Players and their roles in the data space

We have learned about the technical architecture of a data space – but technology alone does not make a functioning marketplace. A data space is more than the sum of its technical components; it is a living ecosystem comprising people, organisations and business relationships. Who are the players that bring this system to life? What roles do they play, and how do individual interests lead to shared success?

These questions lead us to the organisational level of a data space—the level that determines who can participate, what rules apply, and how all participants can fairly take part in value creation. This is where it becomes clear why data spaces are not just a technical innovation, but also a new model for economic cooperation.

Overview of the most important roles

A data space is an interactive network with different participants. Within this network, not only data but also services are consumed and provided. As a result, organisations participating in a data space often take on many different roles. This interaction gives rise to the following key roles in the data space:

• Data provider: Makes its own data available to others in the data space. For example, a sensor manufacturer that feeds in machine data.

• Data consumer: Uses external data, raw or structured data, or datasets from another actor to improve internal processes, enrich products, or gain new insights. Example: A logistics company imports traffic data to optimise routes.

• Service provider: Develops and offers data-based services, e.g., an AI provider that has built data analysis services.

• Service consumer: Accesses ready-made services that have often been developed based on shared data, e.g., an AI service for production optimisation. The user receives insights, forecasts, or tools directly, but not the raw data itself.

Innovative business models often emerge at the service level. Those who consume data need precise quality and licensing rules. Additionally factors such as support, further development and integration often come on top.

Federators and orchestrators play a special role: they ensure that the data space functions as a whole. Unlike providers or consumers, they typically have no direct commercial interest in the exchanged data, instead focusing on operating and further developing the infrastructure.

• Federator: This actor ensures that central technical services in the data space function reliably. These include authentication, data catalogues, protocol translators, and interfaces. The federator enables and facilitates data exchange without becoming an exchange partner itself.

• Orchestrator: The orchestrator has a coordinating function. Its role is to ensure that all relevant actors in the data space jointly make and implement important decisions regarding the data space. For example, it convenes committees in which decisions are made about the rules of the data space. Its most important task is to balance interests and ensure that all actors participate fairly and that no individual players dominate. The orchestrator has a neutral role and acts as a mediator.

Why are neutral federators and orchestrators necessary at all? In a decentralised system where many equal players work together, a coordinating authority is needed to ensure order and fairness. Orchestrators prevent individual powerful players from dominating the data space to their advantage – a risk that quickly arises in the data economy. This is precisely why federal, community-based models are so central in Europe.

The reality of the universal role

In practice, most participants in a data space take on several roles at the same time. This is not only normal, but even desirable, as it creates a balance of interests and prevents one-sided dependencies.

Let’s consider the participation of an automotive company in the Mobility Data Space: As a provider, the company makes vehicle location data and battery status available. At the same time, it uses weather data, traffic information, and charging station availability as a consumer. In this dual role, the company acts as a partner in the ecosystem – it gives and takes in different forms.

The ability to take on different roles in the data space – even simultaneously – opens valuable opportunities for companies to gradually expand their own offerings. Those who act flexibly can, for example, initially provide their own data and thereby gain insights into the requirements of other participants. At the same time, they can supplement their own services or data in a targeted manner by using relevant offerings from other players. This creates a continuous learning process: Companies not only expand their network and product portfolio but also develop a deeper understanding of the dynamics of the data economy. With each step, new opportunities for cooperation and innovative business models emerge, enabling companies to actively shape and further develop their path into the data economy.

Dynamic role changes

Things get interesting when companies dynamically change their roles depending on the situation and their needs. For example, a logistics company is normally a consumer of traffic and weather data. However, during a natural disaster, it could become an important provider by sharing its real-time information on passable routes with emergency services.

This flexibility in roles is a characteristic of successful data spaces. It shows that data spaces are not static marketplaces but living ecosystems that can adapt to changing circumstances.

Governance – the organisational level

Who makes the rules?

