Designing Data Spaces

The role data plays in enterprises is changing as the digital transformation in many sectors gains speed. New business opportunities through data-driven innovation emerge from data sharing in ecosystems. In ecosystems, the interest of the individual must be brought into alignment with the interest of the ecosystem. Trust between participants, data interoperability, and data sovereignty are key requirements which can be met by data spaces. Data spaces are a distributed data integration concept which is taken up by consortia aiming at supporting ecosystem. GAIA-X and IDS specify reference architectures for distributed data infrastructures and data spaces, respectively. While the benefits of data spaces for a fair data economy are recognized by business and policy makers, a deeper understanding is required about the design and evolution of data spaces. This chapter introduces fundamental concepts, identifies design tasks and options, and, thus, provides guidance for the establishment of data spaces.

This chapter is a result of a collaborative effort between data space and industrial domain experts to define cross-sectoral and across initiatives fundamental design principles to build data spaces. A joint paper of this dimension is unique and a great step with regard to the convergence of the large initiatives on data sharing in Europe. The starting point is the implementation in five years of the vision defined by EU Strategy for Data. In that world multiple data spaces will have been widely adopted across Europe, organizations and individuals will have control over their data and a digital world with less dominance of, and dependency on, large, quasi-monopolistic players has been formed. In the following, the way forward is elaborated with fundamentals of data spaces as well as common building blocks describing how the design principles for all sectors are applied to reveal sector-specific benefit. Furthermore, a proposal for governance and business models for data spaces on a collaborative and individual level is presented. Finally, the roadmap for co-creating the soft infrastructure underlying European data spaces is drawn.

International Data Spaces (IDS) enable the sovereign and self-determined exchange of data via a standardized connection across company boundaries. They address the many challenges in the overarching use of data in terms of interoperability, transparency, trust, security, and adaptation by a critical mass. For this purpose, the IDS develops a standardized architecture, which is described and continuously updated in the IDSA Reference Architecture Model (RAM) document. The core of the underlying concept is the linkage of data and usage conditions and their organizational and technical processing and enforcement. Organizational roles and responsibilities are considered on the one hand, and various technical components are defined on the other. The core of the architecture thereby, in contrast to existing solutions and approaches of completely centralized or decentralized architectures, is the paradigm of a federated structure. This enables the actual (raw) data to be exchanged exclusively between the participants without the need for a third party or a central data store. The chapter gives an overview of this approach and provides insights in terms of objectives, roles, and components that are used to enable sovereign data exchange. It enables a basic understanding of the core concept and assessing the impact of such an architecture approach.

The Gaia-X project was initiated in 2019 by the German and French Ministers of Economy to ensure that companies would not lose control of their industrial data when it is hosted by non-EU cloud service providers.Since then, Gaia-X holds an international association presence in Belgium with more than 334 members, representing both users and providers across 20 countries and 16 national hubs and 5 candidate countries.The Association aims to increase the adoption of cloud services and accelerate data exchanges by European businesses through the facilitation of business data sovereignty with jointly approved (user and provider) policy rules on data portability and interoperability.Although for many enterprises, data sovereignty is seen as a prerequisite for using the cloud, a significant driver to boost the digital economy in business is incentivizing business data sharing. Two decades of cost optimization have constrained business value creation, driving many companies to neglect the opportunity to create shared value within a wider industry ecosystem.Now, thanks to the participation of large numbers of cloud users in the domains of Finance, Health, Energy, Automotive, Travel Aeronautics, Manufacturing, Agriculture, and Mobility, among others, Gaia-X is ideally positioned to help industries define appropriate data spaces and identify/develop compelling use cases, which can then be jointly deployed to a compliant-by-design platform architecture under the Gaia-X specifications, trust, and labeling frameworks.The creation of national Gaia-X hubs that act as independent think tanks, ambassadors, or influencers of the Association further facilitates the emergence of new data spaces and use/enabler cases at a country level, before these are subsequently extended to a European scope and beyond. Gaia-X partners share the view that data spaces will play a similar role in digital business as the web played 40 years ago to help the Internet take off.The Gaia-X Working Groups are at the core of the Gaia-X discussions and deliverables. There are three committees : the Technical, the Policies and Rules, and the Data Spaces and Business.The Technical Committee focus on key architectural elements and their evolution, such as and not limited to: Identity and Access Management: bridge the traditional X509 realm and new SSI realm, creating a decentralized network of identity federations Service Composition: how to assemble services in order to create new services with higher added value Self-Description: how to build digital trust at scale with measurable and comparable criteria The Policy and Rules Committee creates the deliverables required to develop the Gaia-X framework (compliance requirements, labels and qualification processes, credentials matrix, contractual agreements, etc.): The Labels and Qualification working group defines the E2E process for labels and qualification, from defining and evolving the levels of label, the process for defining new labels, and identifying and certifying existing CABS. The Credentials and Trust Anchors working group will develop and maintain a matrix of credentials and their verification methods to enable the implementation of compliance through automation, contractual clauses, certifications, or other methods. The Compliance working group collects compliance requirements from all sources to build a unique compliance requirements pool. The Data Spaces Business Committee helps the Association expanding and accelerating the creation of new Gaia-X service in the market: The Finance working group focuses on business modeling and supports the project office of the Association. The Technical working group analyzes the technical requirements from a business perspective. The Operational Requirements working group is the business requirements unit. The Hub working groups hold close contact with all Gaia-X Hubs and support the collection and creation of the Gaia-X use and business cases. These working groups maintain the international list of all use cases and data spaces and coordinate the Hubs.

