Critical data studies
Critical data studies is about exploring the social, cultural, and ethical issues that come with big data. It uses critical thinking to ask who controls data, who is helped or harmed by data, and how power shows up in data practices.
The field began to take shape when scholars danah boyd and Kate Crawford raised important questions about the social impact of big data. In 2014, Craig Dalton and Jim Thatcher helped coin the term “critical data studies” and stressed studying data in its real-world context to think more deeply about it.
Researchers in this area come from many disciplines. They treat data as a historical artifact and look at how data is collected, stored, and used over time. Well-known contributors include Rob Kitchin, Tracey Lauriault, and others who push for rethinking data from cultural, political, and social perspectives.
Several big ideas guide critical data studies. These include Feminist, Anti-Racist, Indigenous, Queer, Decolonial, and Anti-Ableist perspectives, along with concepts like data feminism, dataveillance, datafication, and how algorithms can be biased. These frameworks help researchers examine power, privacy, consent, and who is underrepresented or misrepresented in data.
Why study data critically? Because big data is central to modern life and its tools are rarely neutral. Data are often collected with specific goals and built into systems that reflect existing power and inequality. Seeing data as a historical and social artifact helps reveal hidden agendas, biases, and the choices made during data collection and analysis.
Ethical and practical concerns are a big part of the work. Topics include surveillance and privacy, who owns data, how secure data are, and how data can affect people’s lives in areas like health, education, and the law. For example, researchers highlight how insurance and healthcare algorithms can be biased against certain groups, and they advocate for clearer documentation of models so people can understand how they work and where biases might appear.
Data feminism is one influential approach that uses feminist ideas to push for fairer data practices. It stresses informed consent, privacy, and the responsibility of those who collect data to consider how power and inequality show up in data. Other important ideas include recognizing how data can shape cities and everyday life, and how to ensure that data use respects people’s rights and dignity.
In practice, critical data studies encourage balancing big data with smaller, context-rich data. They urge researchers to “speak for the data” rather than letting data speak for itself, and to question how data are gathered, shared, and applied. This approach aims to improve fairness, accountability, and transparency in data-driven work.
Overall, critical data studies call for more thoughtful, inclusive, and responsible ways of using data—so that data help everyone and do not reinforce existing harms or inequalities.
This page was last edited on 3 February 2026, at 01:14 (CET).