The Elements of Big Data Value: Foundations of the Research and Innovation Ecosystem

Description

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. Foreword Foreword Foreword Preface Acknowledgements Contents Editors and Contributors Part I: Ecosystem Elements of Big Data Value The European Big Data Value Ecosystem 1 Introduction 2 What Is Big Data Value? 3 Strategic Importance of Big Data Value 4 Developing a European Big Data Value Ecosystem 4.1 Challenges 4.2 A Call for Action 4.3 The Big Data Value PPP (BDV PPP) 4.4 Big Data Value Association 5 The Elements of Big Data Value 5.1 Ecosystem Elements of Big Data Value 5.2 Research and Innovation Elements of Big Data Value 5.3 Business, Policy and Societal Elements of Big Data Value 5.4 Emerging Elements of Big Data Value 6 Summary References Stakeholder Analysis of Data Ecosystems 1 Introduction 2 Stakeholder Analysis 3 Who Is a Stakeholder? 4 Methodology 4.1 Phase 1: Case Studies 4.2 Phase 2: Cross-Case Analysis 5 Sectoral Case Studies 6 Cross-Case Analysis 6.1 Technology Adoption Stage 6.2 Data Value Chain 6.3 Strategic Impact of IT 6.4 Stakeholder Characteristics 6.5 Stakeholder Influence 7 Summary References A Roadmap to Drive Adoption of Data Ecosystems 1 Introduction 2 Challenges for the Adoption of Big Data Value 3 Big Data Value Public-Private Partnership 3.1 The Big Data Value Ecosystem 4 Five Mechanism to Drive Adoption 4.1 European Innovation Spaces (i-Spaces) 4.2 Lighthouse Projects 4.3 Technical Projects 4.4 Platforms for Data Sharing 4.4.1 Industrial Data Platforms (IDP) 4.4.2 Personal Data Platforms (PDP) 4.5 Cooperation and Coordination Projects 5 Roadmap for Adoption of Big Data Value 6 European Data Value Ecosystem Development 7 Summary References Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far 1 Introduction 2 The Big Data Value PPP 2.1 BDV PPP Vision and Objectives for European Big Data Value 2.2 Big Data Value Association (BDVA) 2.3 BDV PPP Objectives 2.4 BDV PPP Governance 2.5 BDV PPP Monitoring Framework 3 Main Activities and Achievements During 2018 3.1 Mobilisation of Stakeholders, Outreach, Success Stories 4 Monitored Achievements and Impact of the PPP 4.1 Achievement of the Goals of the PPP 4.2 Progress Achieved on KPIs 4.2.1 Private Investments 4.2.2 Job Creation, New Skills and Job Profiles 4.2.3 Impact of the BDV PPP on SMEs 4.2.4 Innovations Emerging from Projects 4.2.5 Supporting Major Sectors and Major Domains with Big Data Technologies and Applications 4.2.6 Experimentation 4.2.7 SRIA Implementation and Update 4.2.8 Technical Projects 4.2.9 Macro-economic KPIs 4.2.10 Contributions to Environmental Challenges 4.2.11 Standardisation Activities with European Standardisation Bodies 5 Summary and Outlook References Part II: Research and Innovation Elements of Big Data Value Technical Research Priorities for Big Data 1 Introduction 2 Methodology 2.1 Technology State of the Art and Sector Analysis 2.2 Subject Matter Expert Interviews 2.3 Stakeholder Workshops 2.4 Requirement Consolidation 2.5 Community Survey 3 Research Priorities for Big Data Value 3.1 Priority `Data Management´ 3.1.1 Challenges 3.1.2 Outcomes 3.2 Priority `Data Processing Architectures´ 3.2.1 Challenges 3.2.2 Outcomes 3.3 Priority `Data Analytics´ 3.3.1 Challenges 3.3.2 Outcomes 3.4 Priority `Data Visualisation and User Interaction´ 3.4.1 Challenges 3.4.2 Outcomes 3.5 Priority `Data Protection´ 3.5.1 Challenges 3.5.2 Outcomes 4 Big Data Standardisation 5 Engineering and DevOps for Big Data 5.1 Challenges 5.2 Outcomes 6 Illustrative Scenario in Healthcare 7 Summary References A Reference Model for Big Data Technologies 1 Introduction 2 Reference Model 2.1 Horizontal Concerns 2.1.1 Data Visualisation and User Interaction 2.1.2 Data Analytics 2.1.3 Data Processing Architectures 2.1.4 Data Protection 2.1.5 Data Management 2.1.6 Cloud and High-Performance Computing (HPC) 2.1.7 IoT, CPS, Edge and Fog Computing 2.2 Vertical Concerns 2.2.1 Big Data Types and Semantics 2.2.2 Standards 2.2.3 Communication and Connectivity 2.2.4 Cybersecurity 2.2.5 Engineering and DevOps for Building Big Data Value Systems 2.2.6 Marketplaces, Industrial Data Platforms and Personal Data Platforms (IDPs/PDPs), Ecosystems for Data Sharing and Innovat... 