Data spaces answer a question that holds back digital collaboration in industry: how can companies share data with partners without handing it over for good? In a data space, the data stays with its owner. Partners get access under agreed conditions, for a defined purpose, and the owner can see and control how it is used. This principle is called data sovereignty, and it is the foundation of initiatives such as Gaia‑X, Catena‑X and Manufacturing‑X. A data space provides the trust layer that a European data economy needs.
- Definition: a federated environment for trusted data sharing based on common rules and standards
- Core principle: data sovereignty, the owner keeps control over access and use
- Key building blocks: identities, connectors, catalogs, contract negotiation, usage policies, governance
- Important organizations: International Data Spaces Association (IDSA), Gaia‑X, Data Spaces Support Centre
- Industrial examples: Catena‑X (automotive), Manufacturing‑X and Factory‑X (manufacturing)
What is a data space, and how does it work?
A data space is not a database and not a central platform. It is a set of agreements and technologies that lets independent organizations exchange data directly with each other. Each participant connects through its own connector, a software component that handles identity, contracts and the actual data exchange. Before data flows, the provider and the consumer negotiate a contract that defines who may use the data, for what and for how long. Usage policies attached to the data make these terms machine-readable and enforceable.
Around the technical layer sits the governance framework: the rules every participant accepts, who may join, how trust is established and how disputes are resolved. A data space governance authority defines and maintains these rules. This combination of trusted identities, shared standards and common governance is what turns a loose network into a data ecosystem in which companies share data based on trust rather than individual integration projects.
Key components of a data space
- Identity and trust: participants prove who they are through verified identities and credentials, so a data provider knows exactly which data consumers it deals with.
- Connector: the gateway of each participant, for example based on the open-source Eclipse Dataspace Components.
- Catalog and discovery: data offers are published so that others can find them.
- Contract negotiation and usage policies: machine-readable rules that govern data access and use.
- Semantic standards: shared data models such as the Asset Administration Shell make the exchanged data understandable.
- Governance: the rule book, participation conditions and certification that create trusted data sharing.
Interoperability is the goal at every level, and open standards are the way to get there. The Dataspace Protocol, developed in the IDSA community, standardizes how connectors negotiate and transfer data, so that connectors from different vendors are interoperable and multiple data spaces can connect to each other.
How are data spaces different from platforms or cloud services?
On a platform, all participants upload their data to one operator, who controls the infrastructure and often the rules. In a data space, data stays where it is and is shared peer to peer. There is no central party that holds all the data. This makes the data space attractive for industrial data-sharing, where companies are reluctant to place production data, quality data or product designs on a third party’s platform. A cloud service can still host a connector or an application, but it is one participant among many, not the owner of the ecosystem.
Data sovereignty: why companies need control over their data
Many promising use cases fail because companies fear losing control: a supplier does not want a customer to see its margins, a machine builder does not want competitors to learn from its machine data. Data sovereignty means the ability to decide who uses one’s data, under which conditions, and to verify that these conditions are kept. Data spaces implement this through contracts, usage policies and logging. It links directly to digital sovereignty at the level of companies and countries, and to the EU’s goal of a single market for data.
The European framework: from data strategy to common data spaces
The European Union made data spaces a pillar of its data strategy. The 2020 European Strategy for Data announced common European data spaces for sectors such as manufacturing, mobility, health, energy and agriculture. Legislation supports this: the Data Governance Act sets rules for data intermediaries, and the EU Data Act gives users of connected products rights to access and share the data those products generate, including interoperability requirements for data spaces. The EU-funded Data Spaces Support Centre develops a common blueprint and building blocks, and the Data Spaces Business Alliance brings together IDSA, Gaia‑X, FIWARE and the Big Data Value Association.
IDSA, Gaia‑X, Catena‑X and Manufacturing‑X
| Initiative | Role |
|---|---|
| International Data Spaces Association (IDSA) | Develops the International Data Spaces reference architecture, the connector concept and the Dataspace Protocol |
| Gaia‑X | European initiative for a federated, trustworthy data infrastructure, with a trust framework and compliance rules |
| Catena‑X | Data ecosystem of the automotive industry for use cases such as traceability, carbon footprint and quality, based on open-source components |
| Manufacturing‑X | German initiative that transfers the data space approach to the wider manufacturing industry, with sector projects such as Factory‑X |
These initiatives build on each other rather than compete. Catena‑X uses the connector technology from the IDSA community, Manufacturing‑X builds on the foundations of Catena‑X and Plattform Industrie 4.0, and all of them use the Asset Administration Shell as the format for digital twins.
Factory‑X and the role of the OI4
Factory‑X is a collaborative project under the Manufacturing‑X umbrella that builds a trusted data ecosystem for factory outfitters and operators. It worked on 11 use cases that connect supply chain data with shop floor operations. The Open Industry 4.0 Alliance took part as one of 47 partners, alongside 10 associated partners, and contributed to four sub-projects: agile project management (TP1), basic services of the Factory‑X kernel with AAS-based reference implementations (TP4), transfer measures that make results usable for small and medium-sized companies (TP6) and coordination with Manufacturing‑X (TP7). Read more about the OI4 contribution to Factory‑X and the project results.
A key outcome is the MX-Port concept, an open architecture for data exchange across Manufacturing‑X data spaces. It organizes data exchange in five layers, from adapters and converters to discovery and access control, and aligns with the AAS. Factory‑X validated two initial configurations, called Hercules and Leo, to show how the concept fits different integration needs.
Use cases for industrial data spaces
- Product carbon footprint: suppliers share emission data along the value chain without disclosing their full cost structure.
- Digital Product Passport: component data flows from suppliers to the manufacturer of the end product, see Digital Product Passport.
- Traceability and quality: partners trace parts and quality issues across company borders.
- Machine services: machine builders access operating data from customer sites for maintenance and optimization, under contractually defined usage.
- Circular economy: recyclers and remanufacturers receive material and repair information.
Benefits and challenges of data spaces
The benefits are clear: secure data sharing without central data silos, standardized interfaces instead of individual integrations, new business models based on data, and trust through common rules. Data spaces also support value creation for small and medium-sized companies, which can join an existing ecosystem instead of building their own. The challenges are just as real. Onboarding still takes effort, governance needs time to mature, and the business case for each participant has to be clear. That is why transfer packages, reference implementations and practical demonstrators, as developed in Factory‑X, matter as much as the architecture.
Frequently asked questions
What is a data space in simple terms?
A data space is a set of rules and technologies that lets companies share data with each other directly and securely, while each company keeps control over its own data and decides who may use it.
Can you give an example of data sharing in a data space?
A component supplier shares the carbon footprint of a part with its customer through a connector. The customer may use the value only to calculate the footprint of its own product. The supplier’s other data stays private, and the usage terms are recorded.
What is the difference between Gaia‑X and a data space?
Gaia‑X is an initiative that defines a trust framework and rules for a federated European data infrastructure. Data spaces are the concrete ecosystems in which data is shared, and they can use the Gaia‑X framework to establish trust.
What is Manufacturing‑X?
Manufacturing‑X is a German initiative that brings the data space approach of Catena‑X to the manufacturing industry. Projects such as Factory‑X develop use cases, reference implementations and the MX-Port architecture under its umbrella.
What role does the Asset Administration Shell play in data spaces?
The AAS provides the standardized format for digital twins that is exchanged within data spaces. Catena‑X, Factory‑X and Manufacturing‑X all use it, so that shared data is understood the same way by every participant.