The Data Revolution. Big Data, Open Data, Data Infrastructures and Their Consequences
The last few years have witnessed an increasing production of data that have become open, accessible and available at low cost. Although many disciplines are already using 'big data' as instrument of analysis, social sciences have apparently missed the opportunity to exploit their potentialities fully. The purpose of this excellent book is to prove how these data do not exist independently from the ideas, techniques, technologies, people and context that produce, process, manage, analyze and store them. Moreover, the author explores the definition, characteristics and the technique to manage big data, but he also focuses his attention on the challenges of this way of thinking and on how big data are changing existing epistemology and science. Before the big data revolution, the scientific approach was based on computational science based on the simulation of complex phenomena. In the age of big data, however, an exploratory approach based on data intensive, statistical exploration and data mining was used. In the age of big data, however, an exploratory approach based on data intensive, statistical exploration and data mining is used. The author focuses his attention on how big data are changing the approaches and methodologies in four different fields, that is, governing people, managing organizations, leveraging value and producing capitals, creating better places in which to live. The aim of the book is threefold: to provide a detailed reflection on the nature of the data and their wider assemblages; to chart how these assemblages are shifting and mutating all along the development of new data infrastructures; and to reflect on the consequences that these new ways to assemble data may entail the making of sense and on the effects they produce in the world. The 11 chapters ideally can be divided into two main sections. The first section (chapters 1 6) deals with the big data characteristics and the techniques to manage them. The last section (chapters 7 11) consider how big data are changing the epistemology of science across all domains (arts and humanities, social and life sciences, engineering) The interest of these last chapters lies in the core idea of data being not self meaningful, as their meaningfulness is proportionate to the information they can provide. This is particularly interesting as it fosters dense insights and ideas on further development in research. The book starts with the definition of big data and enhances the concept by which data do not exist independently from the ideas, instruments, practices, context and knowledge used to generate, process, analyze and draw conclusions from them. The book continues with an analysis of the data characteristics. Data vary by forms (qualitative and quantitative) structure (structured, semi structured and unstructured) source (captured, derived, exhaust, transient) producer (primary, secondary, tertiary) and type (indexical, attribute, metadata) However, these different types of data share the same characteristic as they all form the basis of the knowledge pyramid where data precede information which, in turn, precedes knowledge. The latter precedes understanding and wisdom. In order to make sense of data, they are usually pooled into datasets and databases designed and organized to enable specific analysis. How they are structured has consequences on the queries and obtainable results. The author underlines the importance of the data assembly process as an issue that needs further attention and research. Chapter 4 explores big data characteristics, that is, volume, velocity, variety, exhaustivity, resolution/indexicality, relationality and flexibility/scalability. The author then examines the interest that the access to large data with those specific characteristics may have for society, governments and business organizations. Chapter 5 concerns the sources of big data. The starting point is that the production of big data has been facilitated by the confluence of five technological innovations: growing computational power, internet, pervasive and ubiquitous computing, indexical and machine readable identification, and massive distributed storage. The data production can be divided into three categories, that is, directed data (generated by traditional forms of surveillance) automated data (generated by automatic function of the device or systems) and volunteered data (traded or gifted by people to a system) Once again, with his critical approach, the author underlines the importance of developing empirical studies to examine in depth the various ways in which big data are being generated, Regional Studies, 2016