Designing Data Spaces The Ecosystem Approach to Competitive Advantage 1st Edition by Boris Otto, Michael ten Hompel, Stefan Wrobel ISBN 9783030939748 303093974X

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ISBN 10: 303093974X
ISBN 13: 9783030939748
Author: Boris Otto, Michael ten Hompel, Stefan Wrobel

Designing Data Spaces The Ecosystem Approach to Competitive Advantage 1st Edition Table of contents:

Part I: Foundations and Context
Chapter 1: The Evolution of Data Spaces
1.1 Data Sharing in Data Ecosystems
1.1.1 The Role of Data for Enterprises
1.1.2 Data Sharing and Data Sovereignty
1.1.3 Example Mobility Data Space
1.1.4 Need for Action and Research Goal
1.2 Conceptual and Technological Foundations
1.2.1 Data Spaces Defined
1.2.2 Roles and Responsibilities in Data Spaces
1.2.3 GAIA-X and IDS
1.3 Evolutionary Stages of Data Space Ecosystems
1.4 Designing Data Spaces
1.4.1 Ecosystem Perspective
1.4.2 Federator Perspective
1.5 Summary and Outlook
References
Chapter 2: How to Build, Run, and Govern Data Spaces
2.1 Data Space Design Principles
2.1.1 Entirely New Services for Users Based on Enhanced Transparency and Data Sovereignty
2.1.2 Level Playing Field for Data Sharing and Exchange
2.1.3 Need for Data Space Interoperability: The Soft Infrastructure
2.1.4 Public-Private Governance: Europe Taking the Lead in Establishing the Soft Infrastructure in a Coordinated and Collabora…
2.2 Building Blocks for Data Spaces
2.2.1 Technical Building Blocks
2.2.2 Governance Building Blocks
2.3 Synthesis of Building Blocks to Data Spaces
2.4 Harmonized Approach to Data Space Governance
2.5 The Way Forward and Convergence: Actions to Take in the Coming Digital Decade
References
Chapter 3: International Data Spaces in a Nutshell
3.1 International Data Spaces
3.1.1 Goals of the International Data Spaces
3.1.2 Reference Architecture Model
3.1.2.1 The International Data Spaces Components
3.1.2.2 The International Data Spaces Roles
3.1.2.3 Usage Control
3.1.3 Certification
3.1.3.1 Security Profiles
3.1.3.2 Participant Certification
3.1.3.3 Component Certification
3.1.4 Open Source
References
Chapter 4: Role of Gaia-X in the European Data Space Ecosystem
4.1 A Quick Introduction to Gaia-X
4.2 The Business World with Gaia-X
4.2.1 Economy of Data
4.2.2 Compliance
4.2.3 Measuring Success
4.3 The Gaia-X Principles
4.3.1 Objectives
4.3.2 Policy Rules and Specifications for Infrastructure Application and Data
4.3.3 Federated Services in Business Ecosystems
4.4 The Gaia-X Data Spaces
4.4.1 Finance and Insurance
4.4.2 Energy
4.4.3 Automotive
4.4.4 Health
4.4.5 Aeronautics
4.4.6 Travel
4.5 The National Hub Organization and the Launching of Additional Data Spaces
4.6 Conclusion: Data Spaces-The Enabler of Digital in Business
References
Chapter 5: Legal Aspects of IDS: Data Sovereignty-What Does It Imply?
