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The public sector is becoming increasingly aware of the potential value to be gained from big data, as governments generate and collect vast quantities of data through their everyday activities.

The benefits of big data in the public sector can be grouped into three major areas, based on a classification of the types of benefits: advanced analytics, through automated algorithms; improvements in effectiveness, providing greater internal transparency; improvements in efficiency, where better services can be provided based on the personalization of services; and learning from the performance of such services.

The chapter examined several drivers and constraints that have been identified, which can boost or stop the development of big data in the sector depending on how they are addressed. The findings, after analysing the requirements and the technologies currently available, show that there are open research questions to be addressed in order to develop such technologies so competitive and effective solutions can be built. The main developments are required in the fields of scalability of data analysis, pattern discovery, and real-time applications. Also required are improvements in provenance for the sharing and integration of data from the public sector. It is also extremely important to provide integrated security and privacy mechanisms in big data applications, as public sector collects vast amounts of sensitive data. Finally, respecting the privacy of citizens is a mandatory obligation in the European Union.

The finance and insurance sector by nature has been an intensively data-driven industry, managing large quantities of customer data and with areas such as capital market trading having used data analytics for some time.

The advent of big data in financial services can bring numerous advantages to financial institutions: enhanced levels of customer insight, engagement, and experience through the digitization of financial products and services and with the increasing trend of customers interacting with brands or organizations in the digital space; enhanced fraud detection and prevention capabilities through the use of big data it is now possible to use larger datasets to identify trends that indicate fraud; and enhanced market trading analysis, where trading strategies which make the use of sophisticated computer algorithms to rapidly trade the financial markets.

This chapter identifies the drivers related with the evolution of the sector, like the impact of regulations, and changing business models, together with the associated constraints related with legacy culture and infrastructures, and data privacy and security issues. The findings, after analysing the requirements and the technologies currently available, show that there are still research challenges to develop the technologies to their full potential in order to provide competitive and effective solutions. These challenges appear at all levels of the big data chain and involve a wide set of different technologies, which would make necessary a prioritization of the investments in R&D, for example, real-time aspects, better data quality techniques, scalability of data management and processing, and better sentiment classification methods.

Massive amounts of sensor and textual data await the energy and transport sector stakeholders once the digital transformation of the sector reaches its tipping point. This chapter gives a definition of big data application scenarios through examples in different segments of the energy and transport sectors. A mere utilization of existing big data technologies as employed by online businesses will not be sufficient. Domain-specific big data technologies are needed for cyber-physical energy and transport systems, while the focus needs to move beyond big data to smart data technologies. Unless the need for privacy and confidentiality is satisfied, there will always be regulatory uncertainty and barriers to user acceptance of new data-driven offerings. The chapter concludes with recommendations that will help sustain the quality and competitiveness of European infrastructures as it undergoes a digital transformation.

The media and entertainment industries are evolving at an unprecedented rate, driven by the twin needs to reduce operating costs and simultaneously generate more revenue from increasingly competitive and uncertain markets. Media companies are in many respects an early adopter of big data technologies because it enables them to drive digital transformation, exploiting more fully not only data which was already available, but also new sources of data from both inside and outside the organization. This chapter presents a wide-ranging overview of the state of the art of big data in the media sector. It introduces the industrial needs, application scenarios, and other aspects of the sector and describes how they influence, and are influenced by, products, customers, and processes. Finally, the research is distilled into a comprehensive set of requirements across the entire big data value chain, alongside the consolidated roadmap tracking the development of key technologies to support semantic data enrichment, data quality, data-driven innovation, and data analysis.

This chapter identifies the cross-sectorial requirements for big data research necessary to define a research roadmap. The aim of the roadmap is to maximize and sustain the impact of big data technologies and applications in different industrial sectors by identifying and driving opportunities in Europe. This chapter details the process used to consolidate the big data requirements from different sectors into a single roadmap. The results comprise a prioritized set of cross-sector requirements that were used to define the technology policy, business, and society roadmaps together with action recommendations. This chapter presents a summarized description of the cross-sectorial consolidated requirements. It discusses each of the high-level and sub-level requirements together with the associated challenges that need to be tackled. Finally, the chapter concludes with a prioritization of the cross-sectorial requirements based on their expected impacts.

This chapter describes big data roadmaps for Europe in the areas of technology, business, policy, and society. The roadmaps outline the most urgent and challenging issues for big data in Europe. They are the result of over 2 years of extensive research and input from a wide range of stakeholders from the European big data ecosystem. The roadmaps will foster the creation of a more stable big data environment by enabling enterprises, business, entrepreneurs, SMEs, and society to gain from the benefits of big data in Europe. The chapter introduces the Big Data Value Association (BDVA) and the Big Data Value contractual Public Private Partnership (BDV cPPP) and describes the role played by the BIG project in their establishment. The BDVA and the BDV cPPP will provide the necessary framework for industrial leadership, investment, and commitment of both the private and public side to build a data-driven economy across Europe.