This is the problem the Data Space Adoption Forum (DSAF) was set up to solve. 

What the Data Space Adoption Forum is 

The DSAF is an IDSA Working Group, convened by the International Data Spaces Association (IDSA) as a neutral, non-commercial coordination ground. It brings together data space operators, cloud and managed service providers (CSPs and MSPs), trust framework providers, and industry representatives to co-develop the services and distribution mechanisms that make data space participation accessible at scale. 

IDSA does not deliver data space services itself. Its role is to provide the neutral coordination layer where players with complementary capabilities can align, co-develop, and agree on shared technical foundations without the risk of one participant’s commercial interests distorting the outcome. 

The founding partners of the DSAF include IDSA, Gaia-X, CISPE (Cloud Infrastructure Services Providers in Europe), leading automotive and manufacturing ecosystems, and cloud and managed service providers. This combination of standards bodies, infrastructure providers, and data space operators reflects the full chain required to move from technical specification to market-ready service. 

The problem to solve  

Industrial data spaces need to scale to millions of participating companies across global supply chains. The current onboarding model cannot support this. Each participant today deploys its own connector stack. Each company procures, configures, and maintains its own infrastructure. Compliance with data space governance requirements must be established from scratch for each organization. The time and cost involved means that, in practice, only large companies can participate. 

The DSAF works on an alternative model: participation as a managed service. Under this model, a cloud or managed service provider hosts the data space infrastructure on behalf of its customers. An SME subscribes to a data space onboarding service through a provider it already works with, accesses a compliant and certified environment, and participates in the data space without needing to build or maintain anything itself. 

This shifts data space participation from a capital-intensive IT project to a subscription service. It is the same shift that cloud computing made for general enterprise IT, applied to trusted data sharing. 

How the forum works 

Work inside the DSAF is organized into parallel workstreams that progress solutions from definition through to implementation. These workstreams cover joint technical coordination, shared onboarding foundations, and go-to-market planning. Solutions are not only specified collectively; they are owned and deployed by the participating actors. This means the Forum produces real implementations in operational environments, not specifications that sit on a shelf. 

IDSA coordinates the workstreams and ensures alignment across the participating organizations. The Forum operates under IDSA’s governance model, which guarantees neutrality and ensures that outputs can be adopted across multiple data space ecosystems. 

Where things stand 

The DSAF launched in February 2026. The first production-ready managed service infrastructure is on track for availability by the end of 2026. 

This is the right moment for cloud and managed service providers to engage. The technical foundation is being finalized. The first production deployment is scoped and under development, anchored in a real and large-scale use case in the automotive sector. Providers who join now can shape the service model, participate in the first commercial deployments, and position themselves in a distribution channel that will serve all data spaces, across all domains. 

How to get involved 

There are three ways to participate in the DSAF. The first is full IDSA membership, which gives access to all IDSA Working Groups including the Forum, voting rights on direction, and full ecosystem access. The second is direct sponsorship, which provides visibility and participation rights in Forum activities and joint go-to-market. The third is a subscription model, which gives seat-based access to Forum workstreams and materials. 

For cloud and managed service providers, the Forum is a direct route into a market that their existing customer relationships already position them to serve. The technical work is progressing. The deployment window is opening. The question is whether your organization is in the room. 

Contact: DSAF@internationaldataspaces.org | data-space-adoption-forum.org   

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An academic partner with reach

IIT Dharwad is an ambitious institution, it operates within a network that spans established industry partners, government bodies and other academic institutions across India. That connectivity is part of what makes it a useful anchor for this kind of initiative. Building a data space ecosystem is as much an organisational challenge as a technical one, and it requires someone who can convene people across sectors over a sustained period.

What is actually planned

The MoU sets out a practical work programme that covers several concrete areas. IDSA and IIT Dharwad will develop business-driven use cases across sectors including telecommunications, automotive, manufacturing, agriculture, smart mobility and railways, with telecommunications and automotive as the initial focus. Alongside that, the partners will run joint projects designed to demonstrate the economic value of data spaces in practice.