5.1 Data Sovereignty: Freedom of Contract and Regulation
5.1.1 No Ownership or Exclusivity Rights in Data
5.1.2 Usage Control: Legally and Technically
5.1.3 Database Rights
5.1.4 Trade Secrets
5.1.5 Competition Law
5.1.6 EU Strategy on Data: The Relevance of Data Spaces
5.1.7 Data Governance Act: First Comments
5.1.8 Personal and Non-personal Data
5.1.8.1 GDPR
5.1.8.2 Free Flow of Non-Personal Data Regulation
5.1.9 Cybersecurity
5.1.9.1 NIS Directive
5.1.9.2 Cybersecurity Act
5.2 Preparing Contractual Ecosystems
5.2.1 Platform Contracts
5.2.1.1 Key Principles
5.2.1.2 Legal TestBed: A Lead Example
5.2.2 Data Licensing Agreements
5.2.2.1 The Contract Matrix
5.2.2.2 The IDS Sample Contracts
5.3 Implementing Compliance
5.3.1 GDPR
5.3.1.1 Controllers, Joint Controllers, and Processors
5.3.1.2 Documentation
5.3.1.3 Breach Notifications
5.3.1.4 Enforcement and Sanctions
5.3.2 Competition Law
5.4 Certifications from a Legal Perspective
5.4.1 Role of Procedural Rules
5.4.2 Additional Aspects
Chapter 6: Tokenomics: Decentralized Incentivization in the Context of Data Spaces
