Hostname: page-component-745bb68f8f-d8cs5 Total loading time: 0 Render date: 2025-01-12T10:48:35.374Z Has data issue: false hasContentIssue false
  • English
  • Français

I-Os in the Vanguard of Big Data Analytics and Privacy

Published online by Cambridge University Press:  17 December 2015

Adam J. Ducey ,
Nigel Guenole ,
Sara P. Weiner ,
Hailey A. Herleman ,
Robert E. Gibby  and
Tanya Delany
Adam J. Ducey*
Affiliation:
IBM, Hazelwood, Missouri
Nigel Guenole
Affiliation:
IBM Smarter Workforce Institute, London, United Kingdom
Sara P. Weiner
Affiliation:
IBM Smarter Workforce, Tucson, Arizona
Hailey A. Herleman
Affiliation:
IBM Smarter Workforce, Frisco, Texas
Robert E. Gibby
Affiliation:
IBM, Cincinnati, Ohio
Tanya Delany
Affiliation:
IBM, Milan, Italy
*
Correspondence concerning this article should be addressed to Adam J. Ducey, IBM, 325 James S. McDonnell Boulevard, Hazelwood, MO 63042. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 4 , December 2015 , pp. 555 - 563
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

EMC, & IDC. (2014, April). The digital universe of opportunities: Rich data and in the increasing value of the Internet of things. Retrieved from http://www.emc.com/leadership/digital-universe/2014iview/index.htmGoogle Scholar
Ferrucci, D., & Lally, A. (2004). UIMA: An architectural approach to unstructured information processing in the corporate research environment. Journal of Natural Language Engineering, 10 (4), 327–348.CrossRefGoogle Scholar
Gibby, R. E., McCloy, R. A., & Putka, D. J. (2013, April). Viewing linkage research through the lenses of current practice and cutting-edge advances. Workshop conducted at the 30th Annual Conference of the Society for Industrial Organizational Psychology, Houston, TX.Google Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8 (4), 491–508.CrossRefGoogle Scholar
Herrmann, D. S. (2007). Complete guide to security and privacy metrics: Measuring regulatory compliance, operational resilience, and ROI. Boca Raton, FL: Auerbach.Google Scholar
Navarro-Arribas, G., & Torra, V. (2015). Advanced research on data privacy in the ARES Project. In Navarrio-Arribas, G. & Torra, V. (Eds.), Advanced research in data privacy (pp. 3–14). Cham, Switzerland: Springer International.Google Scholar
Oswald, F. L., & Putka, D. J. (2015). Statistical methods for big data. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology. New York, NY: Routledge. Retrieved from http://www.researchgate.net/publication/271836790_Statistical_methods_for_big_data_A_scenic_tourGoogle Scholar
Ryan, J., & Herleman, H. A. (in press). A big data platform for workforce analytics. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology. New York, NY: Routledge.Google Scholar
Westin, A. F. (1967). Privacy and freedom. New York, NY: Atheneum.Google Scholar
Zikopoulos, P. C., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. New York, NY: McGraw-Hill.Google Scholar