The governance of a data space encompasses all mechanisms that determine how decisions are made, rules are established, and conflicts are resolved. This organisational level is just as important as the technical infrastructure, because it determines whether a data space remains successful and trustworthy in the long term

Governance structures vary depending on the data space, but are always based on principles such as transparency, legitimacy, and efficiency. Important decisions are made jointly by the participants, often in specialised committees or working groups. Larger or strategically important partners may have more voting power, but no single actor can dominate the data space. A central organisation (orchestrator) is often established to play a special role in governance. This can be structured and organised in various ways. For example, it can be an association or a public-interest limited liability company. Various stakeholders can be involved in this organisation. These may include the key players in an industry, or a neutral organisation that mediates between them.

Governance example: the Mobility Data Space

The governance structure of the Mobility Data Space (MDS) ensures that strategic decisions are made transparently and in a balanced manner in the interests of all participants. At the centre is DRM Datenraum Mobilität GmbH as the operating company (orchestrator), whose majority shareholder is the German Academy of Science and Engineering (acatech). The neutral sponsorship by acatech as an independent and impartial institution is a key factor in its success. It ensures that the diverse interests of the participants – from automobile manufacturers and suppliers to research institutions and public institutions – are given equal consideration. This independent position enables acatech to make objective and long-term decisions that promote the development of a trusting ecosystem and ensure the sustainable operation of the data space.

The shareholders’ meeting is the central body responsible for managing the organisation and, in addition to acatech, consists of a broad spectrum of companies from the mobility sector and related fields, including Deutsche Bahn AG, Volkswagen AG, Deutsche Post DHL Group, and public actors such as the federal states of Bavaria, Baden-Württemberg, and North Rhine-Westphalia. The distribution of votes in the meeting is based on the size of the company shares, which enables representative and balanced representation of interests. The meeting elects both the supervisory board and the advisory board, which perform advisory and monitoring functions. The supervisory board monitors the management and compliance with the company rules while the advisory board provides support.

The role of the technical operator (federator), who is responsible for the stable and secure operation of the data space infrastructure, is also clearly defined in the contract. The technical operator’s tasks include both the operation and further development of the technical components and are essential for the scalability and reliability of the data space. The interaction between a neutral shareholder, structured committees, and a reliable technical operator creates a governance system that ensures transparency, fairness, and the avoidance of dominance by individual players. This lays the foundation for an open, trustworthy, and sustainable data economy in the mobility sector.

Development of common standards

Common standards are developed in an iterative process between technical working groups and practical use cases. This involves not only technical specifications, but also semantic standards: How are data formats defined? What metadata is required? How are quality levels measured and communicated?

This standardisation process is complex, but crucial for success. Only when all participants speak the same “language” they can work together efficiently. At the same time, the standards must be flexible enough to enable innovation and new use cases.

Business models in data spaces

Data spaces create the basis for new, sustainable business models in which companies can achieve economic added value not only through the ownership of data, but above all through its intelligent linking, use, and trading of data and services. It is important to emphasise that data spaces can be used to generate revenue not only from data itself, but also from data-based services – that is to say, digital services that create new benefits from data and offer these as added value on the market. These are based on the premise that value is not only created by owning data, but also by intelligently linking and using it.

Typical data-based service business models include, for example, AI-supported analysis or diagnostic services, automated decision-making aids, platforms for legally compliant data exchange, or marketplaces for data products and services. Data spaces thus open up a wide range of monetisation opportunities: from directly marketing of raw data to developing and distributing highly specialised, data-driven services.

From cost savings to new business areas

Participating in a data space is interesting for companies if they are aiming to save costs through more efficient processes, reduced waste, and optimised warehousing. This is a legitimate and important starting point, but the real potential lies in the development of completely new business areas.

For example, a mechanical engineering company might discover that its operating data, when combined with environmental data, provides valuable insights for sustainability consultancy. This means that the original machine manufacturer can also offer environmental consulting services – a business area that would not have been possible without access to data.

