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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 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]
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“Against this background, they sensitize us to the embeddedness of digital infrastructures in political, social and economic processes and contexts (Kitchin, 2014; Kitchin et al., 2015).”
BackgroundCowley, R., Barnett, C., Katzschner, T., Tkacz, N., & Boeck, F. D. (2017) · Resilience · doi:10.1080/21693293.2017.1348506 · Cited by 15
“To study these displays is to take a specific methodological gambit in relation to ongoing discussions of big data or ‘datafication’ (Kitchin, 2014; Mayer-Schönberger & Cukier, 2013).”
BackgroundMoving back to the future of big data-driven research: reflecting on the social in genomics
Goisauf, M., Akyüz, K., & Martin, G. M. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00544-5 · Cited by 19
“…neglecting the fact that data is not produced free of preceding theoretical framing, methodological decisions, technological conditions and the interpretation of correlations—i.e. an assemblage situated within a specific place, time, political regime and cultural context (Kitchin 2014a).”
BackgroundInfluentialA comprehensive review of current trends, challenges, and opportunities in text data privacy
Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 19
Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems
Calzada, I., Németh, G., & Al-Radhi, M. S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030062 · Cited by 16
Les facettes de l’Open Data : émergence, fondements et travail en coulisses
Jérôme, D., & Goëta, S. (2017) · Collège de France eBooks · doi:10.4000/books.cdf.5005 · Cited by 14
Data Philanthropy: Corporate Responsibility with Strategic Value?
George, J. J., Yan, J., & Leidner, D. E. (2019) · Information Systems Management · doi:10.1080/10580530.2020.1696587 · Cited by 14
“Further, control of data exploration assets mitigates the risk of imitation by rivals even when the data are readily available to rivals (Kitchin, 2014).”
BackgroundLee, A., & Cook, P. (2019) · Sociology Compass · doi:10.1111/soc4.12749 · Cited by 14
Kim, J.-H., Kim, Y.-R., Jung, S.-H., Choi, H.-J., & Kwon, J.-H. (2021) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2021.1925394 · Cited by 16
“For this reason, big data is essential to identifying paradigm shifts in in technology innovation and idea acquisition (Bughin, Chui, and Manyika 2010; Kitchin 2014).”
BackgroundJamil, S. (2022) · Springer eBooks · doi:10.1007/978-3-658-36276-8_21 · Cited by 14
Primary schools and network governance: A policy analysis of reception baseline assessment
Roberts‐Holmes, G., & Bradbury, A. (2017) · British Educational Research Journal · doi:10.1002/berj.3285 · Cited by 14
Neumann, J. (2022) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_193 · Cited by 13
The Praxis and Politics of Building Urban DashboardsPreprint
Kitchin, R., Maalsen, S., & McArdle, G. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2608988 · Cited by 14
Self-citationLocal Search is a Remarkably Strong Baseline for Neural Architecture Search
Ottelander, T. D., Dushatskiy, A., Virgolin, M., & Bosman, P. A. N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72062-9_37 · Cited by 41
“or machine learning tasks in heterogeneous elds, including image recognition [23], natural language processing [21], games [27, 31], and genomics [26]. While the availability of large amounts of data [16] and hardware advancements (e.g., tensor parallel processing units [14]) have been key enabling factors for deep le…”
Daten, Daten, Daten!! Zur Technologisierung von Bildung und Kritik durch Digitalisierung
Höhne, T., & Karcher, M. (2022) · Bildung und Arbeit · doi:10.1007/978-3-658-36909-5_3 · Cited by 13
A Method of Exchanging Data in Smart City by Blockchain
Qian, Y., Liu, Z., Yang, J., & Wang, Q. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00223 · Cited by 14
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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