A recent report from the UN makes the case for “global data literacy” in order to realise the opportunities afforded by the “data revolution”. Here and in many other contexts, data literacy is characterised in terms of a combination of numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just competencies in reading and working with datasets but also the ability to account for, intervene around and participate in the wider (...) socio-technical infrastructures through which data is created, stored and analysed – which we call “data infrastructure literacy”. We illustrate this notion with examples of “inventive data practice” from previous and ongoing research on open data, online platforms, data journalism and data activism. Drawing on these perspectives, we argue that data literacy initiatives might cultivate sensibilities not only for data science but also for data sociology, data politics as well as wider public engagement with digital data infrastructures. The proposed notion of data infrastructure literacy is intended to make space for collective inquiry, experimentation, imagination and intervention around data in educational programmes and beyond, including how data infrastructures can be challenged, contested, reshaped and repurposed to align with interests and publics other than those originally intended. (shrink)

This paper explores the embedding of data producing technologies in people's everyday lives and practices. It traces how repeated encounters with digital data operate to naturalise these entities, while often blindsiding their agentive properties and the ways they get implicated in processes of exploitation and governance. I propose and develop the notion of ‘data doxa’ to conceptualise the way in which digital data – and the devices and platforms that stage data – have come to be perceived in Western societies (...) as normal, necessary and enabling. The ‘data doxa’ concept also accentuates the enculturation of many individuals into a data sharing habitus which frames digital technologies in simplistic terms as panaceas for the problems associated with contemporary life, figures of progress and convenience, and mediums of knowledge, pleasure and identity. I suggest that three types of data-based relations contribute to the formation of this doxic sensibility: fetishisation, habit and enchantment. Each of these relations come to mediate public understandings of digital devices and the data they generate, obscuring the multifaceted nature and hidden depths of data and their propensity to double up as technologies of exposure and discipline. As a result of this situation, imaginative educational programs and revamped regulatory frameworks are urgently needed to inform individuals about the contribution of data to the leveraging of value and power in today's digital economies, but also to protect them from experiencing data-based harms. (shrink)

Debate over the closure of DeVasco High School shows that data-driven accountability was a methodological and administrative processes that produced both transparency and opacity. Data, when applied to a system of accountability, produced new capabilities and powers, and as such were political. It created second-hand representations of important objects of analysis. Using these representations administrators spoke on behalf of the school, the student and the classroom, without having to rely on the first-person accounts of students, teachers or principals. They empowered (...) one group—central city administrators—over another—teachers and principals. After analyzing the form these policies took, this article concludes that it is necessary to rethink the processes that create visibility and invisibility. Public data obscured the voices, experiences and collective traumas of students and faculty within the school. A narrow focus on activities within the schools rendered invisible the structural decisions made by the Department of Education in New York City—to favor small schools over large, comprehensive ones. In order to create understanding, and a sense of common purpose, those who are spoken for in simplified data must also be given the opportunity to debate the representations of their performance and quality. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected data and built tools. (...) These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them. (shrink)