Building broader awareness is also part of the plan. That includes outreach to organisations across India that are considering data sharing approaches, as well as support for connecting corporates, government agencies and industry bodies to accelerate implementation.

The collaboration will also produce joint publications: white papers, case studies, market research and technical reports. A national data space think tank is a stated goal, bringing together universities, public-private organisations and government bodies.

The longer-term aim is an IDSA Hub and a dedicated Competence Centre in India. The India Dataspace Task Force, to be established through an upcoming bootcamp workshop, will set the priorities and sequence for what gets built and when.

The people involved

The MoU was signed by Lars Nagel, CEO of IDSA, and Thorsten Hülsmann, CFO of IDSA, together with Dr. Kalyan Bhattacharjee and Dr. Koteswararao Kondepu from IIT Dharwad. Shanawaz Sheik (IDSA Ambassador), Dr.-Ing. Ahmad J. Rusumdar, Christoph Mertens and Anna Derevianko were instrumental in bringing the collaboration to this point.

“India has the scale, the sectors and the institutional appetite to build something significant here. What has been missing is a structured way to connect those pieces. This partnership is a starting point for that.”
Christoph Mertens, Head of Adoption | IDSA

Where things stand

India’s data space ecosystem is taking shape. Early institutional commitment is building, and the partnership between IDSA and IIT Dharwad adds a formal foundation to that momentum. The bootcamp workshop is the next concrete step, and the task force it produces will set the direction for what follows.

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The governance challenge agentic AI creates 

Traditional access control was built for human users and fixed service accounts. It does not capture an autonomous agent whose model, tools and purpose can change between sessions. Agentic participation creates six distinct pressures on data space governance: speed (agents issue far more requests than humans and act without manual approval), ambiguity (natural-language instructions can be underspecified), composability (a permitted first step can lead to an impermissible later one), malleability (behavior shifts with model updates or adversarial inputs), epistemic risk (agents can produce plausible but false outputs) and a widened attack surface (prompt injection, data exfiltration and agent impersonation all become possible when agents reach external systems). 

These six pressures share one root. Autonomous action that can cross organizational boundaries must stay bound to an accountable participant, an explicit purpose, a policy decision and an auditable record — even when the agent is fast, adaptive and only partly predictable. 

Delegated identity solves the attribution problem 

The paper’s solution is the Delegated Agent Participant ID: a Verifiable Credential that links an executing agent back to its legally liable organization. Before any data transaction begins, the data space Connector verifies six elements within it: the organization (as a cryptographically verified Decentralized Identifier of the legal entity), the agent instance (a transient runtime identifier for the specific session), the model powering the agent’s reasoning, the tool registry profile listing the external capabilities the agent may use, the purpose scope (a machine-readable declaration of intent that must align with the provider’s ODRL usage policies) and the assurance level (the agent’s trust tier backed by testing certificates or accreditations). 

This structure separates three identity layers that have different lifecycles and different accountability owners. The legal participant identity is static and permanently anchored to a corporate entity. The agent identity is transient, tied to a specific task and session. The tool and service identity is tied to version control and software development. Keeping these three layers distinct is what makes the whole system auditable. 

When an authorized agent tasks a secondary agent across organizational boundaries, the data space governs this through cascading delegation. The secondary agent inherits the boundaries of the primary agent’s credential — specifically its purpose scope and assurance level. Usage policies, sandboxing constraints and legal liability flow down the chain. No sub-agent can reach data or take actions the primary organization was not authorized to handle. 

Agent roles in practice 

The paper describes seven distinct agent roles that emerge in data space operation. A discovery agent continuously searches the catalogue for datasets and offerings that match a standing need. A data-gap analysis agent compares an organization’s requirements against what is available and identifies what is worth acquiring. A negotiation agent conducts the contracting process, proposing and counter-proposing usage policies, pricing and terms through the data space’s negotiation flow, with a human approving the final agreement. A cataloguing and curation agent enriches metadata and keeps the knowledge graph current. A conformity and compliance agent checks offerings and policies against verifiable credentials and the regulatory context. A provenance and observability agent reconstructs lineage and monitors usage. A matchmaking agent pairs supply and demand across the ecosystem. 

