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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Big data differ fundamentally from traditional small datasets: they are generated and processed in real time, exhaustive in scope, and have fine resolution.
“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]
Current urban science's approach is shaped by two more recent epistemological positions: inductive empiricism and data-driven science.
“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]
Such practices are now widespread, enabling data brokers to grow rapidly by capturing, combining and repackaging data into private infrastructures for rent or resale, along with analysis and profiles.
“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]
The data and services from these companies are used for many tasks for which the data were never intended, including predictive profiling, social sorting, behavioural nudging, and regulating, controlling and governing individuals and systems.
“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]
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Ingrams, A., Kaufmann, W., & Jacobs, D. (2022) · Policy & Internet · doi:10.1002/poi3.276 · Cited by 133 · see citing works
Leonelli, S. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0122 · Cited by 131 · see citing works
“…are influenced by local research cultures and administration, national science policies, training programmes and available infrastructures (see also [5–7, National governments and international agencies such as the Organisation for Economic Co-operation and Development (OECD), the European…”
BackgroundExploring the impact of digital technologies on professional responsibilities and education
Fenwick, T., & Edwards, R. (2015) · European Educational Research Journal · doi:10.1177/1474904115608387 · Cited by 130 · see citing works
“Critics such as Kitchin (2014), Naughton (2012) and Pariser (2011) have been calling attention to the wide-reaching and accelerating consequences of these forces, claiming that professionals, professional educators, researchers, policy-makers and the public are just beginning to realise the…”
BackgroundUnderstanding demographic and socioeconomic biases of geotagged Twitter users at the county level
Jiang, Y., Li, Z., & Ye, X. (2018) · Cartography and Geographic Information Science · doi:10.1080/15230406.2018.1434834 · Cited by 120 · see citing works
“Meanwhile, researchers have questioned the ability to let big data “ speak for itself ” (Kitchin, 2014), as representativeness (Mislove, Lehmann, Ahn, Onnela, & Rosenquist, 2011), spatiotemporal extent (Rzeszewski, 2018), and uneven geographic information (Graham, De Sabbata, & Zook, 2015) can make…”
BackgroundData and oil: Metaphor, materiality and metabolic rifts
Taffel, S. (2021) · New Media & Society · doi:10.1177/14614448211017887 · Cited by 127 · see citing works
“‘Data is the new oil’ is not the only metaphor that surrounds data; there are also proclamations of a data deluge (Anderson, 2008), data revolution (Kitchin, 2014), and data tsunamis (Zander and Mähönen, 2013); however, claims that data has superseded fossil fuels as the paradigmatic commodity in…”
BackgroundThe Ethics of Using Digital Trace Data in Education: A Thematic Review of the Research Landscape
Hakimi, L., Eynon, R., & Murphy, V. A. (2021) · Review of Educational Research · doi:10.3102/00346543211020116 · Cited by 169 · see citing works
Aradau, C., & Blanke, T. (2016) · European Journal of Social Theory · doi:10.1177/1368431016667623 · Cited by 126 · see citing works
“…increasingly stored and processed, big data-based predictive analytics appears to harbour new capacities of peering into the future and revealing the unknowns to be tamed and governed (boyd and Crawford, 2012; Cukier and Mayer-Schoenberger, 2013; Kitchin, 2014; Lyon, 2014; Aradau and Blanke, 2015).”
BackgroundPatterns in Award Winning Data Storytelling
Ojo, A., & Heravi, B. R. (2017) · Digital Journalism · doi:10.1080/21670811.2017.1403291 · Cited by 125 · see citing works
“Kitchin (2014) describes data as raw ele- ments that are abstracted from phenomena and measured and recorded in different ways.”
BackgroundMethodInfluentialContent moderation: Social media’s sexist assemblages
Gerrard, Y., & Thornham, H. (2020) · New Media & Society · doi:10.1177/1461444820912540 · Cited by 82 · see citing works
“While algorithms do a very good job of appearing to be neutral and wholly driven by user data, they in fact ‘represent certain design decisions about how the world is to be ordered’ (Bucher, 2018: 67) and as such are selective, partial and constructed (Gitelman and Jackson, 2013; Kitchin, 2014: 14).”
BackgroundStudying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Miceli, M., Posada, J., & Yang, T. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492853 · Cited by 125 · see citing works
“Technological development is sociotechnical in nature and data, as an abstraction [28, 55], is not given but created through human discretion [68] and shaped by power dynamics [62].”
