Cited by 1,469Big Data Technologies and ApplicationsEthics and Social Impacts of AIDigitalization, Law, and Regulation

The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences

Kitchin, Rob·2014·SAGE Publications Ltd eBooks

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

Integrity signals

Registry

Crossref · 10.4135/9781473909472

Cites

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Claims the paper makes

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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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Sorted by citations

Authors
Kitchin, R.
Corresponding author
Kitchin, Rob
Venue
SAGE Publications Ltd eBooks
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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