The claim of data sovereignty is inherently linked to putting the legal instruments and tools in the hands of each participant in the ecosystem, allowing freedom of contract as well as ensuring that exercising data exchange and consorted data usage in the data economy is in compliance with general and specific regulations, ranging from anti-trust to GDPR and cyber-security regulations as well as sector specific regulations. The IDS provides a framework and a technology to allow the parties to limit their transaction costs and to ensure effective enforcement through the concept of usage control. In a future world, this will include increased automation of contract execution (conclusion, performance, and enforcement), whereas the steps to reach that goal are plentiful and, as of now, still require to “set the scene” with the means of the traditional contractual agreements. This article provides an overview and orientation on the key legal areas and aspects to consider for stakeholders, participants, and the business more generally and in the application of the IDS architecture.

A significant challenge in bootstrapping a jointly used infrastructure such as Data Spaces is to incentivize the participants to invest in setting up the infrastructure. In this chapter, we investigate this challenge and possible solutions, focusing on an approach called “Tokenomics.”The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space.To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem.

The Information Model of the International Data Spaces (IDS-IM) is the central integration enabler for the semantic interoperability in any IDS ecosystem. It contains the terms and relationships to describe the IDS components, their interactions, and conditions under which data exchange and usage is possible. It thus presents the common denominator for the IDS and the foundation for any IDS communication. As such, its evolution cycles are deeply related with the maturity process of the IDS itself. This chapter makes the following contributions related to the IDS Information Model: a brief overview of the vocabulary, its guiding principles, and general features is supplied. Based on these explanations, several upcoming aspects are discussed that reflect the latest state of discussions about the declaration and cryptographic assurance of identities and decentralized identifiers, and how these need to be treated to ensure compliance with the IDS principles.In addition, we explain the latest developments around the IDS Usage Contract Language, the module of the IDS-IM that expresses Usage Contracts, and data restrictions. These definitions are further implemented with infrastructure components, in particular the presented, newly specified Policy Information Point and the Participant Information Service of the IDS.

Data-driven business models are based on sharing and exchanging data. However, to establish a trustworthy and secure data exchange between different organizations, we have to tackle several challenges. Data sovereignty, for instance, is an essential prerequisite to empower data-driven business models across different organizations. The International Data Spaces provide solutions for data sovereignty to implement a secure and trustworthy data economy.In this chapter, we focus on data usage control and data provenance as building blocks to solve data sovereignty challenges. We introduce concepts and technology for realizing usage control and describe the differences between usage control and access control as well as other related concepts such as digital rights management or user managed access. We present the implementation of data sovereignty in the International Data Spaces starting from the formalization of data usage restrictions as policies (i.e., the policy specification) to the technical compliance and adherence of the data usage restrictions (i.e., the policy enforcement). In doing so, we present the transformation of data usage restrictions to machine-readable policies that can be enforced by the systems. Different technologies, such as the MY DATA Control Technologies can be used to implement the enforcement of data sovereignty in a technical manner and discuss future expansion stages of implementing data sovereignty.