6.1 Tokenomics in the Context of Data Spaces
6.2 Token-Based Supply Chain Management
6.2.1 Supply Chain Traceability
6.2.2 Distributed Ledger Technology and Tokenomics
6.2.3 DLT-Based Supply Chain Traceability
6.3 Tokenomics in the Context of Personal Data Markets
6.3.1 Personal Data Markets
6.3.2 Motivational Factors for Tokenomics Approach in Personal Data Markets
6.3.3 Token Design Principles for Personal Data Markets
6.3.4 Derivation of Token Archetypes for PDMs
6.4 Conclusions
References
Part II: Data Space Technologies
Chapter 7: The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange
7.1 Introduction
7.2 Evolving Trust in the IDS Toward Self-Sovereign Identity
7.3 Definition of Contract Clauses: The IDS Usage Contract Language and Its Core Concepts
7.3.1 The Solid Access Control Model vs. IDS Usage Contract Language
7.3.2 Usage Control Dimensions
7.3.3 Operators for Usage Control Rules
7.4 The Policy Information Point
7.5 The Participant Information Service (ParIS)
7.6 Conclusion: The IDS-IM as the Bridge Between Expressions, Infrastructure, and Enforcement
References
Chapter 8: Data Usage Control
8.1 Introduction
8.2 Usage Control
8.2.1 Access Control
8.2.2 Usage Control
8.2.3 Usage Control Components and Communication Flow
8.2.4 Specification, Management, and Negotiation
8.2.5 Related Concepts
8.2.5.1 Data Leak/Loss Prevention
8.2.5.2 Digital Rights Management
8.2.5.3 User Managed Access
8.2.5.4 Windows Information Protection
8.3 Usage Control in the IDS
8.3.1 Usage Control Policies
8.3.1.1 Policy Classes
8.3.1.2 Policy Negotiation
8.3.2 Usage Control Technologies
8.3.2.1 Integration Concept
8.3.2.2 MY DATA Control Technologies
8.3.3 Logic-Based Usage Control (LUCON)
8.3.3.1 Degree (D)
8.3.3.2 Data Provenance Tracking
8.4 Conclusion
References
Chapter 9: Building Trust in Data Spaces
9.1 Introduction
9.2 Data Sovereignty and Usage Control
9.2.1 Data Provider and Data Consumer
9.2.2 Protection Goals and Attacker Model
9.2.3 Building Blocks
9.3 Certification Process
9.3.1 Multiple Eye Principle
9.3.2 Component Certification
9.3.3 Operational Environment Certification
9.4 Connector Identities and Software Signing
9.4.1 Technical Implementation of the Certification Process
9.4.2 Connector Identities and Company Descriptions
9.4.3 Software Signing and Manifests
9.5 Connector System Security
9.5.1 Trusted Computing Base
9.5.2 Remote Attestation
9.6 Conclusion
References
Chapter 10: Blockchain Technology and International Data Spaces
10.1 Introduction
10.2 Blockchain Technology
10.2.1 Basic Concept
10.2.2 Design Parameters
10.2.3 Smart Contracts
10.2.4 Opportunities of Blockchain Systems
10.3 Blockchain in International Data Spaces
10.4 Application Examples: Industrial Use Cases
10.4.1 TrackChain
10.4.2 Silke
10.4.3 Sinlog
10.4.4 BC for Production
10.5 Conclusion
References
Chapter 11: Federated Data Integration in Data Spaces
11.1 Introduction
11.2 Federated Data Integration Workflows in Data Spaces
11.2.1 A Simple Demonstrator Scenario
11.2.2 A Data Integration Workflow Solution for Data Spaces
11.3 Toward Formalisms for Virtual Data Space Integration
11.3.1 Logical Foundations for Data Integration
11.3.2 Data Integration Tool Extensions for Data Spaces
References
Chapter 12: Semantic Integration and Interoperability
12.1 Introduction
12.2 The Neglected Variety Dimension
12.2.1 From Big Data to Cognitive Data
12.3 Representing Knowledge in Semantic Graphs
12.3.1 Representing Data Semantically
12.4 RDF a Holistic Data Representation for Schema, Data, and Metadata
12.5 Establishing Interoperability by Linking and Mapping between Different Data and Knowledge Representations
12.6 Exemplary Data Integration in Supply Chains with ScorVoc
12.7 Conclusions
References
Chapter 13: Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning
13.1 Introduction
13.2 Big Data, Machine Learning, and Artificial Intelligence
13.3 An Open Platform for Developing AI Applications
13.4 Machine Learning at the Edge
13.5 Machine Learning in Digital Ecosystems
13.6 Trustworthy AI Solutions
13.7 Summary
References
Chapter 14: IDS as a Foundation for Open Data Ecosystems
14.1 Introduction
14.2 Barriers of Open Data
14.3 Related Work
14.4 International Data Spaces and Open Data
14.4.1 IDS as an Open Data Technology
14.4.2 IDS Components in an Open Data Environment
14.4.3 Benefits
14.5 The Public Data Space
14.5.1 The Open Data Connector
14.5.2 The Open Data Broker
14.5.3 Use Case: Publishing Open Government Data
14.6 Discussion and Conclusion
References
Chapter 15: Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure
