The article “Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject” was written by academics Nick Couldry — a Professor of media, communications, and social theory — and Ulises A. Mejias, an associate professor of communication studies. This article was published in 2019 in the Television & New Media journal Vol 20, Issue 4.
Couldry and Mejias argue that the current process of data extraction and appropriation is better understood through the history of colonialism. By using colonialism as a frame to understand the new social, economical and political processes that make the extraction and exploitation of data possible, the authors hope to find ways of resisting this new distinctive stage of capitalism, thus protecting the integrity of human life and our own sense of selfhood. Although I think Couldry and Mejias’s argument is convincing, I want to read a more in-depth development of their argument about data’s colonized subjects. It is important to develop how data colonialism affects different people based on race, sexuality, gender and geographical location.

The article begins by describing the problem with the current data landscape. The authors propose to use colonialism as a framework to understand the way exploitation of human beings through data is normalized, just like historical colonialism normalized the exploitation of human beings through the exploitation of natural resources and labour. Daily life is being transformed into a data stream. This is achieved by a process called data relations: “new types of human relations which enable the extraction of data for commodification” (337). By drawing parallels between historic and data colonialism the authors explain that as historical colonialism was fundamental to industrial capitalism, this new data colonialism will lead us to a new stage of capitalism.

The authors identify four aspects of data colonialism that mirror historical colonialism: Resource appropriation, Ideology, corporate concentration of profits, and a shift of social relations. Resource appropriation refers to the capture of data which has been naturalized because it is seen as a natural resource ready to be exploited. This mirrors historic colonialism’s exploitation of resources that seemed cheap and available for exploitation, i.e. wood or oil.

The second part of this argument refers to an ideological change that is achieved through extractive relationalities in which the extraction becomes normalized in social, practical and political discourses. The authors explain that “Through this discursive move, the links of data back to a prior process of data collection (i.e.,appropriation) are obscured.” (340). This can be compared to historical colonialism in which the exploitation of humans and natural resources became normalized and proposed as a way to benefit humanity through civilization.
Corporate appropriation refers to the social quantification sector and includes corporations such as Google, Facebook, Baidu and Alibaba as well as social media platforms and firms that analyze and trade data. These actors operate in the new global centers of power: the United states and China, who benefit from the exploitation of data, both outside and inside their borders. These actors work in a network which generates profits by selling data to advertisers, governments and law enforcement agencies (340). This can be compared to historical colonialism, in which the exploitation of people and resources in the global south is commodified and used to generate profit for countries in the global north.

Finally, a shift in social relations is necessary to make data colonialism possible. Authors use Marx’s social theory to explain the process by which data becomes abstracted from the process of human life and is commodified to be sold. As big data takes over daily life, “everyday life will have become directly incorporated into the capitalist process of production.” (343).  The victims of this process are humans, who are now colonized subjects under data colonialism. Human relations become data relations that are commodified to create economic value. In this framework all human activity becomes trackable, measurable and exploitable. The authors affirm that “A continuously trackable life is a dispossessed life, no matter how one looks at it. Recognizing this dispossession is the start of resistance to data colonialism.” (345)
The article concludes explaining that data colonialism has a vision of totality, where subjects are categorized and societies are built towards algorithmic control. But this isn’t the only vision that is possible, data colonialism can be resisted by rejecting the appropriation and exploitation of data and the society structures that make it possible.

Couldry and Mejias’ argument is compelling, as it makes the exploitative nature of data extraction much clearer by drawing parallels with historical colonialism. My critique is that it is important to expand on the fact that data colonialism’s subjects are not all the same, some of them have privileges that others don’t. The authors state that “It is a real person who gets offered a favorable price in the supermarket, an opportunity and for social housing, or a legal penalty, all based on algorithmic reasoning.” (344) and also that “Even if, under data colonialism, we are all destined to become data subjects—that is, parties to regular data relations—what this means for one person may be very different from what it means for another.” (345) but they decide not to explain further.

Books such as ‘Weapons of Math destruction’ and ‘Algorithms of oppression’ explain that currently some people are even more surveilled, controlled and oppressed than others, especially people who are part of minoritized groups. Without this analysis the article feels incomplete because it paints an uniform picture of data colonialism’s subjects. It is worth reading the complete book to learn if the authors develop their arguments even further.
To conclude, I believe this article is a great introduction to the actual problems with big data, the general public and students in diverse areas such as computer science, philosophy, information and social sciences, etc. can get an understanding of how the current landscape of big data has many angles, and that we all can resist this new form of colonialism that threatens our lives, relationships, work and own sense of self.