The idea of a fourth paradigm based on data-intensive scientific research has been credited to Jim
Gray, an influential software pioneer and Microsoft
Corp. researcher. 14 Gray argued that prior beliefs
weren’t necessary and that results were fully and
solely driven by what was found in the collected
data. Our interviews with managers of science- and
technology-based companies, however, made the
case that research should not be solely data-driven.
Rather than it being a fourth paradigm, we think of
data-intensive methods as supplemental to existing
methods — a way to expand dimensionality, discover potentially new relationships, and refine
theory. Clearly, data-intensive methods are important complements to experimentation, theoretical
models, computer modeling, and simulation because they take us into a realm beyond what such
methods are capable of today. Researchers just need
to be careful about how they use them.
Sen Chai is an assistant professor of management at
ESSEC Business School in Cergy-Pontoise, France.
Willy Shih is the Robert and Jane Cizik Professor of
Management Practice in Business Administration at
Harvard Business School in Boston. Comment on
this article at http://sloanreview.mit.edu/x/58227, or
contact the authors at email@example.com.
1. D. Simchi-Levi, “OM Research: From Problem-Driven
to Data-Driven Research,” Manufacturing & Service Operations Management 16, no. 1 (February 2014): 2-10.
2. C.M. Reinhart and K.S. Rogoff, “Growth in a Time of
Debt,” American Economic Review 100, no. 2 (May
3. T. Herndon, M. Ash, and R. Pollin, “Does High Public
Debt Consistently Stifle Economic Growth? A Critique of
Reinhart and Rogoff,” Cambridge Journal of Economics
38, no. 2 (March 2014): 257–279.
4. A. Hero and B. Rajaratnam, “Large Scale Correlation
Mining for Biomolecular Network Discovery,” technical
report no. 2015-02, Stanford Department of Statistics,
Stanford, California, January 2015; and C. Rudin, D. Dun-
son, R. Irizarry et al., “Discovery With Data: Leveraging
Statistics With Computer Science to Transform Science
and Society,” white paper, American Statistical Associa-
tion, Alexandria, Virginia, June 2014.
5. J. Aleksic, S.H. Carl, and M. Frye, “Beyond Library Size:
A Field Guide to NGS Normalization,” June 19, 2014,
6. J.G. Lombardino and J.A. Lowe 3rd, “The Role of the
Medicinal Chemist in Drug Discovery — Then and Now,”
Nature Reviews Drug Discovery 3, no. 10 (October 2004):
7. Google discontinued this program after a failure that
missed the peak of the 2013 flu season by 140%; see,
for example, D. Lazar and R. Kennedy, “What We Can
Learn From the Epic Failure of Google Flu Trends,”
October 1, 2015, www.wired.com.
8. L.R. Rabiner, “A Tutorial on Hidden Markov Models and
Selected Applications in Speech Recognition,” Proceed-
ings of the IEEE 77, no. 2 (February 1989): 257-286.
9. For a more extensive discussion of this topic, see, for
example, G. Hinton, L. Deng, D. Yu et al., “Deep Neural
Networks for Acoustic Modeling in Speech Recognition:
The Shared Views of Four Research Groups,” IEEE Signal
Processing Magazine 29, no. 6 (November 2012): 82-97;
X.-W. Chen and X. Lin, “Big Data Deep Learning: Chal-
lenges and Perspectives,” IEEE Access 2 (May 2014):
514-525; and L. Deng and N. Jaitly, “Deep Discriminative
and Generative Models for Pattern Recognition,” chap.
1. 2 in “Handbook of Pattern Recognition and Computer
Vision,” 5th ed., ed. C.H. Chen (Singapore: World Scientific
10. C. Zhang, “Madden-Julian Oscillation,” Reviews of
Geophysics 43, no. 2 (June 2005): 1-36.
11. P. Neilley, interview with authors, Aug. 17, 2015.
12. P.E. Grafton and D.R. Stome, “Analysis of Axisym-
metrical Shells by the Direct Stiffness Method,” AIAA
Journal 1, no. 10 (1963): 2342-2347; and M.J. Turner,
R. W. Clough, H.C. Martin, and L.J. Topp, “Stiffness and
Deflection Analysis of Complex Structures,” Journal of
the Aeronautical Sciences 23, no. 9 (September 1956):
13. P.A.M. Dirac, “Quantum Mechanics of Many —
Electron Systems,” Proceedings of the Royal Society
A 123, no. 792 (April 6, 1929): 714-33.
14. T. Hey, S. Tansley, and K.M. Tolle, eds., “The Fourth
Paradigm: Data-Intensive Scientific Discovery” (Microsoft
Research, Redmond, Washington, 2009).
i. Hey, Tansley, and Tolle, “The Fourth Paradigm.”
Reprint 58227. For ordering information, see page 4.
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All rights reserved.
We think of data-intensive methods as supplemental
to existing methods — a way to expand dimensionality,
discover potentially new relationships, and refine theory.