These roles compose in practice. A typical workflow chains them: a scout agent finds candidate partners, a gap-analysis agent confirms the data closes a real need, a conformity agent verifies the offering’s credentials and a negotiation agent settles terms. Each acts through standard data space interfaces under its delegated identity. 

Evidence from live pilots 

The paper documents pilots that show this working in practice. NEC and EverySense Japan demonstrated automated negotiation of data-trade terms, where two agents — one representing a data buyer, one a data seller — negotiated terms across three dimensions (data characteristics, sharing conditions and price) without human intervention, reaching agreement in around 80 seconds with approximately a 95% automated agreement rate. The terms negotiated by the agents are explicit and auditable; the conclusion of any legally binding contract remains with the participating parties. 

The RoX consortium, a German initiative for data-ecosystem infrastructure in AI-based robotics, uses data space technology as the foundation for sovereign exchange of machine, process and quality data across competing organizations. Its outlook is explicitly agentic: the same governed data supply that feeds robotic applications is intended to feed an agentic layer that automates and orchestrates tasks under the data space’s existing controls. 

The trust formula 

The paper proposes a trust formula as a practical design heuristic: the model may propose, the knowledge engine checks, the policy engine authorizes, the data space records and the human steward remains accountable. The formula is multiplicative. If any factor is absent, trust collapses. A fast AI system without policy is not trustworthy. A policy system without semantics cannot decide correctly. A knowledge base without verification can turn errors into executable rules. A data space provides the environment in which these factors can be assembled and enforced together. 

The path ahead 

The Dataspace Protocol (DSP) — currently undergoing international standardization as ISO/IEC DIS 26450 — separates the control plane (identity, access negotiation, policy decisions) from the data plane (where exchanges happen and where agent-specific protocols such as MCP and A2A can operate). Keeping the two separate is what allows agent tooling to evolve on the data plane without loosening the governance that holds on the control plane. 

Several items remain open. There is not yet an agreed specification for how agent delegation is declared, how a model and its tests are published as governed assets, or how cascading delegation is governed at scale across organizations. The paper’s task force is carrying this work forward, including into the IDSA Rulebook’s AI Agents chapter. An international testbed for data spaces and AI, positioned within the EU Data Union Strategy and Apply AI Strategy, is planned as the environment in which safety, compliance and cross-border interoperability are validated before capabilities reach production. 

The paper is available at Data Spaces and AI: Trustworthy Agentic Participation in Data Spaces. Organizations working on AI integration or data space adoption are invited to engage with the IDSA task force and contribute pilots, infrastructure and standards feedback through the community. 

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Data spaces enable sovereign, rule-based data exchange between organisations. The technical foundations and governance options for this are in place: standards, architectures and reference implementations have been developed. Now the task is to transfer these building blocks into application and scale them across industry and national borders. This is where the cooperation between the three organisations comes in. They are bundling competencies, networks and resources in order to bring trusted data infrastructures into widespread use – as a driver of innovation, competitiveness and digital sovereignty.

Connecting competencies, accelerating application

As an independent anchor of trust, the acatech Foundation brings in its ecosystem from business, science and politics. It also strengthens the partnership with experience from practical transfer projects such as the Mobility Data Space and its practice-oriented work in the pre-competitive sphere.

Gaia-X and IDSA contribute their international network of hubs and competence centres as well as the established technological and organisational foundations of sovereign data spaces – including the IDSA Rulebook, Manifesto for Data Spaces and Reference Architecture Model, and the Gaia-X Trust Framework. These proven building blocks are to be strengthened through the cooperation and scaled in use.
Planned activities include joint initiatives, mutual involvement in network and exchange formats, awareness and knowledge sharing campaigns, and coordinated activities to position sovereign data spaces vis-à-vis politics, business and society as an integral element for conducting the multi-faceted transformational changes we are encountering these days.