From closed chains to open networks

In traditional value chains, value is created linearly and often within a closed system. Data spaces break this pattern: they enable flexible, decentralised networking – value is no longer created at individual points, but continuously within the network of participants. Existing silos are broken down, and data and services from different providers can be brought together according to clearly defined, trustworthy rules. Companies can contribute to the value chain based on their core competencies – from data generation and analysis to the development of data-based services.

Monetisation and revenue models

The central question is: How can value be created and monetised in the data space? There are many ways to generate income in data spaces. The range of potential business areas is so wide that it is almost impossible to provide a complete overview. However, one possible approach to illustrate this would be to structure examples based on different roles:

1. Data generation and provision: Companies provide their own data as products or raw materials and receive direct, usage-based, or subscription-based revenue. The market value is determined by the quality, timeliness, or uniqueness of the data.
2. Data processing and data-based services: The greatest added value often comes from the use and intelligent linking of data. Companies can develop data-based services – such as AI-supported diagnostics, forecasts or process optimisations – and sell these as a service (e.g., as a subscription or pay-per-use).
3. Operating and orchestrating: Operators or orchestrators of data spaces provide the infrastructure and ensure security, standardisation, onboarding, and governance. They generate revenue through membership fees, platform fees, or additional services (e.g., certification, data quality services).
   Important: According to the provisions of the Data Governance Act, orchestrators are generally not allowed to operate on a for-profit basis but primarily generate revenue to cover their costs.

The special feature of the data space model is that value no longer arises solely from the ownership of data, but from its combined and context-related use. The business models are thus far more dynamic and open to innovation than in traditional platform structures.

Practical examples from the German Gaia-X funding competition

Four selected examples from the Gaia-X funded projects in Germany show how diverse and feasible these new business models are:

Car repair 4.0

• What? Medium-sized workshops provide diagnostic and fault data in the data space. On this basis, service providers develop AI-based services for more precise fault predictions and more efficient maintenance planning.
• How? The services are offered as a subscription or pay-per-use. The car repair shops benefit from better diagnostic options, save costs, and can monetise their data in a targeted and controlled manner. Service providers gain access to high-quality training data and can use it to develop innovative products.

EuroDaT

• What? EuroDaT acts as a neutral data trustee. Companies, public authorities, and research institutions can exchange and evaluate highly sensitive data in a legally compliant and anonymised manner.
• How? Funding is provided by usage fees that cover operating costs. Surpluses are used to expand the infrastructure and develop new use cases. EuroDaT thus serves as an independent enabler of complex, data-driven business models, especially for sensitive or legally protected data.

iECO

• What? The iECO data space offers numerous data-based services for the construction industry – from planning tools and construction progress monitoring to asset tracking. Various companies network, exchange data, and offer specialised services.
• How? Monetisation is achieved through pay-per-use fees for special services, onboarding or transaction fees. The data space serves as a shared marketplace where data providers, service providers and consumers can jointly test and scale new digital business logic.

MERLOT

• What? The MERLOT educational data space brings schools, students, course providers, and companies together. They exchange quality-assured data via the data space and enable AI-supported education and career assistants.
• How? Funding comes from membership fees (e.g., for schools/institutions), various subscription models for companies, and technical integration services. AI providers book prominent placements on the marketplace, and companies pay fees for access to talent and educational data.

Conclusion

A data space is much more than its technical infrastructure – it is a living, dynamic ecosystem supported by the interaction of diverse roles and players. The ability to take on different roles flexibly and situationally promotes innovation and new business models. Organisational governance forms the backbone, ensuring long-term trust and sustainable success through transparency, fairness, and neutral orchestration. With clear rules, common standards, and an open, cooperative structure, a data space not only enables more efficient processes but also opens up completely new value creation potential – from data-based services to innovative operating models. The result is a future-proof data economy that enables companies to actively and confidently participate in digital transformation and develop new business areas.