Innovations in Digital Research Methods. Группа авторов

      2 The Changing Social Science Data Landscape

      Kingsley Purdam

      Mark Elliot

      2.1 Introduction

      2.1.1 The Age of Data

      More than a century since the ground-breaking social surveys of Booth in London ¹ and Rowntree in York in the UK, and the subsequent development of mass observation methods in the 1930s, we are now in an age of almost overwhelming volumes of data about many people’s attitudes, circumstances and behaviour. Such data extends from people’s views to images of them, their locations and movements, and their communications. The data is very diverse; it includes lifelong health and prescription records, genetic biomarker profiles and family histories, satellite images, digital passports and their use, databases from product warranty forms, consumption transactions, online browsing records, email and web communications, social media, and mobile phone use. As Berners-Lee and Shadbolt (2011:1) highlight, ‘data is the new raw material of the 21st Century’.

      Social science and the societies that it studies have entered the age of data, though not necessarily the age of data access. Nevertheless, access to this data is increasing; for example, administrative record data held by public bodies, including government departments, is being widened.^{2, 3} The term ‘big data’ has been much used to describe the data revolution and whilst a little simplistic as a concept it moves us forward from Sweeney’s (2001) discussion of the ‘information explosion’, insofar as it captures the growth in the collection and availability of information (for discussions see boyd and Crawford, 2012; Mayer-Schönberger and Cukier, 2013; O’Reilly Radar Team, 2011). Big data denotes volumes of data so large that they are kept in so-called data warehouses, which are digital data storage facilities often cutting across different national borders and data regulation regimes. It is the volume of data (when potentially information about all, or nearly all, of a particular population is included, as opposed to a sample), the variety of the variables, and the speed with which it can be discovered and accessed that open up new opportunities for research and methodological innovation (Mayer-Schönberger and Cukier, 2013; IBM, 2013).

      The term ‘big data’ is used differently by different authors, with some including orthodox or well-established forms of social science data, such as survey responses and focus group transcripts (Elliot et al., 2013).⁴ The new types of data can have very different origins and structures. Some might be collected primarily for research use, whilst other data might be produced as a secondary outcome to another activity, for example, buying a product online or posting views on a blog. Some of this new data has been around in some form and quantity for some time, but its use in social science research has been limited, perhaps because of access and infrastructural constraints, methodological uncertainties and a lack of interest in, or opportunity for, social research use (Elliot et al., 2013).

      In many ways, conceptually the term ‘big data’ fails to capture the all-encompassing nature of the socio-technical transformation that is upon us. Many people who use the term qualify it by stating that big data is not just about volume but also other features: that data can be captured, updated and analysed in (almost) real-time and that it can be linked through multiple data capture points and processes. However, such characterizations are not sufficient; they still express the notion of data as something we have, whereas the reality and