Data is becoming increasingly valuable and must be protected. At the same time, data becomes an economic asset and companies can benefit from exchanging data with each other. The International Data Spaces enable companies to share data while ensuring data sovereignty and security.Data providers can keep control over the processing of their data by utilizing usage control policies, including the verification that these usage control policies are enforced by the data consumer. For this, data processing devices, called connectors, must prove their identity and the integrity of their software stack and state.In this chapter, we present the overall security concept for building trust in data spaces enabling data sovereignty and usage control enforcement. The concept builds on a certification process for components and operational environments utilizing the multiple eye principle. This process is technically mapped to a public key infrastructure providing digital certificates for connector identities and software signing. Finally, the third building block is the architecture and system security of the connectors where usage control must be enforced, the identity and integrity of other connectors and their software stack and state must be verified, and the actual data processing happens.

The core objective of the concept of International Data Spaces (IDS) is to enable controlled exchange and sharing of data between organizations, regardless of the type of data. Sharing of data will generate services that become an asset while data providers maintain their sovereignty. IDS furnish a technology enabler for implementing data economies to exchange data and knowledge, which are according to usage policies. Thus, data turns into an economic asset. However, once data have been provided toward IDS, sovereignty of data owners is of pivotal importance, as well as the question of its use and the transfer of incentives to providers. At this point, blockchain technology enters the ballpark. It is instrumental for the implementation and operation of clearing houses as trading platform for data provision and knowledge utilization. The aim of this chapter is to examine and discuss the role of blockchain for IDS. Next to general blockchain foundations and potentials, blockchain’s specific potential for IDS is discussed and its application is demonstrated by four compelling use cases.

Data Spaces form a network for sovereign data sharing. In this chapter, we explore the implications that the IDS reference architecture will have on typical scenarios of federated data integration and question answering processes. After a classification of data integration scenarios and their special requirements, we first present a workflow-based solution for integrated data materialization that has been used in several IDS use cases. We then discuss some limitations of such approaches and propose an additional approach based on logic formalisms and machine learning methods that promise to reduce data traffic, security, and privacy risks while helping users to select more meaningful data sources.

A key aspect of establishing data spaces is to develop a common understanding of the data to be shared in the data space. Semantic standards and technologies were developed for this purpose since over two decades. In this article, we will discuss the history and importance of semantic integration for data spaces. We will introduce the base concepts of semantic integration—including the global identifiers for data and the W3C standards RDF, RDF-Schema, and OWL. As a result these standards and technologies can be used to devise versatile Knowledge Graphs capturing domain conceptualizations and concrete data representations. We explain how data interoperability can be achieved by linking and mapping between different data and knowledge representations. Finally we will showcase their use with an example for data integration in supply chains with the ScorVoc vocabulary.

Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.

Open data is a popular and flourishing concept. The availability of open and structured data is the foundation of new business models, citizen engagement, and scientific research. However, open data still faces many issues to unfold its full potential, including usability, quality, legal, privacy, strategic, and technical barriers. In addition, the public sector remains its main provider, while industry stakeholders are still reluctant to participate in open data ecosystems. In this article, we present an architecture to overcome these drawbacks by utilizing the concepts, specifications, and technologies provided by International Data Spaces. We developed a prototype to demonstrate and evaluate the practical adoption of our architecture. Our work shows that IDS can act a vital foundation for open data ecosystems. The presented solution is available as open source software.

Modern science increasingly works with large amount of data, which are heterogeneous, are distributed, and require special infrastructure for data collection, storage, processing, and visualization. Science digitalization, likewise industry digitalization, is facilitated by the explosive development of digital technologies and cloud-based infrastructure technologies and services. This paper attempts to understand impact and new requirements to the future Scientific Data Infrastructure imposed by growing science digitalization. The paper presents two lines of analysis: one is a retrospective analysis related to the European Research Infrastructure (RI) development stages and timeline from centralized to distributed and current Federated Interoperable; another line provided analysis of digital technology trends and identified what technologies will impact the future Scientific Data Infrastructure (SDI). Based on this analysis, the paper proposes a vision for the future RI Platform as a Service (PRIaaS) that incorporates recent digital technologies and enables platform and ecosystem model for future science. Notably the proposed PRIaaS adopts TMForum Digital Platform Reference Architecture (DPRA) that will simplify building and federating domain-specific RIs while focusing on the domain-specific data value chain with data protection and policy-based management by design.

