Data Spaces and AI Factories: Building Europe's Data and AI Economy
Europe's Data Union strategy places data spaces, AI factories, and data labs at the centre of its vision for a thriving data and AI economy [1]. While these initiatives are often discussed separately, their real value emerges when they work together.
Trusted data is crucial for trustworthy AI services. Data spaces create trusted environments for sharing and using data. AI factories provide the computing power, expertise, and infrastructure needed to develop advanced AI solutions. Data labs, a newer concept emerging within AI factories, help bridge these worlds by preparing, enriching, and making data accessible for AI development. Recognising the importance of these connections, a dedicated DSSC task, “Data Spaces in the AI Continent”, supports connecting data spaces, AI factories, and data labs.
What Are AI Factories and Data Labs?
AI factories are ecosystems that combine computing infrastructure, data resources, and specialist expertise to support the development and deployment of AI solutions [2]. Their purpose is not only to provide high-performance computing resources but also to create innovation hubs that connect research organisations, supercomputing centres, universities, public organisations, SMEs, and industry.
Data labs are an emerging concept within a larger ecosystem [3]. Data labs will be initially implemented as part of AI factories. Data labs are being developed to strengthen the connection between AI infrastructure and data ecosystems. They provide services for data preparation, cleaning, enrichment, and interoperability. Their role is to make high-quality data easier to discover and use while reducing the effort required from data providers and AI developers.
Further developments are expected in an upcoming project in which data labs are getting established as part of AI Factories across seven initial domains: cybersecurity and internal security, climate and environment, language and culture, health and life sciences, manufacturing and robotics, public administration, and research and science.
Data spaces are enablers of trustworthy and well-governed AI services
AI depends on data throughout its lifecycle. Data is required to train and validate models, provide context during operation, and support AI-powered decision-making. However, developing effective AI solutions is not simply about accessing large amounts of data. High quality, relevance, provenance, and usage rights matter just as much. Poor-quality or poorly governed data can result in inaccurate, biased, or unreliable outcomes. Many organisations are also reluctant to share data because they want to retain control over who can access it, how it can be used, and for what purposes. As AI adoption grows, addressing these concerns becomes increasingly important.
This is where data spaces play a crucial role. Data spaces enable organisations to exchange data within agreed governance, contractual, and policy frameworks. They build the trust needed for collaboration by ensuring data providers maintain control over their assets while data users access valuable resources under transparent, agreed conditions. [4]
Several core capabilities make data spaces particularly valuable for AI:
Controlled access to data through policies, contracts, and access-control mechanisms.
Data sovereignty, allowing organisations to retain control over how their data is used.
Data provenance and quality information, helping AI developers understand the origin and context of data.
Machine-readable policies that can be associated with data products and enforced automatically.
Traceability and accountability through records of data exchanges, agreements, and access events.
Together, these capabilities create the foundation for trustworthy, properly governed AI.
How AI Factories Benefit from Data Spaces
AI factories require large volumes of high-quality, AI-ready data. Data spaces can provide access to this data while ensuring governance requirements are met. By connecting AI factories to data spaces, organisations can discover, access, and exchange data under clearly defined conditions. This allows AI developers to benefit from diverse, high-quality datasets while giving data providers confidence that their rights and usage conditions will be respected. The relationship is mutually beneficial. AI factories gain access to trusted data ecosystems, while data spaces gain new avenues for value creation and innovation.
Why Data Spaces Should Collaborate with AI Factories and Data Labs
The connection between data spaces, AI factories, and data labs offers several strategic benefits. First, data spaces can leverage services already provided by AI factories and data labs instead of building everything themselves. For instance, these infrastructures may offer Secure Processing Environments and data enrichment services, reducing costs and development effort.
Second, access to AI and data-lab services can make participation in a data space more attractive. Organisations may be motivated to join a data space not only to exchange data but also to gain access to supporting services such as storage, processing, or AI resources.
Third, these ecosystems can increase the visibility and discoverability of each other's services. A participant entering through one ecosystem may discover complementary services offered through another.
Finally, collaboration can enable entirely new services and business models. Examples include federated learning, multi-domain data analytics, and cross-sector AI applications that require access to data and capabilities distributed across multiple ecosystems.
By working together, data spaces, AI factories, and data labs can accelerate the adoption of both AI and data-sharing solutions while creating new opportunities for innovation.
Looking ahead
The relationship between data spaces, AI factories, and data labs is still evolving. Technical architectures, governance models, business arrangements, and legal frameworks all need further exploration. Yet the direction is clear: these initiatives are complementary building blocks of Europe's future data and AI economy.
We invite everyone interested in the topic to contact us and share their experiences and ideas. We are interested in discussing with forerunners so we can share lessons learned with others. We are also curating an inventory of material related to this topic, and all input is welcome.
Join the Conversation at the European Big Data Value Forum
The connection between data spaces and AI infrastructure is not only a technical question — it is a strategic priority for Europe's data economy. At the European Big Data Value Forum (EBDVF) on 1 October, the Data Spaces Support Centre (DSSC) is hosting the first dedicated exploratory discussion bringing together representatives from AI Factories and data spaces initiatives to explore exactly these synergies. This session marks an important step in bridging two complementary ecosystems that are central to Europe's AI and data ambitions. If you are working in this space, we would love to hear from you — reach out to the DSSC team and help shape the next steps of this work.
References
[1] EC, European Data Union Strategy. URL: European Data Union Strategy | Shaping Europe’s digital future
[2] EC, AI Factories. URL: AI Factories | Shaping Europe’s digital future
[3] BDVA, 2026. BDVA initiative in Data Labs – Ecosystem mapping. URL: BDVA initiative in Data Labs – Ecosystem mapping - BDV Big Data Value Association
[4] International Data Spaces Association, 2026. Data Spaces and AI: Trustworthy Agentic Participation in Data Spaces. URL: https://internationaldataspaces.org/download/56028/?tmstv=1784197010