BackgroundSchweinsberg, M., Madan, N., Vianello, M., Sommer, S. A., Jordan, J., Tierney, W., Awtrey, E. C., Zhu, L. L., Diermeier, D. A., Heinze, J. E., et al. (2016) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2015.10.001 · Cited by 109 · see citing works
“…original and replication studies are posted on the Open Science Framework (https://osf.io/q25xa/), creating a publicly available resource researchers can use to better understand reproducibility and nonreproducibility in science (Kitchin, 2014; Nosek & Bar-Anan, 2012; Nosek, Spies, & Motyl, 2012).”
Privacy and Security Concerns in the Smart City
Fabrègue, B. F. G., & Bogoni, A. (2023) · Smart Cities · doi:10.3390/smartcities6010027 · Cited by 130 · see citing works
Die Methoden der sozialwissenschaftlichen Datenerhebung im digitalen Zeitalter
Leitgöb, H., & Wolbring, T. (2021) · Schriftenreihe der ASI · doi:10.1007/978-3-658-34396-5_1 · Cited by 119 · see citing works
Datapolis: A Public Governance Perspective on “Smart Cities”
Meijer, A. J. (2017) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvx017 · Cited by 121 · see citing works
Nost, E., & Goldstein, J. E. (2021) · Environment and Planning E Nature and Space · doi:10.1177/25148486211043503 · Cited by 116 · see citing works
“But vast datasets are now produced by digital tools, while analog data is increasingly digitized (Kitchin, 2014).”
BackgroundDemystifying big data: Anatomy of big data developmental process
Shin, D. (2016) · Telecommunications Policy · doi:10.1016/j.telpol.2015.03.007 · Cited by 115 · see citing works
Social Injustice in Surveillance Capitalism
Cinnamon, J. (2017) · Surveillance & Society · doi:10.24908/ss.v15i5.6433 · Cited by 116 · see citing works
Enacting migration through data practices
Scheel, S., Ruppert, E., & Ustek-Spilda, F. (2019) · Environment & Planning. D, Society and Space · doi:10.1177/0263775819865791 · Cited by 86 · see citing works
Ethics for the majority world: AI and the question of violence at scale
Quijano, P. R. (2022) · Media Culture & Society · doi:10.1177/01634437221099612 · Cited by 133 · see citing works
“Data is defined as the ‘material produced by abstracting the world into categories, measures and other forms of representation [. . . ] that constitute the building blocks from which information and knowledge are created’ (Kitchin, 2014: 1).”
BackgroundBibri, S. E., & Allam, Z. (2022) · Smart Cities · doi:10.3390/smartcities5020037 · Cited by 123 · see citing works
Data inequalities and why they matter for development
Cinnamon, J. (2019) · Information Technology for Development · doi:10.1080/02681102.2019.1650244 · Cited by 115 · see citing works
“…with little evidence to demonstrate that simply making existing datasets available will have these impacts, and indeed increasing evidence suggests that open data may simply empower private companies and the already socioeconomically advantaged in society (Gurstein, 2011; Kitchin, 2014).”
BackgroundCaring for data: Value creation in a data-intensive research laboratory
Pinel, C., Prainsack, B., & McKevitt, C. (2020) · Social Studies of Science · doi:10.1177/0306312720906567 · Cited by 113 · see citing works
Data Matters: The Politics and Practices of Digital Border and Migration Management
Leese, M., Noori, S., & Scheel, S. (2021) · Geopolitics · doi:10.1080/14650045.2021.1940538 · Cited by 116 · see citing works
“More recently, scholars have started to acknowledge and conceptualise these “data assemblages” (Kitchin 2014b) that under-pin borders and migration management.”
BackgroundBig Data Model of Security Sharing Based on Blockchain
Li, Y., Junqin, H., Shengzhi, Q., & Ruijin, W. (2017) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2017.31 · Cited by 119 · see citing works
Social equity in the data era: A systematic literature review of data‐driven public service research
Ruijer, E. H. J. M., Porumbescu, G. A., Porter, R., & Piotrowski, S. J. (2022) · Public Administration Review · doi:10.1111/puar.13585 · Cited by 124 · see citing works
Contested futures: envisioning “Personalized,” “Stratified,” and “Precision” medicine
Erikainen, S., & Chan, S. (2019) · New Genetics and Society · doi:10.1080/14636778.2019.1637720 · Cited by 108 · see citing works
“Digital data collection, collation and analysis tools have enabled new research modes that rely on “big data,” i.e. large quantities of varied, dynamic, and mobilizable data (Kitchin 2014).”
Method
- 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
- Social Sciences › Safety Research › 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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