Statements on the cooperations

Manfred Rauhmeier, Board Member of the acatech Foundation: “The phase of concepts is behind us – now implementation counts. With IDSA and Gaia-X, we connect our ecosystem with the leading initiatives for trusted data spaces and bring them to where they create value: in the practice of business and society.”

Ulrich Ahle, CEO of Gaia-X AISBL: “Trusted data infrastructures are no longer a promise for the future, but ready for use. Together with the acatech Foundation, we are accelerating their application – in Germany, Europe and worldwide.”

Lars Nagel, CEO of IDSA: “We have developed the foundation for trusted and sovereign data sharing – now we need partners who bring them into wide-scale application. The cooperation with the acatech Foundation is an important step to bring data spaces into practice.”

About the partners

acatech Foundation: The acatech Foundation sees itself as an impulse generator for the responsible shaping of digital transformation. With its activities, it focuses on practice-oriented transfer projects to promote competitiveness and accelerate innovation in Germany.

Gaia-X European Association for Data and Cloud AISBL: Gaia-X AISBL is an international non-profit organisation based in Brussels. It develops the Gaia-X Trust Framework as a basis for trusted, sovereign data infrastructures and promotes their dissemination through an international network of national Gaia-X Hubs.

International Data Spaces Association (IDSA): IDSA is a non-profit association with more than 180 members from companies, organisations and research institutions in 33 countries. It has developed over the last 10 years the technical and governance foundations for global data spaces, including the IDS Reference Architecture, the IDSA Rulebook and international standards

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Trust as a verifiable property

The paper frames trust as a precondition, not an outcome. An organization will allow an external agent to act on its data only if it can verify, before any action, that the agent, the data and the services involved are what they claim to be.

In a data space this verification is automated. Verifiable Credentials carry fitness attestations, accreditation proofs and conformity certificates alongside the assets they describe. A participant can check them at the point of use without retrieving documents manually and without gaps in the audit chain. The trust hierarchy that organizes these credentials means each check can be verified locally without re-involving whoever issued it.

This makes two distinct things verifiable. The first is the AI system itself: the quality and conformity of a model become transparent through data space credentials, and a bill of data, a bill of software and a bill of licences can document what went into its training. The second is data access: participants have full transparency over what data may be used for what purpose, which directly supports both intellectual property protection and compliance with data governance rules.

Data quality and what it means for AI

One of the most persistent challenges for AI is not a lack of algorithms but a lack of reliable, well-described and legally usable data. AI systems depend on data that is accurate, representative, timely and sufficiently complete for the purpose. Across organizational boundaries, this challenge is sharper: the relevant data often sits with competitors, public authorities, suppliers or research institutions, and even where it exists it may be withheld for reasons of confidentiality, commercial sensitivity or privacy.

A data space addresses this by creating a governed environment for trusted sharing without centralizing the data. Each dataset carries its provenance, collection method, update frequency, quality indicators, semantics, usage constraints and licensing — the context a developer needs to judge whether it is fit for a given purpose. Treated this way, a dataset becomes what the paper calls a data product: not raw signals, but information refined for reuse, carrying the content, quality, context and machine-readable format that a model or application can consume directly.

Observability and regulatory compliance

The paper covers the regulatory landscape in depth. Across the EU, Japan, China, South Korea, Brazil, India and the United States, the approaches to AI governance differ — from binding horizontal regulation under the EU AI Act (Regulation 2024/1689) to promotion-oriented soft law and sector-specific rules. The common thread is that whatever the regulatory style, data spaces supply the operational mechanisms that turn governance expectations into verifiable practice: identity, usage policies, provenance and audit logs.

