Critical data studies

Rob KitchinNational University of Ireland, Maynooth

Critical data studies

• Data are constitutive of the ideas, techniques, technologies, people, systems and contexts that conceive, produce, process, manage, and analyze them

• Need to make sense of big data• Technically• Ethically• politically/economically• spatially/temporally• philosophically

Data Assemblage

Attributes ElementsSystems of

thoughtModes of thinking, philosophies, theories, models, ideologies, rationalities,

etc.Forms of

knowledgeResearch texts, manuals, magazines, websites, experience, word of mouth,

chat forums, etc.

FinanceBusiness 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 &

communitiesOf data producers, curators, managers, analysts, scientists, politicians,

users, citizens, etc.

PlacesLabs, offices, field sites, data centres, server farms, business parks, etc,

and their agglomerations

MarketplaceFor data, its derivatives (e.g., text, tables, graphs, maps), analysts, analytic

software, interpretations, etc.

Nature/plurality of big dataSources• 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

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

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

Rob.Kitchin@nuim.ie@robkitchin

http://www.nuim.ie/progcity/

Sage, Aug 2014

Volume 3(3)