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Home > Open Access Journals > JSS > Vol. 9 > No. 2 (2016)

 

Author Biography

Michael Landon-Murray is an Assistant Professor in the School of Public Affairs (SPA) at the University of Colorado, Colorado Springs. He completed a Ph.D. in Public Administration and Policy at the University at Albany (SUNY), a Master’s of Public and International Affairs at the University of Pittsburgh, and a BA in Political Science at the University at Buffalo (SUNY). Dr. Landon-Murray is also a graduate of the Federal Bureau of Investigation’s Citizens Academy. Before coming to SPA, Dr. Landon-Murray was a Visiting Assistant Professor at the University of Texas at El Paso’s National Security Studies Institute. He can be reached at mlandonm@uccs.edu.

DOI

http://dx.doi.org/10.5038/1944-0472.9.2.1514

Abstract

The potential for big data to contribute to the US intelligence mission goes beyond bulk collection, social media and counterterrorism. Applications will speak to a range of issues of major concern to intelligence agencies, from military operations to climate change to cyber security. There are challenges too: procurement lags, data stovepiping, separating signal from noise, sources and methods, a range of normative issues, and central to managing these challenges, human capital. These potential applications and challenges are discussed and a closer look at what data scientists do in the Intelligence Community (IC) is offered. Effectively filling the ranks of the IC’s data science workforce will depend on the provision of well-trained data scientists from the higher education system. Program offerings at America’s top fifty universities will thus be surveyed (just a few years ago there were reportedly no degrees in data science). One Master’s program that has melded data science with intelligence is examined as well as a university big data research center focused on security and intelligence. This discussion goes a long way to clarify the prospective uses of data science in intelligence while probing perhaps the key challenge to optimal application of big data in the IC.

Acknowledgements

I would like to thank Stephen Coulthart for his generous input while developing this project. I'm also deeply appreciative of the feedback from the two anonymous reviewers.

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