Stakeholder Analysis of Data Ecosystems

Stakeholder analysis and management have received significant attention in management literature primarily due to the role played by key stakeholders in the success or failure of projects and programmes. Consequently, it becomes important to collect and analyse information on relevant stakeholders to develop an understanding of their interest and influence. This chapter provides an analysis of stakeholders within the European data ecosystem. The analysis identifies the needs and drivers of stakeholders concerning big data in Europe; furthermore, it examines stakeholder relationships within and between different sectors. For this purpose, a two-stage methodology was followed for stakeholder analysis, which included sector-specific case studies and a cross-case analysis of stakeholders. The results of the analysis provide a basis for understanding the role of actors as stakeholders who make consequential decisions about data technologies and the rationale behind the incentives targeted at stakeholder engagement for active participation in a data ecosystem.

The Road to Big Data Standardisation

This chapter covers the critical topic of standards within the area of big data. Starting with an overview of standardisation as a means for achieving interoperability, the chapter moves on to identify the European Standards Development Organizations that contribute to the European Commission’s plan for the Digital Single Market. The author goes on to describe, through use cases, exemplar big data challenges, demonstrates the need for standardisation and finally identifies the critical big data use cases where standards can add value. The chapter provides an overview of the key standardisation activities within the EU and the current status of international standardisation efforts. Finally, the chapter closes with future trends for big data standardisation.

Correction to: A Roadmap to Drive Adoption of Data Ecosystems

The original version of the chapter was inadvertently published with an error. The affiliation of the author Davide Dalle Carbonare has now been corrected to “Engineering Ingegneria Informatica, Rome, Italy”.

Data Economy 2.0: From Big Data Value to AI Value and a European Data Space

Artificial intelligence (AI) has a tremendous potential to benefit European citizens, economy, environment and society and already demonstrated its potential to generate value in various applications and domains. From a data economy point of view, AI means algorithm-based and data-driven systems that enable machines with digital capabilities such as perception, reasoning, learning and even autonomous decision making to support people in real scenarios. Data ecosystems are an important driver for AI opportunities as they benefit from the significant growth of data volume and the rates at which it is generated. This chapter explores the opportunities and challenges of big data and AI in exploiting data ecosystems and creating AI value. The chapter describes the European AI framework as a foundation for deploying AI successfully and the critical need for a common European data space to power this vision.

A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence

This chapter presents a best practice framework for the operation of Big Data and Artificial Intelligence Centres of Excellence (BDAI CoE). The goal of the framework is to foster collaboration and share best practices among existing centres and support the establishment of new Centres of Excellence (CoEs) within Europe. The framework was developed following a phased design science process, starting from a literature review to create an initial framework which was enhanced with the findings of a multi-case study of existing successful CoEs. Each case study involved an in-depth analysis and a series of in-depth interviews with leadership personnel of existing CoEs.The resulting best practice framework models a CoE using open systems theory that comprises input (environment), transformation (CoE) and output (impact). The framework conceptualises the internal operation of the CoE as a set of high-level capabilities including strategy, governance, structure, funding, and people and culture. The core capabilities of the CoE include business development, collaboration, research support services, technical infrastructure, experimentation/demonstration platforms, Intellectual Property (IP) and data protection, education and public engagement, policy outreach, technology and knowledge transfer, and performance and impact assessment. In this chapter we describe the best practice framework for CoEs in big data and AI, including objectives, environment, strategic and operational capabilities, and impact. The chapter outlines how the framework can be used by a CoE to support its strategic direction and operational decisions over time, and how a new CoE can use it in the start-up phase. Based on the analysis of the case studies, the chapter explores the critical success factors of a CoE as defined by a survey of CoE managers. Finally, the chapter concludes with a summary.

Big Data Value Creation by Example

The Big Data Value contractual Public-Private Partnership between the European Commission and the Big Data Value Association (BDVA) was signed in October 2014. Since then, more than 50 projects and numerous BDVA members have explored how data can drive innovation across the data stack and how industries can transform business practices. Meanwhile, start-ups have been working at the confluence of new sources of data (e.g. IoT, DNA, HD pictures, satellite data) and new or revisited processing paradigms (e.g. Edge computing, blockchain, machine learning) to tackle new use cases and to provide disruptive solutions for known problems. This chapter details a collection of stories showing concrete examples of the value created thanks to a renewed usage of data.

A Reference Model for Big Data Technologies

The Big Data Value (BDV) Reference Model has been developed with input from technical experts and stakeholders along the whole big data value chain. The BDV Reference Model may serve as a common reference framework to locate big data technologies on the overall IT stack. It addresses the main technical concerns and aspects to be considered for big data value systems. The BDV Reference Model enables the mapping of existing and future data technologies within a common framework. Within this chapter, we detail the reference model in more detail and show how it can be used to manage a portfolio of research and innovation projects.

Business Models and Ecosystem for Big Data

With the recent technical advances in digitalisation and big data, the real and the virtual worlds are continuously merging, which, again, leads to entire value-added chains being digitalised and integrated. The increase in industrial data combined with big data technologies triggers a wide range of new technical applications with new forms of value propositions that shift the logic of how business is done. To capture these new types of value, data-driven solutions for the industry will require new business models. The design of data-driven AI-based business models needs to incorporate various perspectives ranging from customer and user needs and their willingness to pay for new data-driven solutions to data access and the optimal use of technologies, while taking into account the currently established relationships with customers and partners. Successful data-driven business models are often based on strategic partnerships, with two or more players establishing the basis for sustainable win-win situations through transparent resource-, investment-, risk-, data- and value-sharing. This chapter will explore the different data-driven business approaches and highlight in this context the importance of functioning ecosystems on the various levels. The chapter will conclude with an introduction to the data-driven innovation framework, a proven methodology to guide the systematic investigation of data-driven business opportunities while incorporating the dynamics of the underlying ecosystems.

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data.

The European Big Data Value Ecosystem

The adoption of big data technology within industrial sectors facilitates organizations to gain competitive advantage. The impacts of big data go beyond the commercial world, creating significant societal impact, from improving healthcare systems to the energy-efficient operation of cities and transportation infrastructure, to increasing the transparency and efficiency of public administration. In order to exploit the potential of big data to create value for society, citizens and businesses, Europe needs to embrace new technology, applications, use cases and business models within and across various sectors and domains. In the early part of the 2010s, a clear strategy centring around the notion of the European Big Data Value Ecosystem started to take form with the aim of increasing the competitiveness of European industries through a data ecosystem which tackles the fundamental elements of big data value, including the ecosystem, research and innovation, business, policy and regulation, and the emerging elements of data-driven AI and common European data spaces. This chapter describes the big data value ecosystem and its strategic importance. It details the challenges of creating this ecosystem and outlines the vision and strategy of the Big Data Value Public-Private Partnership and the Big Data Value Association, which together formed the core of the ecosystem, to make Europe the world leader in the creation of big data value. Finally, it details the elements of big data value which were addressed to realise this vision.