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In the era of Artificial Intelligence (AI), data has ceased to be simple records and has become the essential fuel of innovation. However, for this fuel to really drive new services, more effective public policies or advanced AI models, it is not enough to have large volumes of information: the data must be varied, of quality and, above all, accessible.
In this context, the data pooling or Data Clustering, a practice that consists of Pooling data to generate greater value from their joint use. Far from being an abstract idea, the data pooling is emerging as one of the key mechanisms for transforming the data economy in Europe and has just received a new impetus with the proposal of the Digital Omnibus, aimed at simplifying and strengthening the European data-sharing framework.
As we already analyzed in our recent post on the Data Union Strategy, the European Union aspires to build a Single Data Market in which information can flow safely and with guarantees. The data pooling it is, precisely, the Operational tool which makes this vision tangible, connecting data that is now dispersed between administrations, companies and sectors.
But what exactly does "data pooling" mean? Why is this concept being talked about more and more in the context of the European data strategy and the new Digital Omnibus? And, above all, what opportunities does it open up for public administrations, companies and data reusers? In this article we try to answer these questions.
What is data pooling, how does it work and what is it for?
To understand what data pooling is, it can be helpful to think about a traditional agricultural cooperative. In it, small producers who, individually, have limited resources decide to pool their production and their means. By doing so, they gain scale, access better tools, and can compete in markets they wouldn't reach separately.
In the digital realm, data pooling works in a very similar way. It consists of combining or grouping datasets from different organizations or sources to analyze or reuse them with a shared goal. Creating this "common repository" of information—physical or logical—enables more complex and valuable analyses that could hardly be performed from a single isolated source.
This "pooling of data" can take different forms, depending on the technical and organizational needs of each initiative:
- Shared repositories, where multiple organizations contribute data to the same platform.
- Joint or federated access, where data remains in its source systems, but can be analyzed in a coordinated way.
- Governance agreements, which set out clear rules about who can access data, for what purpose, and under what conditions.
In all cases, the central idea is the same: each participant contributes their data and, in return, everyone benefits from a greater volume, diversity and richness of information, always under previously agreed rules.
What is the purpose of sharing data?
The growing interest in data pooling is no coincidence. Sharing data in a structured way allows, among other things:
- Detect patterns that are not visible with isolated data, especially in complex areas such as mobility, health, energy or the environment.
- Enhance the development of artificial intelligence, which needs diverse, quality data at scale to generate reliable results.
- Avoiding duplication, reducing costs and efforts in both the public and private sectors.
- To promote innovation, facilitating new services, comparative studies or predictive analysis.
- Strengthen evidence-based decision-making, a particularly relevant aspect in the design of public policies.
In other words, data pooling multiplies the value of existing data without the need to always generate new sets of information.
Different types of data pooling and their value
Not all data pools are created equal. Depending on the context and the objective pursued, different models of data grouping can be identified:
- M2M (Machine-to-Machine) data pooling, very common in the Internet of Things (IoT). For example, when industrial sensor manufacturers pool data from thousands of machines to anticipate failures or improve maintenance.
- Cross-sector or cross-sector data pooling, which combines data from different sectors – such as transport and energy – to optimise services, for example, the management of electric vehicle charging in smart cities.
- Data pooling for research, especially relevant in the field of health, where hospitals or research centers share anonymized data to train algorithms capable of detecting rare diseases or improving diagnoses.
These examples show that data pooling is not a single solution, but a set of adaptable practices, capable of generating economic, social and scientific value when applied with the appropriate guarantees.
From potential to practice: guarantees, clear rules and new opportunities for data pooling
Talking about sharing data does not mean doing it without limits. For data pooling to build trust and sustainable value, it is imperative to address how to share data responsibly. This has been, in fact, one of the great challenges that have conditioned its adoption in recent years.
Among the main concerns are the Protection of personal data, ensuring compliance with the General Data Protection Regulation (GDPR) and minimizing risks of re-identification; the confidentiality and the protection of trade secrets, especially when companies are involved; as well as the Quality and interoperability of the data, as combining inconsistent information can lead to erroneous conclusions. To all this is added a transversal element: the Trust between the parties, without which no sharing mechanism can function.
For this reason, data pooling is not just a technical issue. It requires clear legal frameworks, strong governance models, and trust mechanisms, which provide security to both those who share the data and those who reuse it.
Europe's role: from sharing data to creating ecosystems
Aware of these challenges, the European Union has been working for years to build a Single Data Market, where sharing information is simpler, safer and more beneficial for all actors involved. In this context, key initiatives have emerged, such as the European Data Spaces, organized by strategic sectors (health, mobility, industry, energy, agriculture), the promotion of Standards and Interoperability, and the appearance of Data Brokers as trusted third parties who facilitate sharing.