For EU-based organizations, the alignment is direct. The EU AI Act’s requirements around data governance, documentation and traceability can be demonstrated in practice rather than only asserted, because data space infrastructure records what was exchanged, under which policies, by whom and for what purpose. GDPR compliance is supported in the same way. The European Health Data Space Regulation already mandates compute-to-data approaches for secondary use of health data, and data spaces such as genome.de and sphin-X are implementing these approaches in practice.

Observability and traceability — the ability to reconstruct where data came from, how it was modified, who accessed it and whether it was used according to agreed conditions — are core operational capabilities of a trustworthy data-sharing environment, as defined in ISO/IEC 20151-1. The paper is specific about what this enables: AI governance shifts from a static documentation exercise to an operational capability.

The limits of data space guarantees

The paper is careful about the limits of what data spaces can provide. A data space cannot make a model fair or a dataset unbiased. It can make the provenance, quality, permitted uses and accountability of the data verifiable to everyone who relies on it. That turns several of the EU High-Level Expert Group’s trustworthy AI requirements from stated commitments into properties that can be checked.

Where synthetic data is shared in a data space, its synthetic nature can be explicitly declared through metadata, provenance information, usage policies and quality indicators. Consumers can then assess suitability without assuming equivalence with the original data. That transparency is the contribution — not a guarantee of fitness, but a basis for informed judgment.

The third post in this series covers agentic participation: how AI agents can act inside data spaces under delegated identity, and what governance structures make this safe at scale.

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The relationship runs in both directions, and that bidirectionality is what makes it worth examining carefully.

The governed data foundation AI needs

Most valuable data sits inside organizations that will not share it without legal clarity about permitted uses, liability and the boundaries of data sovereignty. Web scraping and generic platform terms of service cannot reliably reach this data. A data space can. It lets providers and consumers negotiate access terms directly, for each dataset and each intended use, under machine-readable usage policies that make those terms explicit and enforceable.

For AI development, this means access to curated, domain-specific datasets with verifiable provenance. Shared catalogues make data findable across organizational boundaries. Federated identity mechanisms extend trust between parties that have no prior relationship. Usage policies expressed in open standards such as the Open Digital Rights Language (ODRL) specify what data may be used for and under which conditions. These are the governance properties a general AI stack assumes are already settled. In most cross-organizational settings, they are not. Data spaces provide them.

The automation data spaces need to scale

Building a data space means integrating its components into the existing systems of many different participating organizations. That integration work is expensive. AI substantially reduces the cost by generating metadata, aligning schemas across heterogeneous backends, translating human-readable legal terms into machine-readable policies and monitoring policy compliance across participants. These are tasks that would otherwise require sustained manual effort for every new participant onboarded and every new dataset described.

The FAIR principles — Findability, Accessibility, Interoperability and Reusability — provide a practical way to map out where each direction of the relationship applies. On the findability dimension, AI supports metadata generation and semantic search, making catalogues more useful. On the accessibility dimension, the Model Context Protocol (MCP) provides a standardized interface through which AI systems can connect to heterogeneous data services. On interoperability, AI assists in aligning vocabularies and bridging terminology differences without requiring full upfront standardization. On reusability, AI can help translate legal terms into enforceable policies and assess whether datasets are fit for a given purpose.

Three patterns of collaboration

Research by Fujitsu and Fraunhofer ISST describes three patterns by which AI workloads draw on a data space. In collaborative model development, organizations train a shared model by exchanging data or model parameters, with the data space providing the usage policies and provenance records that govern what enters training. In inter-organizational model inference, one organization enriches its own model at inference time with data held by others, with the data space providing discovery, access control and usage conditions. In inter-organizational agent collaboration, autonomous agents from different organizations accomplish a task together, with the data space providing participant identity, contract negotiation and an auditable record.

These patterns recur across the pilots documented in the paper, from AI-enabled robotics in Germany to data marketplace negotiation in Japan. They are not theoretical. They are working in practice.

Implications for organizations

For AI practitioners, the paper is a caution against treating API connections as automatically trustworthy. A model-connected interface is not automatically compliant. A retrieval-augmented generation pipeline is not automatically authorized. Data spaces supply the missing layer: identity, contract, usage control, semantic interoperability and provenance.

