Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology

By J. Dijck. 2014

Metadata and data have become a regular currency for citizens to pay for their communication services and security—a trade-off that has nestled into the comfort zone of most people. This article deconstructs the ideological grounds of datafication. Datafication is rooted in problematic ontological and epistemological claims. As part of a larger social media logic, it shows characteristics of a widespread secular belief. Dataism, as this conviction is called, is so successful because masses of people — naively or unwittingly — trust their personal information to corporate platforms. The notion of trust becomes more problematic because people’s faith is extended to other public institutions (e.g. academic research and law enforcement) that handle their (meta)data. The interlocking of government, business, and academia in the adaptation of this ideology makes us want to look more critically at the entire ecosystem of connective media.

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Citations in the corpus

On the genealogy of machine learning datasets: A critical history of ImageNet (2021, methodology): “Moreover, the focus on the mass accumulation of data from the web as part of new scienti ﬁ c methodologies is not unique to computer vision (Van Dijck, 2014; Kitchin, 2014).”
Intimate Surveillance: Normalizing Parental Monitoring and Mediation of Infants Online (2017, background): “As José van Dijck (2014) has noted, contemporary surveillance practices are predicated on a prevailing datafication taking place wherein all social activity is being tracked and digitized, becoming much sought after big data.”
Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments (2016, unknown): “It is taking place in a context in which ‘ data ﬁ cation ’ – the objective quanti ﬁ cation of all kinds of human behaviour and sociality to enable real-time tracking, monitoring and predictive analysis – has become a new paradigm in science and society (Van Dijck 2014, 198).”
Social Injustice in Surveillance Capitalism (2017, background): “…Peacock (2014) has called ‘unconscionable contracts’—in which the data subjects have no ability to negotiate the terms of the agreement and often insufficient knowledge of the full extent or legalities of personal data collection and use (Degli Esposti 2014; van Dijck 2014; Hoofnagle et al. 2010).”
The decolonial turn in data and technology research: what is at stake and where is it heading? (2021, background): “The decolonial turn treats the ideologies of ‘ dataism ’ (Van Dijck, 2014; Harari, 2016) with the utmost seriousness, but understands them in the extended historical perspective of colonialism, just as capitalism itself can best be understood through the additional lens of colonial relations.”
Heuristics of the algorithm: Big Data, user interpretation and institutional translation (2015, background): “…taking up Big Data as a heuristic – or if it is more a case of powerful institutions using the discourse or ‘myth’ of ‘Big Data’ (Boyd and Crawford, 2012; cf. Boellstorﬀ, 2013) to foster an almost religiously tainted ‘dataism’, as van Dijck (2014) calls this structure of belief (cf. Mager, 2012).”
‘Hypernudge’: Big Data as a mode of regulation by design (2017, unknown): “…model for contemporary digital services is one of ‘barter’, with users agreeing to disclose their personal data to firms in return for services (van Dijck, 2014, p. 2000) under a ‘free’ rather than ‘fee’ for services revenue model, thereby eliminating an important barrier to adoption faced by…”
Data for life: Wearable technology and the design of self-care (2016, background): “16 Instead of a molecularization of biopower, it constitutes a ‘ datafication ’ (van Dijck, 2014; see also Hansen, 2014, p. 20).”
Platformisation (2019, background): “As a process, the development of data infrastructures has been captured through the notion of datafication, referring to the ways in which digital platforms render into data, practices and processes that historically eluded quantification (Kitchin, 2014; Mayer-Schönberger & Cukier, 2013; Van Dijck, ”
Critical analysis of Big Data challenges and analytical methods (2017, background): “In line with the data ﬁ cation concept and ever increasing technological advancements, advocates assert that in the future a majority of data willbegenerated andsharedthroughmachines, as machinescommuni-cate with each other over data networks (Van Dijck, 2014).”
Surveillance, Snowden, and Big Data: Capacities, consequences, critique (2014, background): “In particular, different kinds of data are now being captured and used in new ways, which prompts some to distinguish between surveillance as targeted practices over against fresh form of dataveillance (van Dijck, 2014).”
The datafied child: The dataveillance of children and implications for their rights (2017, background): “Their generation and interpretation always involve particular viewpoints, ideological frameworks, forms of disciplinary expertise, as well as the values, assumptions and biases of those who collect and analyse them (Andrejevic, 2014; Kitchin, 2014; Lupton, 2015b; van Dijck, 2014).”
On fairness (2017, background): “…data from other sources, as a number of recent high-profile cases have shown, such as Edward Snowden’s revelations about the data mining operations of the National Security Agency in the United States and Government Communications Headquarters in the United Kingdom (Lyon, 2014; van Dijck, 2014).”