3 Transforming Transport Case Study 3.1 Data Analytics 3.2 Data Visualisation 3.3 Data Management 3.4 Assessing the Impact of Big Data Technologies 3.5 Use Case Conclusion 4 Summary References Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving T... 1 Introduction 1.1 Aim of the Chapter 1.2 Context 2 Challenges to Security and Privacy in Big Data 3 Current Trends and Solutions in Privacy-Preserving Technologies 3.1 Trend 1: User-Centred Data Protection 3.2 Trend 2: Automated Compliance and Tools for Transparency 3.3 Trend 3: Learning with Big Data in a Privacy-Friendly and Confidential Way 3.4 Future Direction for Policy and Technology Development: Implementing the Old and Developing the New 4 Recommendations for Privacy-Preserving Technologies References A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence 1 Introduction 2 Innovation Ecosystems and Centres of Excellence 2.1 What Are Centres of Excellence? 3 Methodology 4 Best Practice Framework for Big Data and Artificial Intelligence Centre of Excellence 4.1 Environment 4.1.1 Industry 4.1.2 Policy 4.1.3 Societal 4.2 Strategic Capabilities 4.2.1 Strategy 4.2.2 Governance 4.2.3 Structure 4.2.4 Funding 4.2.5 People 4.2.6 Culture 4.3 Operational Capabilities 4.4 Impact 4.4.1 Economic Impact 4.4.2 Scientific Impact 4.4.3 Societal Impact 4.4.4 Impact Measured Through KPIs 5 How to Use the Framework 5.1 Framework in Action 6 Critical Success Factors for Centres of Excellence 6.1 Challenges 6.2 Success Factors 6.3 Mechanisms to Address Challenges 6.4 Ideal Situation 7 Summary References Data Innovation Spaces 1 Introduction 2 Introduction to the European Data Innovation Spaces 3 Key Elements of an i-Space 4 Role of an i-Space and its Alignment with Other Initiatives 5 BDVA i-Spaces Certification Process 6 Impact of i-Spaces in Their Local Innovation Ecosystems 7 Cross-Border Collaboration: Towards a European Federation of i-Spaces 8 Success Stories 8.1 CeADAR: Ireland´s Centre for Applied Artificial Intelligence 8.2 CINECA 8.3 EGI 8.4 EURECAT/Big Data CoE Barcelona 8.5 ITAINNOVA/Aragon DIH 8.6 ITI/Data Cycle Hub 8.7 Know-Center 8.8 NCSR Demokritos/Attica Hub for the Economy of Data and Devices (ahedd) 8.9 RISE/ICE by RISE 8.10 Smart Data Innovation Lab (SDIL) 8.11 TeraLab 8.12 Universidad Politécnica de Madrid/Madrid´s i-Space for Sustainability/AIR4S DIH 9 Summary Reference Part III: Business, Policy, and Societal Elements of Big Data Value Big Data Value Creation by Example 1 Introduction 2 How Can Big Data Transform Everyday Mobility and Logistics? 3 Digitalizing Forestry by Harnessing the Power of Big Data 4 GATE: First Big Data Centre of Excellence in Bulgaria 5 Beyond Privacy: Ethical and Societal Implications of Data Science 6 A Three-Year Journey to Insights and Investment 7 Scaling Up Data-Centric Start-Ups 8 Campaign Booster 9 AI Technology Meets Animal Welfare to Sustainably Feed the World 10 Creating the Next Generation of Smart Manufacturing with Federated Learning 11 Towards Open and Agile Big Data Analytics in Financial Sector 12 Electric Vehicles for Humans 13 Enabling 5G in Europe 14 Summary References Business Models and Ecosystem for Big Data 1 Introduction 2 Big Data Business Approaches 2.1 Optimisation and Improvements 2.2 Upgrading and Revaluation 2.3 Monetising 2.4 Breakthrough 3 Data-Driven Business Opportunities 4 Leveraging the Data Ecosystems 4.1 Data-Sharing Ecosystem 4.2 Data Innovation Ecosystems 4.3 Value Networks in a Business Ecosystem 5 Data-Driven Innovation Framework and Success Stories 5.1 The Data-Driven Innovation Framework 5.2 Examples of Success Stories 5.2.1 Selectionnist 5.2.2 Arable 6 Conclusion References Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework 1 Introduction 2 Data-Driven Innovation 2.1 What Are Business Opportunities? 2.2 Characteristics of Data-Driven Innovation 2.3 How to Screen Data-Driven Innovation? 