This article introduces the topics to be discussed in the latest issue of JNSLP. The author argues that although readers will walk away with a greater understanding of “Big Data” generally, there still remains a need for a greater structural understanding of the subject in order to harness its power and direct its applications for the benefit of national security and the protection of civil liberties.

This article is modeled on a panel discussion at the symposium regarding two hypothetical case studies:  the first about detection technologies related to facial recognition and Terahertz detection and the second about passenger name recognition information created by airlines to manage travel reservations.  Through this conversation, the panelists discuss the relationship of big data collection to Fourth Amendment jurisprudence, the implications of changing technology on the future of big data collections, and the privacy concerns associated with the increased use of these types of surveillance methods as related to law enforcement.

The author reflects on a symposium panel discussion on “Swimming in the Ocean of Big Data: National Security in the Age of Unlimited Information” that occurred before the Snowden disclosures. He analyzes the panel discussion in context of the time at which it occurred and compares it to what has become known since June 2013. The article then focuses on the path to reform, specifically by focusing attention on the strengths and weaknesses of data collection by both the public and private sectors.

In this article, the author argues that the premise of “big data” lies not in the amount of data that we can generate, collect or store, but in the ability to use data to make informed decisions. He explores both the needs of data analysis projects and how to apply these types of projects to real-world circumstances. The article concludes that whatever data systems are ultimately put in place, privacy and civil liberties should be a primary concern and not simply an afterthought; the author proposes methods that can be implemented during the design of big data programs in order to curtail privacy violations.