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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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Is A Star A Document? Catalogued Students and Learning Analytics
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“…produced by abstracting the world into categories, measures and other representational forms - numbers, characters, symbols, images, sounds, electro-magnetic waves, bits – that constitute the building blocks from which information and knowledge are created (Kitchin 2014: 1 in Beer 2019: 2).”
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BackgroundPrivatheit, Ethik und demokratische Selbstregulierung in einer digitalen Gesellschaft
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“Finally, OGD raises managers’ digital awareness, shifting decision-making from experience-based to data-driven, optimizing resource allocation and accelerating innovation (Kitchin 2014).”
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“3 ‘Don’t believe the hype—it’s a sequel’ Some scholars have described AI in terms of ‘hype’ (e.g. Humm et al. 2022; La-Grandeur 2024; Rapanta 2025; Widder and Hicks 2024) or as a ‘revolution’ (e.g. Kitchin 2014; Mayer-Schönberger and Cukier 2013; Simanowski 2021).”
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“There are proprietary data from companies, authoritative data from governments and increasingly available open data (Kitchin 2014).”
Dijital Sosyal Koruma İçin Çok Katmanlı Bir Yapay Zeka Modeli: Türkiye Örneği
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“Reliability encapsulates quixotic reliability (single method of data collection, its results should not vary over time, etc.) and diachronic reliability (e.g., in situ data should stable as observed over time) (Kitchin 2014).”
BackgroundMaking Sense of Platform Intermediation
Barns, S. (2019) · Springer eBooks · doi:10.1007/978-981-32-9725-8_6 · Cited by 4
Information Wants to Be Free, Or Does It?: The Ethics of Datafication
Luers, W. (2017) · Cited by 3
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VERİ EKONOMİSİ VE REKABET HUKUKU
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Big Data Analytics in Basketball Versus Business
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Truth in a sea of data: adoption and use of data search tools among researchers and journalists
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“At the same time, studies (e.g., Bertot et al., 2010; Janssen et al., 2012; Kitchin, 2014; Poel et al., 2018) have indicated, as observed in our study, that many issues (e.g., the lack of accessibility and openness, the problems with literacy and usability, the barriers for adoption, participation…”
BackgroundEthical Issues concerning Health Data Ownership
Cha, H., & Kim, J. (2021) · Korean Journal of Medical Ethics · doi:10.35301/ksme.2021.24.4.423 · Cited by 2
New ethnographic perspective on relational ethics in the field of Artificial intelligence
Pavlović, P., & Fink, M. H. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01751-3 · Cited by 5
“Thus, among other things, eScience (Gray 2009; Kitchin 2014b) and then algorithm studies and the possibilities of a more objective science (Anderson 2008; Dwork and Milligan 2013; Grey 2009). he warned that “an attitude of moral indifference has no connection with scientific objectiv - ity” (Weber…”
Who Watches the Watchdog? Understanding Media Systems as Information Regimes
Ots, M., Berglez, P., & Nord, L. (2023) · Media and Communication · doi:10.17645/mac.7216 · Cited by 3
“The epistemological point of departure for this method (see Kitchin, 2014, p. 10) is the intertwined relations between data (abstracted elements), information (logically linked elements of data), knowledge (organized information), and wisdom , wherein applied and relevant knowledge is ideally…”
MethodDigitalisierung als Herausforderung für die soziologische Theorie
Block, K., & Pohle, J. (2023) · Berliner Journal für Soziologie · doi:10.1007/s11609-023-00503-2 · Cited by 3
“„Daten“ sind nicht nur das „neue Öl“ (Houben und Prietl 2018), sie „verdoppeln“ – eigentlich: multiplizieren – die Welt (van Dijck 2014; Kitchin 2014; Süssenguth 2015; Nassehi 2019), sind aber zugleich überschie-ßend (Baecker 2019) und hoch selektiv (Boyd und Crawford 2012), schaffen ganz eigene…”
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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