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Big data is expected to impact all sectors, from healthcare to media, from energy to retail. The ability to effectively manage information and extract knowledge is now seen as a key competitive advantage for organizations. This chapter explores the value potential of big data with a particular focus on the European context. The chapter identifies the positive transformational potential of big data within a number of key sectors and highlights the need for a clear strategy to increase the competitiveness of European industries in order to drive innovation and competitiveness. Europe needs to foster the development and wide adoption of big data technologies, value adding use cases, and sustainable business models through a Big Data Ecosystem. Finally the chapter describes the key dimensions, including skills, legal, business, and social, that need to be addressed in a European Big Data Ecosystem.

The Big Data Public Private Forum (BIG) Project () was an EU coordination and support action to provide a roadmap for big data within Europe. The BIG project worked towards the definition and implementation of a clear big data strategy that tackled the necessary activities needed in research and innovation, technology adoption, and the required support from the European Commission necessary for the successful implementation of the big data economy. As part of this strategy, the outcomes of the project were used as input for Horizon 2020.

This chapter provides an overview of the BIG project detailing the project’s mission and strategic objectives. The chapter describes the partners within the consortium and the overall structure of the project work. The three-phase methodology used in the project is described, including details on the techniques used within the technical working groups, sectorial forms, and road mapping activity. Finally, the project’s role in setting up the big data contractual Public Private Partnership (cPPP) and Big Data Value Association is discussed.

Big data is the emerging field where innovative technology offers new ways to extract value from the tsunami of available information. As with any emerging area, terms and concepts can be open to different interpretations. The Big Data domain is no different. This chapter examines the different definitions of “Big Data” which have emerged over the last number of years to label data with different attributes. The Big Data Value Chain is introduced to describe the information flow within a big data system as a series of steps needed to generate value and useful insights from data. The value chain enables the analysis of big data technologies for each step within the chain. The chapter explores the concept of a Big Data Ecosystem. It examines the use of the ecosystem metaphor within the business community to describe the business environment and how it can be extended to the big data context. Key stakeholders of a big data ecosystem are identified together with the challenges that need to be overcome to enable a big data ecosystem in Europe.

Different data processing architectures for big data have been proposed to address the different characteristics of big data. Data acquisition has been understood as the process of gathering, filtering, and cleaning data before the data is put in a data warehouse or any other storage solution. The acquisition of big data is most commonly governed by four of the Vs: volume, velocity, variety, and value. Most data acquisition scenarios assume high-volume, high-velocity, high-variety, but low-value data, making it important to have adaptable and time-efficient gathering, filtering, and cleaning algorithms that ensure that only the high-value fragments of the data are actually processed by the data-warehouse analysis. The goals of this chapter are threefold: First, it aims to identify the current requirements for data acquisition by presenting open state-of-the-art frameworks and protocols for big data acquisition for companies. The second goal is to unveil the current approaches used for data acquisition in the different sectors. Finally, it discusses how the requirements of data acquisition are met by current approaches as well as possible future developments in the same area.

The value of big data is predicated on the ability to detect trends and patterns and more generally to make sense of the large volumes of data that is often comprised of a heterogeneous mix of format, structure, and semantics. Big data analysis is the component of the big data value chain that focuses on transforming raw acquired data into a coherent usable resource suitable for analysis. Using a range of interviews with key stakeholders in small and large companies and academia, this chapter outlines key insights, state of the art, emerging trends, future requirements, and sectorial case studies for data analysis.

With the emergence of data environments with growing data variety and volume, organizations need to be supported by processes and technologies that allow them to produce and maintain high-quality data facilitating data reuse, accessibility, and analysis. In contemporary data management environments, data curation infrastructures have a key role in addressing the common challenges found across many different data production and consumption environments. Recent changes in the scale of the data landscape bring major changes and new demands to data curation processes and technologies. This chapter investigates how the emerging big data landscape is defining new requirements for data curation infrastructures and how curation infrastructures are evolving to meet these challenges. Different dimensions of scaling-up data curation for big data are described, including emerging technologies, economic models, incentive models, social aspects, and supporting standards. This analysis is grounded by literature research, interviews with domain experts, surveys, and case studies and provides an overview of the state-of-the-art, future requirements and emerging trends in the field.

This chapter provides an overview of big data storage technologies. It is the result of a survey of the current state of the art in data storage technologies in order to create a cross-sectorial technology roadmap. This chapter provides a concise overview of big data storage systems that are capable of dealing with high velocity, high volumes, and high varieties of data. It describes distributed file systems, NoSQL databases, graph databases, and NewSQL databases. The chapter investigates the challenge of storing data in a secure and privacy-preserving way. The social and economic impact of big data storage technologies is described, open research challenges highlighted, and three selected case studies are provided from the health, finance, and energy sector. Some of the key insights on big data storage are (1) in-memory databases and columnar databases typically outperform traditional relational database systems, (2) the major technical barrier to widespread up-take of big data storage solutions are missing standards, and (3) there is a need to address open research challenges related to the scalability and performance of graph databases.

Big data usage covers the business goals that need access to data, its analyses, and integration into business decision-making. This chapter gives an overview of the applications of big data, focusing on decision support through big data in different sectors. Big data usage is a wide field that is addressed in this chapter by viewing data usage from various perspectives, including the underlying technology stacks, trends in various sectors, the impact on business models, and requirements on human–computer interaction. The chapter explores data usage tools, query and scripting languages, execution engines, APIs, programming models, different technology stacks, and some of the trade-offs involved are discussed. The chapter presents general aspects of decision support, followed by a discussion of specific access to analysis results through visualization and new explorative interfaces. Emerging trends and future requirements are presented with special emphasis on Industry 4.0 and the emerging need for smart data and smart services.

This chapter provides the conceptual background and overview of big data-driven innovation in society. Specifically, it examines the nature of data-driven innovation, exemplars of big data-driven innovations in sectors spanning healthcare, public sector, finance, media, energy, and transport. It discusses core enablers for these innovations highlighting factors and challenges associated with the adequate diffusion, uptake, and sustainability of big data-driven initiatives. Finally, it presents policy recommendations to guide the development of a big data innovation ecosystem.

Several developments in the healthcare sector, such as escalating healthcare costs, increased need for healthcare coverage, and shifts in provider reimbursement trends, trigger the demand for big data technology. The wide scope and variety of discussed big data applications indicate the promising opportunities of big data technologies to improve overall healthcare delivery. However, in order to realize those applications, one needs to enable seamless access to the various health data sets. As of today, access to health data is only possible in a very constrained and limited manner. In order to improve this situation and for establishing the basis for the widespread implementation of big data applications in the healthcare sector, several technical requirements such as the semantic enrichment of data, data integration and sharing, data privacy and security, as well as data quality, need to be addressed. In terms of market adoption, the big data revolution in the healthcare domain is in a very early stage with the most potential for value creation and business development unclaimed as well as unexplored. Current roadblocks are the established system incentives of the healthcare system, which hinder collaboration and, thus, data sharing and exchange. The trend towards value-based healthcare delivery will foster the collaboration of the stakeholder to enhance the value of the patient’s treatment, and thus will significantly foster the need for big data applications.