The “Silicon Economy” is synonymous with a coming digital infrastructure (digital ecosystem) based on the automated negotiation, disposition, and control of flows of goods, enabling new, digital business models (not only) for logistics. This infrastructure requires and enables the trading of data without losing sovereignty over the data. It is the digital infrastructure and environment for the highly distributed AI algorithms along value networks. In contrast to oligopolistic developments in the B2C sector ( amazon.com , AirBnB, Alibaba, Uber, etc.), the Silicon Economy is a federated and decentralized platform ecosystem, the basic components of which are made available to the general public as open source for free use.The Silicon Economy ecosystem is becoming an enabler of supply chain ecosystems in which goods, autonomously controlled by Artificial Intelligence (AI), undergo orchestrated processes according to the situation.This article focuses on the origins and potentials but also on the technological foundations and challenges of the transformation toward a Silicon Economy.

The digital transformation strongly affects the agricultural domain. Still, there is a lot of potential for optimization in many work and business processes. In the current agricultural digital ecosystem, numerous isolated, often non-interoperable solutions exist. In this chapter, we motivate the need and added value of an “Agricultural Data Space” (ADS for short). We outline an ADS concept, which resulted mainly from the Fraunhofer lighthouse project “Cognitive Agriculture” (COGNAC) and describe the necessary prerequisites and technical solution approaches. Complemented by the possibilities of a transparent and open marketplace for data, digital products, and software services, such a data space would address many of the existing obstacles to widespread acceptance and take-up of digital technologies. Overall, an ADS as part of an extended digital ecosystem will significantly advance digitalization in agriculture. In the end, we provide application scenarios for which an agricultural data space can add value.

Exchange of sensitive medical data between healthcare providers and researchers requires a particularly high level of trust and security. Involving patients and citizen into this process increases transparency and may improve the outcome of preventive, diagnostic, and therapeutic measures. We propose to build the structure of a healthcare ecosystem on the basis of “apps” that not only hold (and exchange) health data but support user interaction and healthcare process management. Emphasis on process support may also be used to improve data quality, which is an important prerequisite for evidence-based medicine and the training and usage of future AI tools.

This chapter describes the application of the IDS principles, architectural artifacts, and technologies to the application domain of industrial production and smart manufacturing, in particular as drafted by the Platform Industrie 4.0 in their Reference Architecture Model Industrie 4.0 (RAMI4.0) and follow-on specifications about the Asset Administration Shell (AAS). It elaborates on the working approach of the IDS-Industrial Community (IDS-I) for the analysis of requirements on data sovereignty. This activity is motivated by the vision 2030 of the Platform Industrie 4.0 that states autonomy, including data sovereignty, as one strategic field of action.The chapter presents how IDS-I aims at systematically deriving and analyzing data sovereignty aspects from the two reference use cases, Collaborative Condition Monitoring (CCM) and Smart Factory Web (SFW), in order to identify architectural and technological synergies and gaps between the International Data Spaces (IDS) and the specifications of the Platform Industrie 4.0.

The energy sector is in a dynamic transition from centralized systems with large fossil power plants to a decentralized system with a high number of renewable energy assets and a rapidly increasing number of additional flexible loads from storage solutions, e-mobility, or power-to-heat applications.To operate the system reliably, demand and supply have to be matched at all times very closely. Thus, the sector is very data and communication intensive and requires advanced ICT solutions to automate processes and deal with the enormous complexity.The Energy Data Space can enable the digitalization of the energy transition by providing an architecture to make data available in order to increase the efficiency in asset and system operation.Data provision and market communication within the system operations of electricity grids is a key use case due to its central role in the sector. Next, the integration of data from the smart meter rollout could as well be built on Data Space technology. Further use cases include predictive maintenance and the energy supply of buildings.Initial research projects have demonstrated the feasibility of basic use cases. On the European level, the Platoon project will provide seven pilot applications by 2024.

