The DOI matches its Crossref record.
The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences
Abstract
Chapter 1: Conceptualising Data What are data? Kinds of data Data, information, knowledge, wisdom Framing data Thinking critically about databases and data infrastructures Data assemblages and the data revolution Chapter 2: Small Data, Data Infrastructures and Data Brokers Data holdings, data archives and data infrastructures Rationale for research data infrastructures The challenges of building data infrastructures The challenges of building data infrastructuresData brokers and markets Chapter 3: Open and Linked Data Open data Linked data The case for open data The economics of open data Concerns with respect to opening data Chapter 4: Big Data Volume Exhaustive Resolution and indexicality Relationality Velocity Variety Flexibility Chapter 5: Enablers and Sources of Big Data The enablers of big data Sources of big data Directed Data Automated data Volunteered data Chapter 6: Data Analytics Pre-analytics Machine learning Data mining and pattern recognition Data visualisation and visual analytics Statistical analysis Prediction, simulation and optimization Chapter 7: The Governmental and Business Rationale for Big Data Governing people Managing organisations Leveraging value and producing capital Creating better places Chapter 8: The Reframing of Science, Social Science and Humanities Research The fourth paradigm in science? The re-emergence of empiricism The fallacies of empiricism Data-driven science Computational social sciences and digital humanities Chapter 9: Technical and Organisational Issues Deserts and deluges Access Data quality, veracity and lineage Data integration and interoperability Poor analysis and ecological fallacies Skills and human resourcing Chapter 10: Ethical, Political, Social and Legal Concerns Data shadows and dataveillance Privacy Data security Profiling, social sorting and redlining Secondary uses, control creep and anticipatory governance Modes of governance and technological lock-ins Chapter 11: Making Sense of the Data Revolution Understanding data and the data revolution Researching data assemblages Final thoughts
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Big data have fundamentally different properties to traditional 'small' datasets, being generated and processed in real time, exhaustive in scope and having fine resolution [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“it should be noted that its approach is shaped by two more recent epistemological positions [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“Such data practices are now common, enabling the rapid growth of data brokers which capture, gather together and repackage data into privately held data infrastructures for rent (for one time use or use under licensing conditions) or re-sale, along with data analysis and profiles [1,69].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“The data and services these companies offer are used to perform a wide variety of tasks for which the data were never intended, including to predictively profile, socially sort, behaviourally nudge, and regulate, control and govern individuals and the various systems and infrastructures with which they interact [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
Citations
Cited by
Sorted by citations
The platformization of cultural production: Theorizing the contingent cultural commodity
Nieborg, D. B., & Poell, T. (2018) · New Media & Society · doi:10.1177/1461444818769694 · Cited by 1,530
Thinking critically about and researching algorithms
Kitchin, R. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1154087 · Cited by 1,471
“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).”
BackgroundSelf-citationWhen data is capital: Datafication, accumulation, and extraction
Sadowski, J. (2019) · Big Data & Society · doi:10.1177/2053951718820549 · Cited by 1,057
“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).”
BackgroundThe social power of algorithms
Beer, D. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1216147 · Cited by 945
“Here, we see the role of algorithms, as decision-making parts of code (see Beer, 2013, p. 65), as being analytic and decisive (for a discussion of the role of algorithms in data analytics, see Kitchin, 2014, pp. 100–112).”
Method“Big Data Surveillance: The Case of Policing
Brayne, S. (2017) · American Sociological Review · doi:10.1177/0003122417725865 · Cited by 596
“…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).”
Background
Metadata
- Authors
- Kitchin, R.
- Corresponding author
- Kitchin, Rob
- ORCID
- Kitchin, Rob
- Venue
- SAGE Publications Ltd eBooks
- Publisher
- SAGE Publications Ltd
- Published
- 2014
- OpenAlex
- W584790758
- Cited by
- 1,469
- References
- 0
- Citations by year
- 2026: 170 · 2025: 150 · 2024: 120 · 2023: 126 · 2022: 156 · 2021: 143 · 2020: 113 · 2019: 123 · 2018: 99 · 2017: 108 · 2016: 100 · 2015: 52 · 2014: 8
- FWCI
- 135.08
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- SAGE Publications Ltd eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- data revolution, big data, open data, data infrastructure, data brokers, data repositories, linked data, data analytics, data-driven research, computational social science, digital humanities, data quality, data integration, interoperability, data ethics, privacy, dataveillance, profiling, anticipatory governance, technological lock-in, data assemblages
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