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

1. Critical data studies
Rob Kitchin
National University of Ireland, Maynooth

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. 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. 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. 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. 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. Rob.Kitchin@nuim.ie
@robkitchin
http://www.nuim.ie/progcity/
Sage, Aug 2014
Volume 3(3)