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The most fascinating and profitable subject of predictive algorithms is the human actor. Analysing big data through learning algorithms to predict and pre-empt individual decisions gives a powerful tool to corporations, political parties and the state. Algorithmic analysis of digital footprints, as an omnipresent form of surveillance, has already been used in diverse contexts: behavioural advertising, personalised pricing, political micro-targeting, precision medicine, and predictive policing and prison sentencing. This volume brings together experts to offer philosophical, sociological, and legal perspectives on these personalised data practices. It explores common themes such as choice, personal autonomy, equality, privacy, and corporate and governmental efficiency against the normative frameworks of the market, democracy and the rule of law. By offering these insights, this collection on data-driven personalisation seeks to stimulate an interdisciplinary debate on one of the most pervasive, transformative, and insidious socio-technical developments of our time.

Part 1: Keynote Panel ; International audience ; Governments have always retained public service data internally in their own systems with only limited information provided to the public and other stakeholders such as the business, charitable and NGO communities. However, the rapid advancement of ICTs coupled with electronic publishing via the Internet in the last decade in particular has enabled governments to exploit the potential of wider distribution and use of such data previously held in internal systems. The panellists will discuss how Big, Open and Linked Data (BOLD) can be utilized to drive innovation and what obstacles and challenges may be encountered. Empowering citizens, potential mis-use in identity theft, policy manipulation or market distortion, and the need to combine open data with closed sources will be discussed.

Information technologies are now being developed that transform the physical environment, and the human interactions within it, into a "digital skin" of the city. This skin consists of a sensored and metered urban environment. In concert with ubiquitous computing, and the increasing use of electronically-mediated interactions in general, the physical world is becoming a platform for generating much new data on the workings of human society, its interactions with the physical environment, and manifold processes in economics, politics, and social interactions. The city is a subject of this revolution, in the sense that the technologies are predicted to make it possible to manage the physical city in ways not previously possible, but also to make possible major changes in the political and social interactions of people within cities, and between citizens and government. The city is also an objective basis for the revolution, in the sense that it is the sensored and metered platform that can generate unprecedented "big data" for many new types of uses. This revolution opens up many questions for urban theory and research, and many new issues for public and urban policy, which are explored in this paper.

PurposeThis article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance.Design/methodology/approachFollowing a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes.FindingsThe results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP.Originality/valueAlthough some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.

Recent computing power and storage advancements have meant more data are being collected and stored. Referred to as 'Big data', these data sources offer researchers myriad opportunities to make observations about the social world. These data can be massive, provide insight into whole populations rather than just a sample, and be used to analyse social behaviour in real time. Administrative data, a subcategory under the big data umbrella, also offers researchers abundant opportunities to conduct highly relevant research in many areas, including sociology, social policy, education, health studies and many more. This paper offers reflections on social research during the digital age by examining different forms of data, both 'big' and 'small', and their associated advantages and disadvantages. The paper concludes by suggesting that although big data has some promising elements, it also comes with some limitations and povwill not replace 'traditional' social surveys. And yet, when used in conjunction with social surveys, appropriately and ethically, big data could offer the researchers additional valuable insights.