Dataveillance is the observation, collection, and processing of data, whether on a personal or group scale. The term dataveillance comes from the work of surveillance theorist Roger Clarke, who proposed this term as a way of capturing the impact of data processing and information technology systems on personal or mass surveillance. What distinguishes dataveillance from analog surveillance is its reliance on the technical means of processing. The potential of dataveillance, as detailed in this entry, is visible in the corporate world, in national security applications, as well as in citizens’ demands for transparency. Therefore, dataveillance’s massive potential, dependent on computing power, is at once a tool for profit, an instrument of mass surveillance, and a technique for ensuring better governance. This entry first describes dataveillance, then examines its usage in the corporate world and by governments, and concludes by examining the possibility of discrimination and the ethically ambiguous nature of dataveillance.
Although data may be raw, their aggregation and processing through digital means can be very informative. This view of data and their processing has led authors to increasingly speak of data-doubles—the collection of data that we leave behind or have been stored about us. When aggregated or processed, data-doubles produce a noncorporeal image of our lives that the data controller can use to tailor goods and services to a person or to assign the person to one category (e.g., citizens, consumers) or another. The monitoring of personal credit histories, the sharing of passenger travel data between states, and the tracking of power usage through smart home technologies are all examples of how data automation and digital processing can be put to work for surveillance.
Dataveillance is now extensively used in the corporate world, not only for efficiency gains but also for better profiling of customers. For instance, businesses are keen to use radio-frequency identification tags on products, in place of product codes, to track individual items through a supply chain. Businesses also track people as well as goods, and dataveillance has emerged as an important way for free online and off-line services to derive sustainable revenue from the use of personal data. In fact, this monetization of personal data has become a central business model on the Internet. Social networking sites, leveraging the personal information and usage habits of their users, analyze these data with the express goal of providing relevant targeted advertising to their users. While online social networking is one of the more recent models of dataveillance, card-based consumer loyalty programs have consistently made use of mass collection of data to categorize consumer tastes for client businesses, rewarding their users with redeemable points. Companies also increasingly derive revenue from selling a range of devices that cater to surveillance of one’s own data (e.g., fitness trackers, heart monitors, step counters, mobile applications that track sleep patterns).
As the types of data that can be collected continue to increase in line with the sophistication of sensory technologies—including factors such as gait recognition and online click patterns—so does the potential for discrimination. This has raised concerns about what David Lyon calls “social sorting,” the tendency of surveillance to facilitate or create new forms of categorization. The use of databases is central to the operation of data harvesting and use, and as computer processing power, storage capacity, and software complexity have increased, so have the positive and negative impacts of digitally mediated surveillance.
One of the distinguishing elements of dataveillance is the sheer capacity to process raw data into information or actionable knowledge that digital computing capacity has enabled. As the sources of data, and their processing, have become more mobile, so has the potential space in which to exert dataveillance. The growing use of mobile phones produces not only a more connected population but also a large potential base of personal location data. The ability to automate the recording, uploading, and processing of images has enabled the creation of vast virtual worlds in mapping software, as well as facilitating the mass collection of information that was previously too tedious to massively collect, such as automobile license plate numbers. The ethically ambivalent nature of surveillance applies in the case of dataveillance, as this method enables large-scale tracking and profiling as much as it does the facilitation of mobility and better decision-making information.
In sum, dataveillance is a contested terrain. Some practices, such as the use of biometrics for tracking employee attendance patterns, reinforce corporate control. Others, such as government tax analysis databases, reinforce the state’s sovereign ability to see and control its territory. The use of data by citizens and consumers can create not only new forms of discrimination but also a demand for transparency. Dataveillance, though its potential grows in line with computing capacity, remains an ethically ambiguous practice.
Gemma Galdon Clavell
See also Big Data ; Passenger Data ; Social Sorting ; Transparency
Clarke, Roger. “Information Security and Dataveillance.” Communications of the ACM, v.31/5 (1988).
Degli Esposti, Sara. “When Big Data Meets Dataveillance: The Hidden Side of Analytics.” Surveillance & Society, v.12/2 (2014).
Trottier, Daniel. Social Media as Surveillance: Rethinking Visibility in a Converging World. Farnham, England: Ashgate, 2012.