3 The ``Making-of´´ the DDI Framework 3.1 State-of-the-Art Analysis 3.2 DDI Ontology Building 3.3 Data Collection and Coding 3.3.1 Selection Criteria 3.3.2 Sample Data Generation 3.3.3 Coding of Data 3.4 Data Analysis 4 Findings of the Empirical DDI Research Study 4.1 General Findings 4.2 Value Proposition 4.3 Data 4.4 Technology 4.5 Network Strategies 4.6 Revenue Strategy 4.7 Type of Business 5 Conclusion References Recognition of Formal and Non-formal Training in Data Science 1 Introduction 1.1 The Data Skills Challenge 1.2 Formal and Non-formal Learning 2 Key Projects on Data Skills 2.1 The EDISON Project 2.2 The EDSA Project 3 The Need for the Recognition of Data Skills 4 BDV Data Science Badges for Formal Education 4.1 Methodology 4.2 Badge Overview 4.3 Platform 5 BDV Data Science Labels for Non-formal Education 5.1 Methodology 5.2 Label Overview 6 Pilot and Use Case 6.1 BDV Badge Pilot 6.2 BDV Education Hub 7 Conclusion References The Road to Big Data Standardisation 1 Introduction 2 About Standardisation 2.1 ICT Standardisation and the European Union 2.1.1 ETSI: The European Telecommunications Standards Institute 2.1.2 CEN: The European Committee for Standardization 2.1.3 CENELEC: The European Committee for Electrotechnical Standardization 2.1.4 The European Multi Stakeholder Platform on ICT Standardisation 3 Identifying Big Data Use Cases 3.1 Use Case Summaries 4 Big Data Standards: The Beginning 4.1 NIST Big Data Public Working Group 4.1.1 Volume 1, Definitions 4.1.2 Volume 2, Taxonomies 4.1.3 Volume 3, Use Cases and General Requirements 4.1.4 Volume 4, Security and Privacy 4.1.5 Volume 5, Architectures White Paper Survey 4.1.6 Volume 6, Reference Architecture 4.1.7 Volume 7, Standards Roadmap 4.2 ISO/IEC JTC1´s Data Management and Interchange Standards Committee (SC32) 4.2.1 Next-Generation Analytics 4.2.2 Big Data 5 Big Data Standards Work 5.1 IEEE Big Data 5.2 ITU-T Big Data 5.3 ISO/IEC JTC1 WG 9 Big Data Working Group 5.4 JTC1 SC42: Artificial Intelligence 5.4.1 Membership 5.4.2 Working Groups and Study Groups JTC1 SC42 5.4.3 List of Published Standards in JTC1 SC42 5.4.4 List of Standards in Progress JTC1 SC42 6 Trends and Future Directions of Big Data Standards 6.1 Public Sector Information, Open Data and Big Data 6.2 European Commission-Funded Standards Projects 6.3 The Big Data Value Association (BDVA) 6.4 European Commission Standardisation Ongoing Activities 6.5 Open Consultation AI White Paper and Data Strategy 7 Future (Big) Data Standardisation Actions 7.1 ISO/IEC JTC1: Data Usage Advisory Group-AG9 7.2 ISO/IEC JTC1 SC42 AI WG2 Data 8 Summary References The Role of Data Regulation in Shaping AI: An Overview of Challenges and Recommendations for SMEs 1 Introduction 2 Framework Conditions for Big Data 3 The EU Landscape of Data Regulation 3.1 Data Governance Foundations 3.1.1 Data Governance and the Protection of Personal Data 3.1.2 Coding Compliance: The Role of Privacy-Preserving Technologies in Large-Scale Analytics 3.1.3 Non-personal Data (FFoD) 3.1.4 Security of Data 3.1.5 Intellectual Property 3.1.6 Public Sector Information and the Database Directive 3.1.7 Copyright Reform 3.1.8 Data Ownership 3.1.9 Data Economy 3.1.10 Competition 3.1.11 Data Sharing and Accessibility 3.1.12 Consumers, e-Commerce and e-Privacy 4 Conclusions 4.1 Recommendations for SMEs and Start-Ups 4.1.1 Potential of Privacy-Preserving Technologies 4.1.2 Distinction Between Personal and Non-personal Data 4.1.3 Data Security 4.1.4 Intellectual Property and Ownership of Data 4.1.5 Use of Consumer Data: Importance of Transparency and Informed Consent References Part IV: Emerging Elements of Big Data Value Data Economy 2.0: From Big Data Value to AI Value and a European Data Space 1 Introduction 2 The AI Value Opportunity 3 AI Challenges 3.1 Business Models 3.2 Trust in AI 3.3 Ecosystem 3.4 Technology 4 Towards an AI, Data and Robotics Ecosystem 4.1 European AI, Data and Robotics Framework 4.2 Innovation Ecosystem Enablers 4.3 Cross-Sectorial AI, Data and Robotics Technology Enablers 5 A Common European Data Space 6 Summary References