Systems, Man & Cybernetics - April 2015 - 29

◆ Acquisition: Big Data does not imply a big/complete

understanding of an underlying problem, Big Data
quantity does not imply Big Data quality, and a Big
Data sample does not imply a representative-or even
complete-sample.
◆ Access: Big Data's on-demand accessibility may create privacy or "big-brother" concerns, its real-time
abilities may obscure past and future concerns, and
its cybersecurity concerns may overlook personalsecurity concerns.
◆ Analytics: Big Data's inherent messiness may
obscure underlying relationships, its correlational findings may result in unintended causal consequences, and its unleashing of information may
obscure underlying truths.
◆ Application: Big Data's feasible explanations may
obscure more probable explanations, its evidencedriven findings may obscure underlying factual knowledge, and its subjective, consumer-centric findings
may obscure simpler, objective findings.
Other concerns include surveillance by autocratic
governments and the processing of data in an increasingly unfocused, unproductive, and generally "shallow"
manner [1]. Moreover, even Google's vaunted flu-prediction algorithm (which, in 2009, was able to predict
and locate the spread of H1N1 flu on a near real-time
basis) failed in 2012. It was predicting more than double
the proportion of doctor visits for influenza-like illness
than the U.S. Centers for Disease Control and Prevention (which bases its estimates on a survey of clinics
located throughout the United States). Of course, potential Big Data concerns or problems can be mitigated with
thoughtful and effective approaches and practices; for
example, legislation could be promulgated and passed
to forbid the invasion of privacy and to dispense severe
sanctions against those who break the law or who knowingly publish false findings.

environmental, economic, and other criteria to enhance
the decision making process when designing, implementing, operating, and maintaining a system or system of
systems to meet societal needs in a fair, ethical, and sustainable manner throughout the system's life cycle.
As indicated in the "History" column in the January
2015 issue of IEEE Systems, Man, and Cybernetics
Magazine [7], the IEEE SMC Society has a great future;
its SMC methods are relevant for addressing challenging
problems arising in system domains that are becoming
dominant in this century. However, the methods must
be refined and expanded to meet the changing needs of
the 21st century from a system to a system-of-systems
vision, from a disciplinary to a multidisciplinary outlook, from a mass-production to a mass-customization
focus, from a steady-state to a real-time perspective,
from a causational to a correlational approach, and
from an optimal to an adaptive approach.
About the Author
James M. Tien (jmtien@miami.edu) earned his S.M., E.E.,
and Ph.D. degrees from the Massachusetts Institute of Technology and his B.E.E. degree from Rensselaer Polytechnic
Institute. An elected member of the prestigious U.S. National Academy of Engineering (NAE), he joined the University
of Miami as a distinguished professor and dean of its College of Engineering in 2007. He has held elected leadership
positions in the NAE (Peer Committee chair, section chair),
the IEEE (Society president, vice president of the Publication Services and Products Board, vice president of the Educational Activities Board, member of the Board of Directors), and INFORMS (section chair, membership chair). He
was recently awarded the 2010 Richard M. Emberson Award
for his "vision and leadership in advancing IEEE's global visibility and recognition as an innovator in technical, publication, and educational services."
References

Big Data and SMC
Nevertheless, it should be noted that Big Data is a disruptive innovation that should be embraced by SMC
practitioners, consistent with the SMC foci advanced
by Hipel et al. [2]. Big Data is an essential tool that can
deal with the complex, modern systems that are becoming increasingly human centered, if not human focused,
with goods and services becoming more personalized or
customized. The implication in regard to SMC methods
is profound; such methods must truly be multidisciplinary-they must include techniques from the social
sciences (i.e., sociology, psychology, and philosophy)
and management (i.e., organization, economics, and
entrepreneurship). In essence, SMC must become an
integrative, adaptive, and multidisciplinary approach
to creative problem solving that considers the stakeholders' value systems and satisfies important societal,

[1] N. Carr, The Shallows: What the Internet Is Doing to Our Brains. New York: Norton, 2010.
[2] K. W. Hipel, M. M. Jamshidi, J. M. Tien, and C. C. White, "The future of systems, man
and cybernetics: Application domains and research methods," IEEE Trans. Syst. Man
Cybern., vol. 37, no. 5, pp. 726-743, 2010.
[3] N. N. Taleb, The Black Swan: Second Edition. New York: Random House, 2010.
[4] J. M. Tien, "Toward a decision informatics paradigm: A real-time information
based approach to decision making," IEEE Trans. Syst. Man Cybern., vol. 33, no. 1, pp.
102-113, 2003.
[5] J. M. Tien, "The next industrial revolution: Integrated services and goods," J. Syst.
Sci. Syst. Eng., vol. 21, no. 3, pp. 257-296, 2012.
[6] J. M. Tien, "Overview of big data: A US perspective," The Bridge, vol. 44, no. 4, pp.
12-19, 2014.
[7] J. M. Tien, "My perspectives as a past president," IEEE. Syst. Man Cybern. Mag.,
vol. 1, no. 1, pp. 17-20, Jan. 2015.

Ap ri l 2015

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