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In this article, we discuss on a general and mainly theoretical-conceptual level how schools and societies can/should respond to Big Data. Firstly, we identify what Big Data is. Based on Levi Bryant's onto-cartography, we suggest that Big Data can be characterized ontologically as different socio-technical mechanic assemblages. These assemblages comprise different combinations of social systems, big states and big companies, and Big Data technologies on the one hand; and citizens, not least teachers and students, on the other hand. Secondly, we present three different assembling scenarios: 1) a state model, where a Big State in alliance with new technology (and companies) uses Big Data to control citizens and students; 2) a market model, where Big Companies in alliance with new technology (and politicians) are free to use Big Data to nudge citizens and students; 3) a democratic model, where citizens and students are protected by the state from being surveilled, controlled and nudged by new big Cyborgs and also educated to be critical of and act with and against Big Data.

For several years, the social sciences experience an almost Copernican revolution: range and scope of social science data are increasing rapidly, research on computer-based methods for classification and analysis of existing large data volumes experiencing an interdisciplinary boom. The very much lamented lack of information about individual behavior or institutions has become - at least in part - abundantly wrong (King 2011). For this, a bit simplistic, but impressive example: The data collected as part of the German Election Study and the ALLBUS since 1949 can be easily put together on a gigabyte of storage. About the social media platform Twitter, which increasingly becoming the focus of scientific interest, will, over the rule of thumb, about four gigabytes of data generate - per hour. This illustrates not necessarily the rapid increase in political science relevant data - about the content some arbitrarily collated Tweet Collection for political science research let themselves worthy of debate - but probably the enormous potential of new data sources, as well as the extraordinary technical challenges are faced with the researchers, might study the social behavior on the World Wide Web. Adapted from the source document.

Intro -- Big Data In The GovTech System: Scientific Vision and Modern Empirical Experience (Introduction) -- Contents -- GovTech in the Provision of High-Tech Educational Services Based on Big Data -- On the Need and Opportunities for Digitalization of the Educational and Methodological Support of the Educational Process in the Context of Improving Its Quality Indicators -- 1 Introduction -- 2 Materials and Method -- 3 Results -- 4 Conclusion -- References -- Effectiveness of the Education System: Comparative Analysis of the Estimated Data Parameters -- 1 Introduction -- 2 Materials and Methods -- 3 Discussion -- 4 Results -- 5 Conclusion -- References -- Modern Educational Platforms for Distance Training on Lean Production -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- The Use of Digital Technologies in the Implementation of the Meta-Subject Approach as a Trend in the Development of the International Educational Environment -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- Digital Skills Shaping as a Factor of Sustainable Development of Higher Education -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- Digital Technologies in the Teacher's Professional Activities -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- Distance Learning in Higher Education: Technologies of the Moodle Electronic Environment -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- State Regulation of the Economy by Industry Using Big Data in the GovTech -- Improving the Application of Information Technology in the Economy of Service Organizations -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusion -- References -- Development of the Service Sector in a Digital Environment -- 1 Introduction -- 2 Methodology.

Master's thesis in Industral Economics ; The European Commission has implemented the General Data Protection Regulation (GDPR) which will replace the current, but obsolete, Data Protection Directive 95/46/EC. When legally effective, May 25th 2018, it will impose a much stricter regime and sanctions which magnitude may force bankruptcy. It increases dramatically the scope of what is considered personal data while restricting the processing as such. Thus, curtailing businesses' opportunity to drive value through big data analytics. In an increasingly data-driven economy, where data is drawn in the same breath as competitive advantage, it may seem like the candle is burned at both ends. Pursuant to the issue a question arises to whether the value of data will diminish. Consequently, this work researches how the GDPR will impact the value of data, with an emphasis on value driven trough the big data value chain. The research is carried out in three phase: A preliminary analysis that identifies a set of value drivers; a primary analysis that identifies influences from the GDPR on said value drivers; and a case study on smart meter data. The results are presented as five assertions which make up the foundation of a discussion. The research finds that the short-term impact raises concern to limitations put on: realizing value in public interest; harnessing the power of algorithms in automated decision-making; and discovery of new knowledge through data mining. However, the positive long-term impact are expected to overshadow the negatives and to ensure a sustainable data-economy in the future. The research concerns a legislation that is yet to be enforced. The results are therefore predictions rather than hard facts, but will serve as insight to possible future challenges. ; submittedVersion