15.1 Introduction
15.2 European Research Area
15.2.1 European Research Infrastructures and ESFRI Roadmap
15.2.2 European Open Science Cloud (EOSC)
15.3 Technology-Driven Science Transformation
15.3.1 Science Digitalization and Industry 4.0
15.3.2 Transformational Role of Artificial Intelligence
15.3.3 Promises of 5G Technologies
15.3.4 Adopting Platform and Ecosystems Business Model for Future SDI
15.3.5 Other Infrastructure Technologies and Trends
15.4 Defining Future Scientific Data Infrastructure
15.4.1 Paradigm Change in Modern Data-Driven/Digital Science
15.4.2 Timeline of the European RI Development/Evolution
15.4.3 General Requirements to Future Data-Driven Research Infrastructures
15.5 Proposed PRIaaS Architecture Model
15.5.1 Actualization Platform Components
15.6 Research Data Management in the Future SDI
15.6.1 European-Wide and International Initiatives and Projects
15.6.2 From FAIR Data Principles to STREAM Data Properties
15.7 Future Research and Development
References
Part III: Use Cases and Data Ecosystems
Chapter 16: Silicon Economy: Logistics as the Natural Data Ecosystem
16.1 The Digitization of Everything and Artificial Intelligence in Everything Will Change Everything for Everyone
16.2 Potential of the Silicon Economy for Logistics and Supply Chain Management
16.3 Silicon Economy inside
16.3.1 Big Picture/Vision
16.3.2 Silicon Economy Architecture
16.3.2.1 Architectural Patterns
16.3.2.2 Architectural Components
16.3.3 The Role of Open Source
16.4 Conclusion
References
Chapter 17: Agricultural Data Space
17.1 Digital Transformation in Agriculture
17.1.1 The Agricultural Domain
17.1.2 Agricultural Digital Ecosystem
17.1.3 Domain-Specific Challenges and Requirements
17.2 Agricultural Data Space (ADS)
17.2.1 Domain Architecture
17.2.2 Possible Levels of IDS Integration
17.2.3 General Benefits of an Interoperable Agricultural Data Space
17.3 Application Scenarios
17.3.1 Sustainable Management of Nutrient Cycle
17.3.2 New Business Models and Fulfilling Legal Obligations with Data in the ADS
17.3.3 Governmental Platforms
17.4 Summary and Outlook
References
Chapter 18: Medical Data Spaces in Healthcare Data Ecosystems
18.1 Introduction
18.2 Elements of Medical Data Spaces
18.3 Scenario 1: Health and Disease Management
18.4 Scenario 2: Integrated Care
18.4.1 Care Management as a Service
18.4.2 Patient Self-Monitoring as a Service
18.4.3 Risk Manager
18.4.4 Data Resource Browser and Patient Data Viewer
18.5 Precision Medicine
18.6 Healthcare Data Ecosystems
18.7 Structure of a Healthcare Data Space
18.8 User-Centered Concepts in a Healthcare Data Space
18.8.1 Trusted Users
18.8.2 Single Entry Point
18.8.3 Unified Health Report (aka Virtual Health Record)
18.9 Data Quality in the Medical Data Space
18.10 Conclusion
References
Chapter 19: Industrial Data Spaces
19.1 Motivation for Industrial Data Spaces
19.2 Industrial Perspective
19.3 Requirements Analysis in the IIoT
19.4 IDS-I Reference Use Cases
19.4.1 Collaborative Condition Monitoring (CCM)
19.4.2 Smart Factory Web (SFW)
19.5 Requirements Analysis for Data Sovereignty
19.6 Major Concepts of the International Data Spaces (IDS)
19.7 Exemplary Use Case Analysis
19.8 Outlook
References
Chapter 20: Energy Data Space
20.1 New ICT Solutions for Decentralized, Data-Intensive, and Distributed Processes
20.2 Use Cases in the Energy Data Space
20.2.1 Communications in Electrical Grids
20.2.2 Predictive Maintenance
20.2.3 Energy Management Gateway: From the Perspective of an SME
20.3 Early Demonstration Projects
20.3.1 Fraunhofer Demonstration Project “EnDaSpace ́ ́
20.3.2 Bauhaus.MobilityLab
20.4 Summary and Outlook
References
Chapter 21: Mobility Data Space
21.1 Mobility Data: The Status Quo
21.2 The Mobility Data Space: Architecture and Components
21.2.1 Data Sovereignty Through Usage Control
21.2.2 The Mobility Data Space as a Distributed System
21.2.3 Design and Operation of Central Components
21.3 The Mobility Data Marketplace (MDM) as Central Platform Within the Mobility Data Space
21.4 Datenraum Mobilität (DRM): A National Implementation in Germany
21.5 Connecting Data Platforms
21.6 Application Example: “Mobility Service Provider ́ ́
21.7 A Common Mobility Data Space: Outlook on a European Level
21.8 Implementation Within mFUND Research Projects
Part IV: Solutions and Applications
Chapter 22: Data Sharing Spaces: The BDVA Perspective
22.1 Introduction
22.2 Vision
22.3 Challenges
22.4 Call to Action
22.5 Convergence: Data Platform Projects of the Big Data Value PPP
22.5.1 The Portfolio of Projects