Data pooling fits fully into this vision: it is one of the practical mechanisms that allow these data spaces to work and generate real value. By facilitating the aggregation and joint use of data, pooling acts as the "engine" that makes many of these ecosystems operational.
All this is part of the Data Union Strategy, which seeks to connect policies, infrastructures and standards so that data can flow safely and efficiently throughout Europe.
The big brake: regulatory fragmentation
Until recently, this potential was met with a major hurdle: the Complexity of the European legal framework on data. An organization that would like to participate in a data pool cross-border had to navigate between multiple rules – GDPR, Data Governance Act, Data Act, Open Data Directive and sectoral or national regulations—with definitions, obligations, and competent authorities that are not always aligned. This fragmentation generated legal uncertainty: doubts about responsibilities, fear of sanctions, or uncertainty about the real protection of trade secrets. In practice, this "normative labyrinth" has for years held back the development of many common data spaces and limited the adoption of the data pooling, especially among SMEs and medium-sized companies with less legal and technical capacity.
The Digital Omnibus: Simplifying for Data Pooling to Scale
This is where the Digital Omnibus, the European Commission's proposal to simplify and harmonise the digital legal framework, comes into play. Far from adding new regulatory layers, the objective of the Omnibus is to organize, consolidate and reduce administrative burdens, making it easier to share data in practice.
From a data pooling perspective, the message is clear: less fragmentation, more clarity, and greater trust. The Omnibus seeks to concentrate the rules in a more coherent framework, avoid duplication and remove unnecessary barriers that until now discouraged data-driven collaboration, especially in cross-border projects.
In addition, the role of data intermediation services, key actors in organizing pooling in a neutral and reliable way, is reinforced. By clarifying their role and reducing certain burdens, it favors the emergence of new models – including technology startups – capable of acting as "arbiters" of data exchange between multiple participants.
Another particularly relevant element is the strengthening of the protection of trade secrets, allowing data holders to limit or deny access when there is a real risk of misuse or transfer to environments without adequate guarantees. This point is key for industrial and strategic sectors, where trust is an essential condition for sharing data.
New opportunities for data pooling: public sector, companies and data reuse
The regulatory simplification and confidence-building introduced by the Digital Omnibus is not an end in itself. Its true value lies in the concrete opportunities that data pooling opens up for different actors in the data ecosystem, especially for the public sector, companies and information reusers.
In the case of public administrations, data pooling offers particularly relevant potential. It allows data from different sources and administrative levels to be combined to improve the design and evaluation of public policies, move towards evidence-based decision-making and offer more effective and personalised services to citizens. At the same time, it facilitates the breaking down of information silos, the reuse of already available data and the reduction of duplications, with the consequent savings in costs and efforts.
In addition, data pooling reinforces collaboration between the public sector, the research field and the private sector, always under secure and transparent frameworks. In this context, it does not compete with open data, but complements it, making it possible to connect datasets that are currently published in a fragmented way and enabling more advanced analyses that expand their social and economic value.
From a business point of view, the Digital Omnibus introduces a significant novelty by expanding the focus beyond traditional SMEs. The so-called small mid-caps, mid-cap companies that also suffer the impact of bureaucracy, are now benefiting from regulatory simplification. This significantly increases the base of organizations capable of participating in data pooling schemes and expands the volume and diversity of data available in strategic sectors such as industry, automotive or chemicals.
The economic impact of this new scenario is also relevant. The European Commission estimates significant savings in administrative and operational costs, both for companies and public administrations. But beyond the numbers, these savings represent freed up capacity to innovate, invest in new digital services, and develop more advanced AI models, fueled by data that can now be shared more securely.
In short, data pooling is consolidated as a key lever to move from the punctual sharing of data to the systematic generation of value, laying the foundations for a more collaborative, efficient and competitive data economy in Europe.
Conclusion: Cooperate to compete
The proposal of data pooling in the Digital Omnibus marks a before and after in the way we understand the ownership of information. Europe has understood that, in the global data economy, sovereignty is not defended by closing borders, but by creating secure environments where collaboration is the simplest and most profitable option.
Data pooling is at the heart of this transformation. By cutting red tape, simplifying notifications, and protecting trade secrets, the Omnibus is taking the stones out of the way so that businesses and citizens can enjoy the benefits of a true Data Union.
In short, it is a question of moving from an economy of isolated silos to one of connected networks. Because, in the world of data, sharing is not losing control, it is gaining scale.
Content created by Dr. Fernando Gualo, Professor at UCLM and Government and Data Quality Consultant. The content and views expressed in this publication are the sole responsibility of the author.