To successfully support decision-making or even automatically make decisions of their own, intelligent transport and mobility systems require large amounts of data. Although multitudes of mobility data are already being collected today, the comprehensive processing and exploitation of this data have often been impossible due to technical, legal, or economic reasons. With Mobility Data Space, an open data space is now being created which offers access to real-time traffic data and sensitive mobility data beyond their secure exchange and which links existing data platforms to each other. In the future, it will thus be possible to provide comprehensive mobility data on a national level.Based on a decentralized system architecture developed by the International Data Spaces Association e. V., the Mobility Data Space offers an ecosystem in which data providers can specify and control the conditions under which their data can be used by third parties. This approach creates data sovereignty as well as trust, and data users can be sure about data origin and quality. By integrating data from the public and private sector via regional and national platforms, the Mobility Data Space will become a digital distribution channel for data-driven business models, providing entirely new options of data acquisition, linking, and exploitation.Whether data provider, user, developer, or end user—the Mobility Data Space takes all acting parties into consideration and offers: Data sovereignty and security along the value chain Standardized access to data from both public and private sources Space for the emergence of new business models, distribution channels and services, as well as a larger selection of innovative mobility services and applications

Using data and Artificial Intelligence, it is possible to answer the big questions, how sustainable the planet is or what impact industry has on climate. The Big Data Value Association (BDVA) believes that Data Sharing Spaces will be a key enabler to this vision. The BDVA community has created a unified perspective on the value of data sharing spaces across the pillars of data, governance, people, organization, and technology, with trust as a central foundation. This chapter details this BDVA perspective, explaining the five pillars needed to create value in data with trust as a central concept, together with the tools and mechanisms for strategic stakeholders to create data sharing spaces jointly. It elaborates the strategic challenges which need to be overcome and sets out our call to action for the community to make this a reality. The chapter also summarizes the initial progress on data platform development, data governance, and Trustworthy AI to make data sharing spaces a reality. Finally, it details an example of a data space in smart manufacturing.

Private and public organizations hoard troves of data yet remain unable to unlock its full business potential. Data exchange platforms powered by adapted technology and driven by data exchange strategies act as catalysts to develop data ecosystems and data spaces, accelerate data circulation, and liberate its value.Data spaces are powerful business, innovation, and societal enablers, whose growth and success rely on their ability to foster and develop trust.Data exchange platforms contribute a lot to building trust as they provide the required tools and automation to data acquirers, data providers, and data exchange services providers to operate at scale within secure and compliant environments.New and upcoming European regulatory frameworks also contribute to raising trust as they foster a harmonized data ecosystem across member states and define the rules of engagement between businesses, governments, and individuals engaged in data sharing and exchange.Additionally, data exchange environments must provide traceability at all levels of the data transactions, which is particularly needed in increasingly complex data ecosystems.Finally, in order to provide the flexibility required to answer the needs of complex, distributed, and heterogeneous environments, different models of data exchange governance are necessary.

This chapter describes how smart applications from multiple domains can participate in the creation of data spaces based on FIWARE software building blocks. Smart applications participating in such data spaces share digital twin data in real time using a common standard API like NGSI-LD and relying on standard data models. Each smart solution contributes to build a complete digital twin data representation of the real world sharing their data. At the same time, they can exploit data shared by other applications. Relying on FIWARE Data Marketplace components, smart applications can publish data under concrete terms and conditions which include pricing or data usage/access policies.A federated cloud infrastructure and mechanisms supporting data sovereignty are necessary to create data spaces. However, additional elements have to be added to ease the creation of data value chains and the materialization of a data economy. Standard APIs, combined with standard data models, are crucial to support effective data exchange enabling loose coupling between parties as well as reusability and replaceability of data resources and applications. Similarly, data spaces need to incorporate mechanisms for publication, discovery, and trading of data resources. These are elements that FIWARE implements, and they can be combined with IDSA architecture elements like the IDS Connector to create data spaces supporting trusted and effective data sharing.The GAIA-X project, started in 2020, is aimed at creating a federated form of data infrastructure in Europe which strengthens the ability to both access and share data securely and confidently. FIWARE is bringing mature technologies, compatible with IDS and CEF Building Blocks, which will accelerate the delivery of GAIA-X to the market.