22.5.2 Cross-Cutting Challenges in Data Platforms
22.6 The Needs for Data Governance
22.7 Towards Trustworthiness of Industrial AI
22.8 Example: Smart Manufacturing Data Space
22.9 Conclusions
References
Chapter 23: Data Platform Solutions
23.1 Data Circulation: The Catalyst of Economic Value Creation
23.2 Trust as the Cornerstone of Data Exchanges
23.2.1 A Solid Data Exchange Environment at the Heart of the Data Value Chain
23.2.2 Regulating Ecosystems: Compliance at the Heart of Data Exchange
23.3 The Need for Traceability
23.4 Data Exchange Governance: Toward Hybrid Data Exchanges Platforms
23.4.1 Innovative Nature of the Hybrid Approach
References
Chapter 24: FIWARE for Data Spaces
24.1 Introduction to FIWARE
24.2 FIWARE and Data Spaces
24.2.1 Data Interoperability
24.2.2 Data Sovereignty and Trust
24.2.3 Data Value Creation
24.3 Toward Development of European Data Spaces
24.4 Conclusions
Chapter 25: Sovereign Cloud Technologies for Scalable Data Spaces
25.1 Introduction: Toward Open Clouds
25.1.1 Openness
25.1.2 Security and Trust
25.1.3 The Pillars of Digital Sovereignty
25.1.3.1 Data Sovereignty
25.1.3.2 Operational Sovereignty
25.1.3.3 Software Sovereignty
25.1.4 Partnering with European Companies
25.2 Technological Evolution from Cloud Native Applications to Data Spaces
25.2.1 Containerization: A New Paradigm
25.2.2 Container Orchestration: Kubernetes (k8s)
25.2.3 Service Mesh: Istio
25.2.4 Data Mesh
25.2.5 Data Space
25.3 Interoperability as a Key Enabler for Hybrid and Large-Scale Data Spaces
25.3.1 Big Data and Data Lakes: An Early Generation of Data Spaces
25.3.2 Gravitation and Expansion: The “Yin and Yang ́ ́ of a Successful Data Spaces Strategy
25.3.3 Portability and Interoperability: A Perfect Complement
25.3.4 Interoperability via Solutions-Specific Connector Implementations
25.4 Future Outlook
References
Chapter 26: Data Space Based on Mass Customization Model
26.1 Big Data Analysis and Mass Customization Model (MCM)
26.1.1 Introduction of Haier COSMOPlat
26.1.2 Overview of MCM in Haier COSMOPlat
26.1.3 MCM and Haier Interconnected Factory
26.1.4 From Mass Production to Mass Customization
26.2 Data Service and Data Flow of COSMOPlat
26.2.1 Current Data Service
26.2.2 Data Flow Based on COSMOPlat
26.3 Use Cases of MCM
26.3.1 Best Practices of Big Data Technology
26.3.2 Value Chain of Use Cases
References
Chapter 27: Huawei and International Data Spaces
27.1 Inherent Issues with Ecosystems
27.1.1 Functions and Natural Characteristics of Ecosystems
27.2 Concerns and Actions of Regulators
27.3 Issues with the Data Ecosystems and How Europe Intends to Address It
27.4 Role of IDS in Helping to Address these Concerns
27.5 Laws, Regulations, and National Standards in China on Data Protection
27.6 Why International Data Spaces Are Important
27.7 Specific Examples on How IDS and GAIA-X Can Be Used from Huawei Perspective
27.8 Conclusions
References
Chapter 28: International Collaboration Between Data Spaces and Carrier Networks
28.1 NTT ́s Business Domains, Mission, and Services
28.2 Key Requirements for Data Spaces
28.2.1 Why We Need Common, Secure, and Fair Data Spaces
28.2.2 Use Cases for International Industrial IoT Data Utilization
28.2.2.1 Use Case 1: Mobility as a Service (MaaS)
28.2.2.2 Use Case 2: Factory as a Service (FaaS)
28.2.2.3 Key Requirements for Future Global Data Platforms
28.3 NTT R&D Related to Data Spaces
28.3.1 IOWN (Innovative Optical and Wireless Network)
28.3.2 4D Digital Platform
28.3.3 Digital Twin Computing (DTC)
28.3.4 “DATA Trust ́ ́ and “Smart Data Platform with Trust ́ ́
28.4 Initiatives to Interconnect GAIA-X and NTT Platform
28.4.1 Collaboration Between IDS Connector and SDPF with Trust
28.4.2 Interconnect Factories in Europe and Japan with IDS
28.5 Future Prospects and Expectations for Data Spaces
References
Chapter 29: From Linear Supply Chains to Open Supply Ecosystems
29.1 Introduction
29.2 Data at the Center of Industrie 4.0
29.2.1 Data Ecosystems and Data Spaces
29.2.2 Standards
29.2.3 Trust and Security
29.3 SAP Asset Intelligence Network
29.4 Data Governance with IDS
Chapter 30: Data Spaces: First Applications in Mobility and Industry
30.1 Introduction
30.1.1 Data Is Broken and Data Space Is a Law
30.1.2 Data Exchange and Trading for Better and New Business
30.2 Transition to Data Space-Enabled Mobility
30.2.1 From Auto (Hardware) to Mobility (Service)
30.2.2 Traffic Is Broken: New Connected Mobility to the Rescue?
30.2.3 Synthesis of Simulation and Real-Life Data Space Prototyping
