Datafication

What does datafication mean?

Datafication (sometimes also referred to as datification) implies in a broad sense that something is turned into data. Data can be defined as “material produced by abstracting the world into categories, measures and other representational forms [...] that constitute the building blocks from which information and knowledge are created” (Kitchin 2014, p. 1).  

In theory, anything can be turned into data and historically, processes of datafication within the business world have been happening for a long time (Mejias & Couldry 2019). But in contemporary use, datafication refers specifically to the transformation of human life and social inter(action) into quantified data (van Dijck 2014). Datafication can be seen as combining two processes: “the transformation of human life into data through processes of quantification, and the generation of different kinds of value from data” (Mejias & Couldry 2019, p. 3). An example is the way in which clicks, likes, and comments on social media are collected and used to profile and personally target new content and suggested ads to a user.  

The term in this understanding was first introduced by Mayer-Schönberger and Cukier (2013) in response to the rise and increasing impact of big data. Since then, the concept has been adopted and spread widely by researchers.  

The process of datafication and the structures and actors involved

Mejias & Couldry (2019) suggest that the datafication process can be roughly divided into two main parts: data collection and value generation. Data collection can take many forms, but often it is done through smart devices, apps, or other platforms. They gather comprehensive data about users, collate and analyse the data and generate micro-targeted marketing data and predictive behavioural insights. The output generated by the platforms are then turned into value, often through monetization. Either it is used to sell targeted products or services to the user, or it is sold to other parties who wish to influence users towards various goals.  

Data flows within a variety of structures, including platforms, services, apps, databases, and hardware devices, and the actors involved in datafication range widely. There are the corporations, both the big ones that are responsible for a vast amount of the datafication in our lives, such as Google, Amazon, Microsoft, or Facebook (in the West) and the small ones, such as companies working with hardware, software, platforms, data analysis, spam etc. But there are also state actors, civic actors such as activists and journalists, and non-state actors such as terrorists and hackers, who all have the capacity to produce, collect and analyse data for different purposes (Mejias & Couldry 2019).  

Critical perspectives on datafication

Datafication practices have been criticised on several levels, for example, for commodifying human life through the digital footprint we leave behind and for being a form of digital labour that is not recognised nor rewarded (Mejias & Couldry 2019). Nowadays, we live in “a world of near-ubiquitous data collection” (Montgomery, 2015, p. 267) where behaviour, emotions, and relations are collectable, quantifiable, predictable, and monetised (Mascheroni 2018) and this also threatens the individual's fundamental right to self, especially the right to privacy and individual autonomy.  

Datafication has also been criticised for being a form of data surveillance (dataveillance) or surveillance capitalism (Zuboff 2015), as datafication enables a continuous monitoring of citizens' practices both in the public and private spheres (Mascheroni 2019), and as human experiences provide the material that creates the behavioural data used to influence and even predict our actions (Zuboff 2015).  

Furthermore, researchers have pointed out how the data collected can be misused for purposes other than those originally intended and used as a basis for discriminatory practices. Studies have shown that both corporations and state sectors, such as social services, use datafication to discriminate individuals particularly from disadvantaged classes and ethnic populations (cf. Eubanks 2017; Benjamin 2019; Mejias & Couldry 2019). 

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