The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

By D. Stuart. 2015

928 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Information Systems

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Citations in the corpus

Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work (2014, methodology): “…data held in archives and repositories are being digitized and linked together and made available through new data infrastructures, and vast swathes of government produced and held data are being made openly accessible as the open data movement gains traction (Lauriault et al. 2007, Kitchin 2014a).”
Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process (2016, background): “ging with large, complex data sets distributed across networked technologies (Kitchin, 2014).”
When data is capital: Datafication, accumulation, and extraction (2019, background): “This, in turn, means that data is a foundational form of capital for everything from the ‘smart home’ to the ‘smart city,’ finance to governance, production to distribution, consumer devices to enterprise systems, and much more (Kitchin, 2014).”
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets (2016, methodology): “Based on a review of deﬁnitions of Big Data, Kitchin (2013, 2014) contends that Big Data are qualitatively diﬀerent to traditional, small data along seven axes (see Table 1).”
Algorithms and their others: Algorithmic culture in context (2016, methodology): “The rise of interest in Big Data techniques (e.g. Boellstorﬀ and Maurer, 2015; Kitchin, 2014) is of course a signiﬁcant source of interest in algorithms in the ﬁrst place, but the topic of data structures – the speciﬁc representations that organize data in order to make it processable by algorithms…”
Construction with digital twin information systems (2020, background): “Broadly speaking, information is compiled by interpreting data , information is processed to create knowledge , and knowledge supports decisions and actions (Kitchin, 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).”
Questioning Big Data: Crowdsourcing crisis data towards an inclusive humanitarian response (2016, background): “It covers a wide array of channels and formats, ranging from Facebook and Twitter messages, to (online) government records, to content created by formal media outlets, to responses and comments recorded in needs assessment surveys NGOs (summarize and) post online (Kitchin, 2014).”
The ethics of smart cities and urban science (2016, background): “These critiques undoubtedly still hold, though advocates of computational social and urban science counter that in the age of big data the variety, exhaustivity, resolution and relationality of data, plus the growing power of computation and new data analytics, address some of the criticism, especia”
Big Data in food and agriculture (2016, background): “Critical social scientists have suggested that Big Data cannot be treated as a technical accomplishment separable from the social, and in particular from questions of justice and ethics (Busch, 2014; Couldry and Turow, 2014; Crawford et al., 2014; Elmer et al., 2015; Kitchin, 2014).”
Heuristics of the algorithm: Big Data, user interpretation and institutional translation (2015, background): “As Couldry (2014), Kitchin (2014) and Mosco (2014) have recently remarked, it is important to clarify whether what we are observing is an academic or a governmental turn to actually taking up Big Data as a heuristic – or if it is more a case of powerful institutions using the discourse or ‘myth’ of…”
Digital subjectivation and financial markets: Criticizing Social Studies of Finance with Lazzarato (2016, background): “If CDS theorists are right to suggest that data are never raw, but always dependent on social contexts that render data meaningful (Kitchin, 2014: 20), then the question arises what kind of subject is needed to cook the data.”
Platformisation (2019, background): “…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, 2014; and Mejias & Couldry, 2019 on datafication, as part of this special section).”
The platformization of cultural production: Theorizing the contingent cultural commodity (2018, positive): “Similarly, it should be observed that data infrastructures, such as APIs, not only preformat, process, and articulate end-user activities (Kitchin, 2014), but they also push complementors to align and subsequently integrate their own data infrastructures and strategies with those of the GAFAM platfo”
The Threat of Algocracy: Reality, Resistance and Accommodation (2016, background): “With the ongoing data revolution and the transition towards the so-called Internet of Things, this trend can only be set to grow (Kitchin 2014a; Kellermeit & Obodovski 2013; Rifkin 2014).”