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Critical data studies


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A short set of slides that accompanied my thoughts as a discussant on papers presented at the alt.conference on Big Data at the Conference of the Association of American Geographers, Tampa, April 8-12, 2014

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Critical data studies

  1. 1. Critical data studies Rob Kitchin National University of Ireland, Maynooth
  2. 2. Critical data studies • Data are constitutive of the ideas, techniques, technologies, people, system s and contexts that conceive, produce, process, manage, and analyze them • Need to make sense of big data • Technically • Ethically • politically/economically • spatially/temporally
  3. 3. Data Assemblage Attributes Elements Systems of thought Modes of thinking, philosophies, theories, models, ideologies, rationalities, etc. Forms of knowledge Research texts, manuals, magazines, websites, experience, word of mouth, chat forums, etc. Finance Business models, investment, venture capital, grants, philanthropy, profit, etc. Political economy Policy, tax regimes, public and political opinion, ethical considerations, etc. Govern-mentalities / Legalities Data standards, file formats, system requirements, protocols, regulations, laws, licensing, intellectual property regimes, etc. Materialities & infrastructures Paper/pens, computers, digital devices, sensors, scanners, databases, networks, servers, etc. Practices Techniques, ways of doing, learned behaviours, scientific conventions, etc. Organisations & institutions Archives, corporations, consultants, manufacturers, retailers, government agencies, universities, conferences, clubs and societies, committees and boards, communities of practice, etc. Subjectivities & communities Of data producers, curators, managers, analysts, scientists, politicians, users, citizens, etc. Places Labs, offices, field sites, data centres, server farms, business parks, etc, and their agglomerations Marketplace For data, its derivatives (e.g., text, tables, graphs, maps), analysts, analytic software, interpretations, etc.
  4. 4. Nature/plurality of big data Sources • Directed surveillance • Automated data generation • Automated surveillance • Capture systems • Digital devices • Sensed and scanned data • Interaction and transactional data • IoT (Internet of things) and M2M (machine to machine) • Volunteered data generation • Social media • Sousveillance • Crowdsourcing • Citizen science Characteristics • huge in volume • high in velocity • diverse in variety • exhaustive in scope (n=all) • fine-grained in resolution, uniquely indexical • relational in nature • flexible, holding the traits of extensionality and scalability
  5. 5. Critical data studies Political/ethical issues • Data shadows, dataveillance • Privacy • Data security • Profiling, social sorting, redlining • Control creep, anticipatory governance • Modes of governance, technological lock-ins Technical/organisation issues • Deserts and deluges • Access • Quality/veracity/lineage • Standards, integration, interoperability • Poor analytics, ecological fallacies, data dredging • Skills, resourcing Epistemology, methodologies and practices of academia • Data empiricism, data science, computational social science, digital humanities • Data analytics
  6. 6. Road map for critical data studies • Philosophical reflection and synoptic, conceptual, critical, normative analyses; • Detailed empirical research concerning the genesis, constitution, functioning and evolution of big data assemblages • trace out the contextual, contingent and relational processes and socio-technical arrangements at play within whole assemblages or specific aspects of them • utilising genealogies, deconstruction, ethnographies, and observant participation, analytics
  7. 7. @robkitchin Sage, Aug 2014 Volume 3(3)