The data revolution : big data, open data, data infrastructures & their consequences

By Rob Kitchin. 2014

1,230 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Information Systems

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Citations in the corpus

Big Data Surveillance: The Case of Policing (2017, background): “…on mechanical objectivity; see also Espeland and Vannebo 2007; Hacking 1990), new analytic platforms and techniques are deployed in preexisting organizational contexts (Barley 1986, 1996; Kling 1991) and embody the purposes of their crea-tors (boyd and Crawford 2012; Gitelman 2013; Kitchin 2014).”
Thinking critically about and researching algorithms (2014, background): “As are the complex set of decision-making processes and practices, and the wider assemblage of systems of thought, ﬁ nance, politics, legal codes and regulations, materialities and infrastructures, institutions, inter-personal relations, which shape their production (Kitchin, 2014).”
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).”
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).”
Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process (2016, background): “Acknowledging this integral labour of repair in data science is an important step in understanding the economic, political, and cultural transformations accompanying the ‘data revolution’ (Kitchin, 2014).”
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.”
Digital turn, digital geographies? (2018, background): “While geographies of the digital have understood data to be key to all digital communication, big data refers to a quantitative and qualitative shift in the amount, velocity, variety, resolution and flexibility of data that is now collected and analysed by a range of devices (Kitchin, 2014b).”
The Smart City as Global Discourse: Storylines and Critical Junctures across 27 Cities (2019, background): “The smart city is more than “mere” discourse.1 Indeed, much of its critique centers upon the problematique of the “place-less” corporate-governmental discourse (e.g., Hollands, 2008; Shelton et al., 2015; Söderström et al., 2014; Kitchin, 2015).”
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).”
Data Epistemologies, The Coloniality of Power, and Resistance (2019, background): “The emergence of a growing field of critical studies on data epistemologies (boyd and Crawford 2012; Couldry and Mejias 2019; Kitchin 2014) and resistance (Milan and Gutiérrez 2015; Milan and Treré 2017; Milan and van der Velden 2016; Treré 2018) demonstrates the urgency of addressing the…”
The Threat of Algocracy: Reality, Resistance and Accommodation (2016, methodology): “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).”
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…”
The conundrum of police officer-involved homicides: Counter-data in Los Angeles County (2016, background): “Critical data studies generally critiques the widely held understanding of data as an objective set of facts that exist prior to ideology, politics, or interpretation (Kitchin, 2014).”
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).”
Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS (2016, background): “…raised questions about these claims, by highlighting the difficult and complicated tasks needed for the use of Big Data in the sciences (Leonelli, 2014a), the technical impossibility of many data-driven claims (Kitchin, 2014b) and the role of hypotheses in research based on Big Data (Ratti, 2015).”
Social media data for conservation science: A methodological overview (2019, background): “Social media data, like big data sources in general, are characterized by large volumes of data, internal variability, varying veracity and high velocity of accumulation (Kitchin, 2014).”