Realigning Europe’s data management – it must be federal and sustainable

Prof. Martin, what exactly is meant by federal structures in data management? 

Professor Alexander Martin: A federal approach to data management keeps data physically within a given region while allowing collaboration across different federal structures such as cities, states, countries or the European Union. In its smallest form, data federalism can be reproduced in many areas of application, including for a city or municipality, a company or corporate location, or critical infrastructure such as an airport or railway station. This decentralization creates a high degree of data sovereignty.

You’ve talked about “regional data works” in this context. What does this term entail? 

Martin: The idea is to establish “utility companies” for data: many municipal utility companies don’t generate electricity themselves, but they do manage a region’s power supply. We want to transfer this principle to data. The decisive advantage of a regional data works is that it establishes a connection between the abstract, virtual elements of data infrastructure and the specific, physical ones. It also translates the approach into specific use cases from day one. Take a city, for example: data is collected from sensors within the municipality, stored on local servers and also processed there. In this way, the virtual data remains within the specific geographical, physical area of the municipality. This allows us to achieve regional sovereignty over the data, establish trust and also increase acceptance for solutions.

What prompted you to take such an active interest in the concept of federal data management?

In my opinion, what we in Europe need is innovative, responsible data management that provides a counterpoint to market-driven concepts and national-level initiatives. Data is the foundation of our digital society and has the potential to enhance numerous areas of life, the economy and public administration. At the same time, data is increasingly tying up resources and is often not used in a targeted way. What’s more, a large proportion of data traffic takes place outside Europe. I want to initiate solutions to these problems. 

What are the advantages of these federal structures and why are you convinced that Europe needs this specific form of data management?

The principle of federalism is firmly established in Europe and works well. Given this proven track record, the question becomes why it shouldn’t also be applied to data. There are several advantages, starting with regional sovereignty over the entire data life cycle. From sensors for data acquisition to data communication and storage to value creation through data analysis, sovereignty over that data remains in the regions. And as I mentioned earlier, this approach increases acceptance and trust by virtue of the fact that it connects the abstract, virtual elements of data infrastructure and the specific, physical ones. Then there’s the flexibility in responding to changes in technology, for instance caused by new digital solutions and possibilities. And finally, greater sustainability through shorter transmission routes.

How does this tie in with existing strategies and concepts for dealing with data in Germany and Europe?

Our recommendations take many existing concepts a step further. For instance, Germany’s 2021 data strategy also aims to establish innovative and responsible ways of using data for the greater good. And the European data strategy pursues similar goals. There are also special links to the Gaia-X project, which aims to pave the way for open, connected infrastructure in Europe based on European values such as data protection, transparency and openness.

Let’s get down to brass tacks. How do you propose establishing this kind of data management? What’s it going to take?

In terms of research, the first challenge of regional and federal data management lies in establishing the physical infrastructure and the corresponding virtual infrastructure. This would allow relevant data to be gathered from all kinds of sources, transmitted, stored, processed and used to derive recommendations. Particular attention should be paid here to interoperability with existing infrastructure, such as regional, one-off solutions or Gaia-X.

The second challenge is that new techniques and methods of data processing are required to deal with the ever increasing volumes of data. Applying the latest research findings in the field of distributed architecture and decentralized processing – the key concept here is edge computing – as well as artificial intelligence can offer a decisive competitive advantage here. A structure such as Fraunhofer CCIT, in which more than 20 Fraunhofer Institutes from different research areas work together on cognitive Internet technologies, can provide valuable ideas for implementation.

Position paper "Federal Structures in Data Management" for download

Which topic areas and which sections of society will benefit most from such regional and federal structures? Who needs them most?

Federal data management could benefit both the economy and society. The top priority is always to translate this into specific applications. For instance, in two different topic groups, we’ve already identified a great many applications each of which, in its own way, stands to benefit from regional and federal structures.

When it comes to the smart city, these are traffic and transportation management, infrastructure management, environmental management and regional supply processes. At the end of the day, our concept would give regions more data sovereignty while increasing transparency and acceptance. It would also generate more value for the general public.

In terms of Industry 4.0, federal data management could help with predictive and safety-critical monitoring of infrastructure and production, or help optimize building management. In addition to enhancing data sovereignty at companies, it also improves responsiveness and resilience across the board.

Interview by Lucas Westermann

Infrastructure:

• Establish physical and virtual data infrastructure
• Ensure interoperability with existing solutions
• Introduce consistent systems

Increasing data use:

• Incorporate abstract data infrastructure into specific applications
• Provide users with access to data
• Make the benefits and potential of available data tangible
• Establish networks and facilities to test these solutions

Data competence:

• Foster the development of operator and business models
• Promote municipal and regional collaboration for data exchange