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Title page 1

Contents 5

Foreword 4

Abbreviations and acronyms 7

Executive summary 8

1. Overview 11

1.1. Objective: Assessing the distinctive value of data spaces 12

1.2. Scope and approach 14

References 16

Endnotes 18

2. Understanding data spaces 19

2.1. From data integration to data governance 20

2.2. Emerging definitions and converging architectures 21

2.3. A two-tier working definition 22

2.4. Data spaces and related concepts 25

2.4.1. Adjacent concepts 25

2.4.2. Equivalent initiatives 26

References 27

Endnotes 29

3. Multi-party co-ordination as the distinctive value proposition and challenge of data spaces 30

3.1. The co-ordination problem behind multi-party data sharing 31

3.2. Comparative advantage 33

3.3. How data spaces address the co-ordination problem of multi-party data sharing 36

3.3.1. Distributed governance 36

3.3.2. Open interoperability and portability 38

3.3.3. Identity, usage control and provenance for trust 39

3.4. The bootstrapping problem as central barrier to viability and adoption 41

References 47

Endnotes 50

4. The evolving policy landscape 54

4.1. Regulatory frameworks that require multi-party data sharing 55

4.2. Public investment and institutional design 57

4.2.1. Supply-side: Funding data space infrastructure 58

4.2.2. Demand-side: Funding participant adoption 60

4.3. Standards, and cross-border interoperability and convergence 61

4.4. Certification and public trust frameworks 63

4.5. Inter-governmental (G2G) data spaces and government as operator and anchor participant 63

References 67

Endnotes 70

5. Conclusions and policy considerations 71

5.1. Targeting investment where the structural condition holds 72

5.2. Distinguishing between funding for the technical foundation and for ecosystems 72

5.3. Addressing the demand-side funding gap 73

5.4. Supporting business models around infrastructural value 73

5.5. Supporting adoption of trust-enhancing technologies and their integration by design 74

5.6. Adopting certification and government anchoring to build institutional trust 74

5.7. Recognising AI-driven data demand as a pathway to viability 75

5.8. Enhancing measurement 75

References 76

Annex A. OECD Workshop on "Assessing the Sustainability of Business Models in Data Spaces and the Role of Public Policies" 77

Tables 6

Table 3.1. Comparison of data-sharing mechanisms and enablers 34

Table 3.2. Governance configurations of data spaces based on type of actors 45

Figures 6

Figure 1.1. Development stage distribution of 235 data space initiatives 14

Figure 2.1. Stylised architecture of a data space 24

Figure 3.1. Building block adoption rates: All entries vs. operational data spaces 33

Figure 3.2. Operational share by sector 35

Figure 4.1. EU public funding rate by development stage 59

Boxes 6

Box 1.1. The evidence base: Data source limitations and the measurement gap 15

Box 2.1. The landscape of data spaces standardisation 22

Box 2.2. SDMX: A mature data-sharing ecosystem for official statistics 24

Box 2.3. The "data fabric": Data mesh extended to inter-organisational settings 26

Box 3.1. The case of the Catena-X Automotive Network 37

Box 3.2. Privacy-enhancing technologies as complement to contract-based usage control 41

Box 3.3. The case of the Maritime Data Space 43

Box 3.4. The case of Agdatahub 44

Box 3.5. Telecommunications operators as data space service providers: The case of T-Systems 46

Box 4.1. The case of the Automotive and Battery Traceability Centre (ABtC) 57

Box 4.2. Inter-governmental (G2G) data-sharing frameworks in the public sector 65

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