What are data spaces – and why they are becoming increasingly relevant for companies.

The next evolutionary step in the data economy

In an increasingly networked economy, data is no longer an isolated commodity, but part of dynamic value creation networks. Data is created, flows, and changes – across corporate boundaries. Traditional data management systems quickly reach their limits. They are often centralized, inflexible, and not designed for controlled, context-dependent collaboration with third parties.

Dataspaces offer a new approach: They enable the trustworthy, decentralized, and sovereign exchange of data – across organizational boundaries, systems, and industries. This opens up entirely new possibilities for collaboration, value creation, and innovation for companies – while maintaining data sovereignty.

What are dataspaces?

A data space is an organizational and technical framework in which multiple actors can exchange and use data according to common rules without losing control over their data.

In contrast to traditional platforms where data is collected and processed centrally, a Dataspace is based on the principles of:

• Decentralization

• Interoperability

• Data sovereignty

• Trustworthiness through common standards

Dataspaces are therefore not databases, but networked ecosystems in which participants provide their data in a controlled manner and make it available for use by others – according to clearly defined governance models, technical interfaces and legal frameworks.

Why are data spaces relevant?

1. Promoting data-driven innovation

The controlled merging of heterogeneous data sources – such as those from partners, suppliers or customers – creates new data-based services, business models and insights , e.g. for predictive maintenance, CO₂ footprinting, smart products or AI applications.

2. Strengthening data sovereignty

Participants retain control over their data at all times – they decide who may use which data, for what purpose, and under what conditions. This is a crucial advantage, especially for sensitive or personal data.

3. More efficient collaboration in value networks

Dataspaces make it possible to make data-based collaborations faster, more secure and legally compliant , for example in Industry 4.0, logistics, agriculture, energy or the public sector.

4. Support for regulatory requirements

Initiatives such as GAIA-X , the EU Data Governance Act (DGA) , and the Data Act rely on trustworthy data spaces as the infrastructural basis of a European data market. Data spaces are thus compatible with European regulations and funding programs .

Application examples

• Catena-X : An open, collaborative data space for the automotive industry where OEMs, suppliers and service providers exchange their data along the entire supply chain.

• Mobility Data Space : Enables secure, interoperable data exchange for transport providers, municipalities and platforms.

• Healthcare Dataspaces : Research is being conducted, for example, within the framework of Gaia-X for cross-sector research and patient care while maintaining data protection.

• Energy and sustainability data spaces : Enable ESG reporting, circular economy data or shared environmental indicators along supply chains.

What companies need to pay attention to

1. A clear data strategy is a prerequisite

Dataspaces require companies to be able to capture, classify, and manage their data in a structured manner . Without internal data clarity, external data collaboration is not possible.

2. Legal framework & governance

The exchange of sensitive data requires clear governance structures : Who is allowed to do what? Who is liable? Which licenses apply? Issues of GDPR compliance and intellectual property rights must also be clarified in advance.

3. Technological interoperability

Participating systems must be technically connectable – e.g., via standardized APIs, ontologies, data formats, and metadata models. Only then will the vision of an open ecosystem become a reality.

4. Security and trust

Trust is created through transparent rules, auditability and technical protection measures such as encrypted data flows, access management and policy enforcement .

5. Organizational anchoring

Establishing or participating in a data space is not a purely IT project. Business, legal, IT, and strategy must work together – with clear roles, roadmaps, and KPIs.

Conclusion: Dataspaces as the key to data-driven ecosystems

In a networked economy, companies are no longer just data owners , but active designers of data ecosystems . Dataspaces enable the potential of data to be leveraged comprehensively, sovereignly, and in a value-creating manner – while adhering to legal and ethical standards.

They thus form a supporting infrastructure for:

• Platform economies with a European value foundation

• Digital sovereignty in global supply chains

• Sustainability and transparency requirements (e.g. CSRD, EU Taxonomy)

• Innovation capability in an AI-driven future

For companies that want to not only manage data but also use it strategically , data spaces are not a topic of the future – but a central field of action for the present .

References

• International Data Spaces Association (IDSA). (2022). Dataspaces – Fundamentals, Architecture, and Use Cases .

• European Commission. (2023). The European Strategy for Data .

• Fraunhofer ISST. (2022). Data Sharing in Practice – Frameworks for Trusted Ecosystems .

• GAIA-X AISBL. (2023). Dataspaces as Building Blocks for Digital Sovereignty .

• McKinsey & Company. (2021). The Emerging Role of Data Ecosystems .

• BMWK (Federal Ministry for Economic Affairs and Climate Protection). (2022). Practical Guide: Sovereign Data Exchange in Business .

• Palmirani, M., & Vitali, F. (2021). Data Governance in European Dataspaces . Journal of Data Law & Policy.