30.2.4 Simulation and Berlin Digital Twin: Benefits in Theory
30.2.5 Data Spaces: From “Mine, Mine, Mine ́ ́ to “Win-Win-Win ́ ́
30.2.6 From Simulation to Reality: IDS in NPM and RealLabHH
30.2.7 Catena-X Automotive Network and Deutsche Telekom
30.3 Industrial Data Spaces
30.3.1 Most Wanted Use Cases with Own Data: And with Shared Data
30.3.2 The Umati Story
30.3.3 Data Sovereignty for More Industrial Data Exchange
30.3.4 Make Your Choice
30.4 Conclusion
References
Chapter 31: Competition, Security, and Transparency: Data in Connected Vehicles
31.1 Data in Connected Vehicles
31.2 Plans of the Automotive Industry Thwart Competition
31.3 Quick Regulation of Data Access Needed
31.4 Ensuring Competition: Clear Rules for Access to Data
31.5 Ensuring Data Security Even with Competitive Data Access
31.6 Data Transparency and Data Ownership Create Confidence in New Technology
31.7 Need for Political Action
References
Chapter 32: Data Space Functionality
32.1 Introduction
32.2 Business Considerations for Data Sharing
32.3 Data Sovereignty Spectrum: From the Technical and Legal Perspective
32.4 The iSHARE Trust Mechanism for Data Spaces
32.4.1 Standards and Contract
32.4.2 Connecting Legal Entities to Data
32.4.3 iSHARE Roles and the IDS Reference Architecture
32.5 Practical Example: Data Sharing Along the Chain
32.5.1 Faster Inland Dispatch of Sea Freight
32.5.2 Faster Information for the Road Authority in Case of Truck Accidents
32.5.3 Inland Container Terminal Sharing Data Down Stream
32.6 Conclusions
Chapter 33: The Energy Data Space: The Path to a European Approach for Energy
33.1 Mission and Goals of the Energy Data Space
33.2 Challenges Addressed for the Energy Data Space in Context of GAIA-X
33.3 Solution: Data Space Description in a Holistic View-Detailed View on the Endeavor
33.3.1 Partners of the Ecosystem
33.3.2 Description Use Cases: Renewables
33.3.2.1 Renewables: Wind and Solar Asset Description Model
33.3.2.2 Renewables: Work Risk Prevention
33.3.2.3 Renewables: Common Taxonomy Definition: IEC Standards
33.3.3 Description Use Cases: Nuclear
33.3.3.1 Nuclear: Day-to-Day Collaboration Capabilities within GIFEN
33.3.3.2 Nuclear: Industry Observatory, Capabilities Mapping and Related Data Analytic Services
33.3.3.3 Nuclear: ESPN Digital Platform for the Nuclear Sector
33.3.3.4 Nuclear: eWork Platform
33.3.4 Description Use Cases: Low Carbon Hydrogen (H2)
33.3.4.1 H2: Import/Export International Routes Setting up
33.3.4.2 H2: Station Networks Information Sharing
33.3.4.3 H2: Mobility Asset Monitoring
33.3.5 Description Use Cases: Energy Efficiency
33.3.5.1 Energy Efficiency: Energy Renovation-Map Building Potential for Renovation
33.3.6 Description Use Cases: Electric Vehicle
33.3.6.1 Electric Vehicle: Energy Roaming
33.3.6.2 Electric Vehicle: New Services
33.3.6.3 Electric Vehicle: CPO and DSO Investment and Planning
33.3.7 Description Use Cases: Local Energy Communities
33.3.7.1 Local Communities: Local Communities of Energy Setting Up and Decentralization
33.3.7.2 Local Communities: Stadtwerke/Local Open Data for Business Models
33.3.8 Description Use Cases: Networks
33.3.8.1 Networks: Long-Term Scenarios
33.3.8.2 Networks: OrtoPhotos
33.3.8.3 Networks: Real-Time Data for EaaS Market Design Cross-Border
33.3.8.4 Networks: Congestion Management Through TSO-DSO Traffic Light
33.3.8.5 Networks: Cross-TSO Failure or Labelled Image Database for Predictive Maintenance Training
33.3.8.6 Networks: Energy Data-X
33.3.9 Description Use Cases: Compliance and Traceability
33.3.9.1 Compliance and Traceability: Green Certifications
33.3.9.2 Compliance and Traceability: Infrastructure Data for New Business Models
33.3.9.3 Compliance and Traceability: Existing Standards Integration to GAIA-X
33.3.9.4 Compliance and Traceability: Trusted HUB
33.3.10 Maturity Indication of the Data Space and Current Health Status
33.3.10.1 Addressing the Demand Side
33.3.10.2 Representing the Supply Side
33.3.10.3 Creating a Sustainable Business Model in the Data Space
33.3.10.4 Ramping up the TRL: From Prototype to Operation
33.3.10.5 Needed Component Certification from the GAIA-X Federation Services
33.4 Evolution of the Energy Data Space
33.4.1 Roadmap of the Evolution
33.4.2 Quick Wins (for 2021)
33.4.3 Mid-Term Benefits (2022-2023) Building on Already Launched or Soon-to-Be-Launched Projects
33.4.4 Long-Term Benefits Requiring Significant Investments on the 2021-2025 Period
Reference

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Tags: Boris Otto, Michael ten Hompel, Stefan Wrobel, Data Spaces, Competitive Advantage