For data space practitioners, the paper is a preparation guide. The familiar primitives — participants, credentials, catalogues, data products, Connectors, usage policies — are sufficient. What changes is that some participants will now be autonomous agents. Handling that requires making agent identity, scope, tool access and audit explicit. The foundations are already in place.

The next post in this series looks at how data spaces make AI trustworthy across organizational boundaries — covering the trust framework, verifiable identity and what the regulatory landscape requires.

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The collaboration will focus on aligning Tractus-X with core IDSA assets. These include the IDS Reference Architecture Model, governance frameworks, standards and protocols. Both organizations will support the evolution and implementation of essential data space standards such as DSP and DCP, along with data space components including connectors, data planes and digital twins. The partnership also extends to industry-specific KITs, with the goal of ensuring interoperability across industries and ecosystems.

The cooperation spans several strategic areas.

• Architecture and standards harmonization
• Governance, certification and security-by-design approaches
• Community building through workshops, hackathons and enablement activities
• Open-source infrastructure and development environments
• International scaling of data spaces and stronger collaboration across global initiatives

ARENA2036 and IDSA also intend to unlock synergies between Tractus-X, Manufacturing-X, the Data Spaces Support Centre (DSSC) and other European initiatives. The aim is to accelerate trusted data sharing and digital sovereignty at scale.

Trusted and interoperable data exchange forms the foundation for connected value chains, industrial AI and digital ecosystems. By combining their strengths, ARENA2036 and IDSA are taking a further step toward building a globally connected data economy.

Both organizations thanked everyone involved in the partnership and expressed confidence that the shared ambitions will translate into tangible results.

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Company Certificate Management in Catena-X addresses exactly this: a certificate is uploaded once and shared securely with all business partners, with auditable access. It is the entry use case of the Data Space Accelerator and a concrete way to experience what productive data exchange looks like in day-to-day operations.

What the Data Space Accelerator is

The Data Space Accelerator is a study investigating how companies move from initial readiness to productive data exchange in Catena-X. The potential of data spaces is widely recognized, while there is still limited empirical evidence on which factors explain successful onboarding and where the gaps behind slower adoption lie.

The study addresses this with a before-and-after survey design: participating companies are assessed at the start of onboarding and again after completing their first productive data exchange. This makes it possible to diagnose typical readiness gaps along the data value chain, identify company profiles and use cases with a higher likelihood of successful adoption, and derive targeted levers to accelerate onboarding. The reference data space is Catena-X, and the use cases are standardized Catena-X use cases documented in the open-source repository Tractus-X.

For participating companies, the study is designed to deliver:

• empirical evidence on the economic and organizational benefits of data sharing,
• actionable recommendations to strengthen digital readiness,
• case-based insights from multiple use cases.

What participation involves

Participation focuses on one or more concrete use cases implemented in real operations, supported by certified partners who guide companies through each step. The path follows six stages:

1. Submit an offer and choose the program path.
2. Review and acceptance against the tender documents.
3. Select the use case (entry: Company Certificate Management).
4. Guided onboarding into the Catena-X data space.
5. Complete the first productive data exchange.
6. Receive the milestone-based payout.

Two paths are available. The Basic Path covers Company Certificate Management. The Advanced Path adds one further Catena-X use case of the company’s choice — such as Product Carbon Footprint, Digital Product Passport, Traceability, Data Driven Quality, Demand and Capacity, Short-Term Supply (PURIS), or Circular Economy. Prior experience with data spaces is not required, and internal effort is designed to stay limited and predictable.

Remuneration based on verified results

Remuneration is milestone-based and tied to verified results: successful onboarding, implementation of the selected use case, and the first productive data exchange along that use case. The Basic Path provides €15,000 (net), and the Advanced Path adds a further €15,000 (net), up to €30,000 (net) in total. The amount reflects the milestone-based structure of the program rather than a reimbursement of a company’s actual implementation costs. For participants based outside Germany, it is recalculated using a purchasing-power index, as set out in the tender documents.

