Foreword 3

Abbreviations and acronyms 6

Executive summary 7

1 Overview 10

 1.1. Objective: Assessing the distinctive value of data spaces 11

 1.2. Scope and approach 13

 References 15

 Endnotes 17

2 Understanding data spaces 18

 2.1. From data integration to data governance 19

 2.2. Emerging definitions and converging architectures 20

 2.3. A two-tier working definition 21

 2.4. Data spaces and related concepts 24

 References 26

 Endnotes 28

3 Multi-party co-ordination as the distinctive value proposition and challenge of data spaces 29

 3.1. The co-ordination problem behind multi-party data sharing 30

 3.2. Comparative advantage 32

 3.3. How data spaces address the co-ordination problem of multi-party data sharing 35

 3.4. The bootstrapping problem as central barrier to viability and adoption 40

 References 46

 Endnotes 49

4 The evolving policy landscape 53

 4.1. Regulatory frameworks that require multi-party data sharing 54

 4.2. Public investment and institutional design 56

 4.3. Standards, and cross-border interoperability and convergence 60

 4.4. Certification and public trust frameworks 62

 4.5. Inter-governmental (G2G) data spaces and government as operator and anchor participant 62

 References 66

 Endnotes 69

5 Conclusions and policy considerations 70

 5.1. Targeting investment where the structural condition holds 71

 5.2. Distinguishing between funding for the technical foundation and for ecosystems 71

 5.3. Addressing the demand-side funding gap 72

 5.4. Supporting business models around infrastructural value 72

 5.5. Supporting adoption of trust-enhancing technologies and their integration by design 73

 5.6. Adopting certification and government anchoring to build institutional trust 73

 5.7. Recognising AI-driven data demand as a pathway to viability 74

 5.8. Enhancing measurement 74

 References 75

Annex A. OECD Workshop on “Assessing the Sustainability of Business Models in Data Spaces and the Role of Public Policies” 76

FIGURES

Figure 1.1. Development stage distribution of 235 data space initiatives 13

Figure 2.1. Stylised architecture of a data space 23

Figure 3.1. Building block adoption rates: All entries vs. operational data spaces 32

Figure 3.2. Operational share by sector 34

Figure 4.1. EU public funding rate by development stage 58

TABLES

Table 3.1. Comparison of data-sharing mechanisms and enablers 33

Table 3.2. Governance configurations of data spaces based on type of actors 44

BOXES

Box 1.1. The evidence base: Data source limitations and the measurement gap 14

Box 2.1. The landscape of data spaces standardisation 21

Box 2.2. SDMX: A mature data-sharing ecosystem for official statistics 23

Box 2.3. The “data fabric”: Data mesh extended to inter-organisational settings 25

Box 3.1. The case of the Catena-X Automotive Network 36

Box 3.2. Privacy-enhancing technologies as complement to contract-based usage control 40

Box 3.3. The case of the Maritime Data Space 42

Box 3.4. The case of Agdatahub 43

Box 3.5. Telecommunications operators as data space service providers: The case of T-Systems 45

Box 4.1. The case of the Automotive and Battery Traceability Centre (ABtC) 56

Box 4.2. Inter-governmental (G2G) data-sharing frameworks in the public sector 64