
Will pervasive and ubiquitous computing converge with
underlying network infrastructure providing uber-views
using advanced data analytics for convenience, care, and
control purposes?9
Finally, many big data applications will have unintended
and unpredictable results as the data scientist seeks to
reveal new trends and patterns that were previously
hidden. For example, genetic screening could reveal the
likelihood of being predisposed to an incurable disease
like Alzheimer’s that leads to long-term anxiety about the
future, such as being ineligible for life insurance. Likewise,
technotherapeutics could assist elderly patients in one way
but assert unhealthy controls on others.10
We can live with many of these uncertainties for now
with the hope that the benefits of big data will outweigh
the harms, but we shouldn’t blind ourselves to the possible
irreversibility of changes—whether good or bad—to society.
IN THIS ISSUE
Members of the IEEE Society for Social Implications
of Technology are actively engaged in exploring big data
developments and their social and ethical implications. This
special issue presents some of the subjects important to SSIT.
The five articles we selected represent perspectives from
diverse interests from both operational and nonoperational
stakeholders in the big data value chain.
Jess Hemerly provides us with an overview of public
policy considerations for a data-driven future. Hemerly, a
public policy and government relations analyst at Google,
emphasizes the need to tread carefully in the regulation
of data flows so as not to adversely impact innovation
stemming from the data sciences.
Paul Tallon addresses the need for big data governance by
positing that data does have a measurable economic value
and that there are technical, reputational, and economic
risks to manage. Tallon also presents an important
discussion on the cost of big data to organizations.
Jeremy Pitt and his coauthors write on the need to
understand big data within the context of collective
awareness, as a smart grid infrastructure can have
a positive impact on societal transformation toward
sustainability. The authors argue that computational
management of common-pool resources requires a new
approach—institution science.
Marcus Wigan and Roger Clarke are more circumspect
about the role of big data in society, pointing to the fact
that underlying problems have been in existence since the
inception of automated computers. Instead, the authors
point to the consequences of big data, including legality,
data quality, disparate data meanings, and process quality,
as just a few of the bigger issues needing attention.
Finally, we include a case study on the hopes of big
data in the health informatics space in an article written
by Carolyn McGregor. This article focuses on discovery
and the future possibilities that monitoring real-time
physiological characteristics of humans may afford to
health and well-being.
We need improved powers of discernment, as
well as verifiable proof, to better understand big
data’s opportunities and risks. It will unquestion-
ably become an integral part of our society, used in both
commercial and government applications. Our challenge
will be to maximize the benefits of big data while minimiz-
ing its harms. We hope that this special issue of Computer
inspires readers to help meet this increasingly important
challenge.
References
1. K. Michael and R. Clarke, “Location and Tracking of Mobile
Devices: Überveillance Stalks the Streets,” Computer Law
& Security Rev., vol. 29, 2013, pp. 216-228.
2. R. Abbas, “The Social Implications of Location-Based
Services: An Observational Study of Users,” J. Location-
Based Services, vol. 5, nos. 3-4, 2011, pp. 156-181.
3. J. Pitt, “Design Contractualism for Pervasive/Affective
Computing,” IEEE Technology and Society Magazine, vol.
31, no. 4, 2012, pp. 25-28.
4. E. Strickland, “The Gene Machine and Me,” IEEE Spectrum,
Mar. 2013, pp. 26-32.
5. A. Hayes, “Cyborg Cops, Googlers and Connectivism,”
IEEE Technology and Society Magazine, vol. 32, no. 1, 2013,
pp. 23-24.
6. S. Mann, “Through the Glass, Lightly,” IEEE Technology and
Society Magazine, vol. 31, no. 3, 2012, pp. 10-14.
7. K. Michael and M.G. Michael, “The Social and Behavioural
Implications of Location-Based Services,” J. Location-Based
Services, vol. 5, nos. 3-4, 2011, pp. 121-137.
8. F. Sestini, “Collective Awareness Platforms: Engines for
Sustainability and Ethics,” IEEE Technology and Society
Magazine, vol. 31, no. 4, 2012, pp. 54-62.
9. M.G. Michael and K. Michael, “Towards a State of
Uberveillance,” IEEE Technology and Society Magazine,
vol. 29, no. 2, 2010, pp. 9-16.
10. M. Gagnon, J.D. Jacob, and A. Guta, “Treatment Adherence
Redefined: A Critical Analysis of Technotherapeutics,”
Nursing Inquiry, vol. 20, no. 1, 2013, pp. 60-70.
Katina Michael is an associate professor in the School of
Information Systems and Technology at the University of
Wollongong, New South Wales, Australia. Her research fo-
cuses on emerging technologies as well as national security
technologies and their corresponding social implications.
Michael received a PhD in information and communica-
tion technology from the University of Wollongong. She is a
senior member of IEEE. Contact her at katina@uow.edu.au.
Keith W. Miller is the Orthwein Endowed Professor for
Life-Long Learning in the Sciences at the University of
Missouri–St. Louis. His research interests include software
testing and computer ethics. Miller received a PhD in com-
puter science from the University of Iowa. He is a member
of IEEE and ACM. Contact him at kmill2@uis.edu.
