Michel Foucault

Martin Heidegger

Sigmund Freud

Walter Benjamin

Immanuel Kant


We have choosen to display these critical thinkers in order to let people be 'up to date' whit what they are thinking and Tweeting in our modern day. Perhaps this is all part of the information society. A Term introduced by Manuell Castells. Jean-Francois Lyotard would call this the commodifiation of information.

“Data collection is at the core of contemporary digital marketing; individual consumers are tagged with unique identifiers when they engage with online services, and then tracked, profiled, and interactively targeted for personalized marketing and advertising as they navigate the Web” (Montgomery et al. 663). Especially with behavioural advertising, people are being tracked for advertising targeting using video, music, or other applications, which could be collected through diverse techniques (Montgomery et al. 664). That data collection happens at a large scale is pointed out by Borgesius (54). There is almost no escape for users not to be tracked. The collection goes even further that even every word typed in a search query box, even the video’s that have been downloaded or every word that has been typed in an email is data that a marketer can use for more customized advertisements (55). Eventually, firms will delete some data it has collected about the person’s online behaviour after assuming the person’s interests (65). The process of finding new information in datasets is called data mining, which can be analysed in numerous ways.
Data mining is the nontrivial process of using algorithmic techniques to discover (faster than humanly possible) hidden patterns and unknown relationships among many variables in masses of observed data to produce understandable, meaningful, and potentially useful information for knowledge building and decision-making (Chow-White and Sandy Green 4).

To put in another way, by analysing the data using specific designed software there is a possibility to find hidden connections among the different data. As Borgesius explains: “Software is used to analyse the data in order to find correlations, and these correlations can be unexpected.” (65).

It is essential for knowing how firms collect data, in order to point out that this data is very valuable. Hence, information and thus data is being made into a commodity. As has been stated in chapter 2, firms can sell copies of particular data and sell this to other firms. For instance data brokers are “companies that collect consumers’ personal information and resell or share that information with others” (Ramirez et al. i). In other words, firms could buy or retrieve information themselves and add it to the online profiles they have after which they could then resell or share the information to others. Data has become a product that will could be sold, like in an auction, to the highest bidder in real time hence Real Time Bidding (RTB). “In RTB, Ad Exchanges leverage Cookie Matching to broadcast user data to ad buyers” (Olejnik, Minh-Dung, and Castelluccia 1). These auctioning are automated processes where the buyer has the chance of showing is ad to a person. Other terms for this practice are RTB, ‘audience selling’ or ‘audience buying’ (Borgesius 71). Companies like Google, Yahoo, Microsoft, and Facebook have Ad Exchanges. “RTB enables the advertisers to give a bid for every individual impression” (Zhang et al. 1).

Data could also give power to firms over other firms if they decide not to sell data but merely rent the data, of which its content will not be visible to other firms (Borgesius 70). An example of this sort of power is advertising networks who do not sell any copies of their data. They merely show the ads on behalf of the firm (the advertiser), who will not receive (most of the time) a copy of the data (which could be in the form of profiles) (Borgesius 71). “This type of data disclosure could be seen as a modern version of list rental. With list rental, a list broker sends leaflets to a set of people, based on what it knows about those people. The advertiser does not receive a copy of the list” (71).
In addition, there is also the power that firms have over the user through the data. People do not are rarely have access to the (raw) data. Tim Berners-Lee has said in an interview with the Guardian about data mining: “If my computer understands all that, then it’s in a position to be very valuable to help me run my life, you know, to guess what I need next, to fill in a lot of the context . . . to guess what I want to read in the morning” (qtd. in Andrejevic 1673). The truth is many firms like for instance Facebook or Google do not give users access to the data they get. Andrejevic says in reaction to Berners-Lee that giving people power over their data does not really do anything because of the many discrepancies that re associated with the data (1674). As he puts it, there are different capacities needed for users in order to put the data in full use. He continues by saying: Even if users had access to their own data, they would not have the pattern recognition or predictive capabilities of those who can mine aggregated databases. Moreover, even if individuals were provided with everyone else’s data (a purely hypothetical conditional), they would lack the storage capacity and processing power to make sense of the data and put it to use (1674).

This means that even if people would have access to their data, they lack the knowledge (and perhaps money) to process and store the data and putting it to full use. For many firms like Facebook and Google this is no problem, because they do not lack the resources like money, space, and knowledge to process and store the data. Meaning they have through the data power over the user.

The researchers Boys and Crawford have observed a distinction in terms of data and power, and saw that on the one hand there are ‘the Big Data rich’ and ‘the Big Data poor’ (boyd and Crawford 674). Although they are talking about university researchers, the same thing is also applicable for the online advertising firms as has explained above. The Big Data rich are those who can purchase, store or generate large datasets such as companies (Facebook and Google). The Big Data poor are those who are “excluded from access to the data, expertise, and processing power” (Andrejevic 1675). This means that because there is a relative small group who is able to purchase, store, generate and trade data they have a large amount of power in the ecosystem. They will decide what, who and when the data will available. As McCue’s has said: “If knowledge is power, then foreknowledge [via predictive analytics] can be seen as battlespace dominance or supremacy” (qtd. in Andrejevic 1680).