Dieser Band befasst sich vor dem Hintergrund der digitalen Transformation mit Big Data, der darauf bezogenen Analytik und praktischen Anwendungen. Beispiele sind die Einwirkung auf Einstellungen und Verhalten, die Entwicklung neuartiger Geschäftsmodelle, die Steuerung von lebenswichtigen Infrastrukturen, die Fortentwicklung der Wissenschaft, Predictive Policing, Cyberkriminalität u. a. Das Recht, so das deutsche und europäische Datenschutzrecht sowie das Kartellrecht, sind nicht hinreichend auf die besonderen Probleme von Big Data abgestimmt. Entgrenzungen, Vermachtungen, Intransparenzen u.a. erschweren rechtlichen Schutz. Erforderlich sind neue oder veränderte Formen regulativer Gestaltung und Kontrolle, darunter auch Veränderungen im Datenschutzrecht, Sicherungen von Transparenz und Zurechenbarkeit, der Ausbau systemischen Schutzes, erweiterte Folgenabschätzungen, rechtliche Umhegungen von Selbstregulierung und vieles andere. Mit Beiträgen von Wolfgang Hoffmann-Riem, Gerrit Hornung, Yoan Hermstrüwer, Andreas von Arnauld, Tobias Mast, Stephan Dreyer, Markus Oermann, Kevin Dankert, Matthias Bäcker, Jan C. Joerden, Tobias Singelnstein, Thomas Hoeren.

This book presents the fundamentals for understanding the concept of big data, including data analysis methods, learning processes, its applications to insurance and its position within the insurance market. Topics ranging from classical data analysis methods to the impact of big data on the present and future insurance market are discussed to give an overview of big data methods applied to insurance problems. As an edited book, the reader will find chapters written by authors well-known in their fields, including data scientists, actuaries, statisticians and engineers. This book has been written for readers who want to gain a better understanding of big data and its applications within companies and organizations in the fields of banking, insurance and marketing

Big data applications in the transport sector have achieved national and EU-level interest as a driver for future economic growth and at the same time a source of concern, in terms of negative socio-economic impacts. This report reviews current policies implemented in the EU, its Member States and internationally, which support or restrict the (re-) use, linking of and sharing of data, in the context of big data techniques and in the transport sector. Also, the report illustrates in selected examples of transport-related private companies, the types of private sector policies that have been adopted or promoted. While there are not any distinctly big data policies, each political entity has implemented some policies aimed at protecting the privacy of its citizens, encouraging data sharing among private and public sector entities, and develop policies that support the digitalization of the transport sector. Some of the key areas of policy in the transport sector are for instance the implementation of Intelligent Transport System, the increased Open Data policies, Automated Driving, and Smart Mobility. Preceding and in light of these developments, the private sector has also moved ahead to incorporate the use of big data techniques into their own business models as process or product innovations. The potential applications in the transport sector are diverse, as digitalization is a major trend of the transport sector. The report covers six distinct transport sub-sectors, where the application of big data is or potentially could be used. The aim is to highlight the challenges and enablers of data sharing in the different cases. The cases are: Railway Operators, Open Data in the Airport Operator Context, Real-time Road Traffic Management, Big Data in Supply Chain Management, Managing Port operations, and Connected and Automated Vehicles. The development of a policy roadmap to foster the growth of big data in transport will require an understanding of how existing policies affect the economic, political, social and ...