Alex Littleton

Data aggregation is the act of linking data with other users to analyze trends and track user behavior. Data mining refers to extracting data from user activities to create a profile of individual people (Gilliom and Monahan, 2013). As with all methods of surveillance, data aggregation and mining can have some serious implications surrounding privacy. Data aggregation and mining deal directly with obtaining user data through methods of surveillance through monitoring credit card transactions, internet search history, social media use, etc. and linking that data with other users to track their behavior. This could result in an organization or company having power over others, particularly when companies obtain individuals data in order to try and sell products and services to them (Gilliom and Monahan).

Data aggregation and mining is present in our everyday lives. For example, the tech company Kinsia makes smart-thermometers that sync up to users’ smartphones and track fevers and symptoms. This product has become popular to parents with young children. Kinsia aggregated the data it collected from users and sold it Clorox. Clorox then targeted ZIP codes around the country with an increase in fevers with advertisement for products such as disinfecting spray or wipes (Maheshwari, 2018). Another example of data aggregation and mining would include Acxiom. Acxiom is a data aggregator that has data on each and every one of us such as our retail interests, credit score, clothing sizes, income, race, and even our address (Gilliom and Monahan, 2013). Acxiom sells this data to interested companies which then target us as potential consumers with advertisements.
Al-Saggaf and Islam (2015) explore the potential danger that data mining could have regarding social networking sites. Data mining algorithms can derive private information about individuals from social networking sites (Al-Saggaf and Islam). However, data aggregation and mining can prove to be very useful as well. For example, in the smart agriculture industry, data aggregation is being used to make farms more cost-effective which benefits consumers and farmers. The smart agriculture industry along with many other industries could benefit immensely in the near future (Smart, 2018). It is important to remember the power data aggregation and mining gives to those who control our data.


Al-Saggaf, Y., & Islam, M. (2015). Data mining and privacy of social network sites’ users: Implications of the data mining problem. Science & Engineering Ethics, 21(4), 941-966. https://doi.org/10.1007/s11948-014-9564-6

Gilliom, J., & Monahan, T. (2013). SuperVision: An introduction to the surveillance society. Chicago, IL: University of Chicago Press.

Maheshwari, S. (2018, October 23). This thermometer tells your temperature, then tells firms where to advertise. The New York Times. Retrieved from https://www.nytimes.com/2018/10/23/business/mediafever-advertisements-medicine-clorox.html

Smart agriculture market is likely to register a 13.50% CAGR between 2017 and 2025, projects TMR. (2018, October 23). Digital Journal. Retrieved from http://www.digitaljournal.com/pr/3992890


Icon for the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Key Concepts in Surveillance Studies Copyright © 2019 by Alex Littleton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

Share This Book