Who can take part

The study primarily addresses SMEs and Mid-Caps in automotive and adjacent industries, including machine building, that are not yet active in Catena-X. Companies of all sizes may apply, provided the program requirements are met. Eligibility extends to EU member states and to countries that have signed a Government Procurement Agreement with Germany, listed in the DSA Remuneration PPP Index.

Timeline and how to apply

All program activities and milestones are completed within 2026, and applications are open until the end of September 2026. Onboarding and the first productive data exchange need lead time, so an early start gives teams room to work through the steps with their onboarding partner.

Full details, participation requirements, and the application are available on the official program website. Questions on participation, eligibility, onboarding, remuneration, timelines, or study requirements can be directed to the program team.

Learn more and apply: data-space-accelerator.com

Contact: dsa@internationaldataspaces.org

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One participant captured the spirit of the discussion in a simple yet powerful statement:

If data spaces did not exist today, we would have to invent them.

The remark resonated because it reflected a broader realization shared across the roundtable. The conversation was not primarily about technology. Technical interoperability, standards, and federated architectures continue to advance at an impressive pace around the world. Rather, the discussion focused on a complementary challenge: How to create the governance, trust, and economic incentives necessary to make large-scale data sharing work in practice.

This challenge has become particularly urgent in the age of AI.

Artificial intelligence is dramatically increasing demand for high-quality, trustworthy, and context-rich data. At the same time, organizations are becoming more cautious about how their data is accessed, combined, used, and monetized. As AI systems become more powerful, questions about accountability, transparency, provenance, and control are moving from the margins to the center of the debate.

The rise of AI is therefore exposing a critical governance gap. While the world has made significant progress in developing technologies for data exchange, the governance mechanisms required for a functioning global data economy remain unresolved.

Who determines the conditions under which data can be used? How can compliance be ensured? How can data contributors retain meaningful control? And how can value generated from data be distributed fairly?

These questions are no longer theoretical – they are central to whether large-scale data sharing can work in practice.

This is where data spaces increasingly come into focus. Rather than serving merely as technical infrastructures for data sharing, they emerge as governance frameworks that combine interoperability, trust mechanisms, and shared rules for participation. Sovereignty is not an obstacle to data exchange – it is a prerequisite. Organizations are willing to share valuable data only when they can rely on clear rules, transparent governance, and enforceable usage conditions. Data spaces establish exactly these conditions, enabling responsible and scaled data sharing while preserving the control and trust that participants require.

The discussion also highlighted the importance of ensuring that the future data economy is truly global.

Participants stressed that global interoperability cannot be achieved through technology alone. It also requires governance approaches capable of bridging different regulatory environments, economic realities, and societal expectations.

This perspective became particularly visible in discussions about equitable participation in data-driven value creation. Several participants raised concerns about scenarios in which data from less economically mature regions contributes to global innovation without corresponding participation in the value generated. The challenge, therefore, is not only to enable global data flows, but also to ensure that trust, data sovereignty, and value creation can be shared more broadly across regions and communities.

Another area of strong consensus concerned business value.

Participants repeatedly emphasized that the long-term success of data spaces will depend on their ability to create tangible economic benefits. Organizations will not participate because a technology is elegant or because a standard exists. They will participate when data spaces help them solve real problems, unlock new opportunities, reduce costs, improve compliance, or create new forms of collaboration.

The question is therefore shifting from “How do we build data spaces?” to “What value do data spaces create?”

The Global Data Spaces Roundtable pointed to a broader conclusion: the future of the data economy will not be defined solely by our ability to move data across boundaries. It will be defined by our ability to establish trusted governance for how data is shared, used, and transformed into value.

As AI accelerates this transformation, data spaces are increasingly being viewed not merely as a technological concept, but as a governance framework for the next generation of the global data economy – one that can integrate emerging markets, respect sovereignty, and create equitable value for all participants.

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