The cloud has changed the way we consume technology either as individual users or in a business context. However, cloud computing can only transform organizations, create innovation, or provide the ability to scale digital business models if there is trust in the cloud and if the data that is being generated, processed, exchanged, and stored in the cloud has the appropriate safeguards. Therefore, sovereignty and control over data and its protection are paramount. Data spaces provide organizations with additional capabilities to govern strict data usage rules over the whole life cycle of information sharing with others and enable new use cases and new business models where data can be securely shared among a defined set of collaborators and with clear and enforceable usage rights attached to create new value. Open and sovereign cloud technologies will provide the necessary transparency, control, and the highest levels of privacy and security that are required to fully leverage the potential of such data spaces. Digital sovereignty, however, still means many things to many people. So to make it more concrete, in this article, we will look at digital sovereignty across three layers: data sovereignty, operational sovereignty, and software sovereignty. With these layers, we will create a spectrum of solutions that enable scalable data spaces that will be critical for the digital transformation of the European economy.

The industrial Internet has brought the biggest economic opportunity since the mobile Internet. Haier started the exploration of intelligentization, networking, and informationalization as early as 2005 and gradually launched an industrial Internet platform COSMOPlat with independent intellectual property rights. In COSMOPlat, traditional mass manufacturing model is replaced by mass customization model (MCM), in which production is driven by users’ order rather than inventory. What the model achieved is not simply automation but also real intelligent manufacturing with high efficiency driven by high precision. With users’ participation in the whole process, manufacturing is precisely made according to the users’ dynamic needs, which will better meet actual requirements.The article provides an overview of the mass customization model of COSMOPlat and how it facilitates enterprises for digital transformation and upgrade. Also indicated are the successful use cases in application.

In a digitalized and deeply interconnected industrial ecosystem, it is of paramount importance to create mechanisms that seamlessly guarantee standardization and verifiability, interoperability (including regional legislation), transparency, and trustworthiness, in particular in the intermediation of all businesses and stakeholders, including SMEs. In this respect, the International Data Spaces and GAIA-X initiatives pave the way to a framework for collaboration that use secure and trusted data services to safeguard digital sovereignty.Huawei is a leading ICT provider, operating in more than 170 countries, and an active member of more than 600 standardization bodies and industry associations, among them IDS and GAIA-X. With their international footprint, IDS and GAIA-X are of the utmost importance for Huawei in Germany, Europe, and globally. Here, we provide a brief overview of our understanding of the data ecosystem’s inherent issues, and how regulations address them, followed by more specific examples of how IDS and GAIA-X objectives can be supported by Huawei, with emphasis on validating concepts in the manufacturing domain as a prominent reference example for an important European market vertical. We illustrate the use case of an evolved implementation of Industry 4.0 that integrates machines and cloud services. 5G connectivity for trusted networked production is added to the example. We finally highlight the use of GAIA-X compliant federated AI for control, maintenance, and match-making of demand and supply actors.

NTT, headquartered in Japan, is a global ICT company with data centers, network facilities, R&D centers, and business locations all over the world. It is developing new data infrastructures that utilize next-generation information communications technology and digital twin computing. This section introduces NTT’s R&D activities, services and solutions, and future initiatives to ensure security, compliance, fairness, transparency, and interoperability in the global data space. The details are as follows: 1. Concerns and issues regarding the cross-border sharing and use of industrial IoT data between multiple companies 2. Use cases considered by RRI (Robot Revolution and Industrial IoT Initiative) and requirements for global data spaces 3. Next-generation optical and wireless network “IOWN” and highly reliable data infrastructure “SDPF with Trust” proposed by NTT to realize data spaces that meet requirements for global data spaces 4. Demonstration system and international interconnection experiments using IDS Connectors to connect data spaces between Japan and Europe 5. Ideal international data platform architecture that connects GAIA-X compliant data spaces to networks of telecommunications carriers in various countries 6. Concept of a digital immune system “Global autonomic nerve” that combines data spaces with IoT and AI to achieve sustainable governance of economic activities and the global ecosystem

The 2020s will be the beginning of an “Age of Data.” Data will help us unleash huge potentials in private but particularly also in the industry. At the same time, partly driven by the availability of this real-time information, the customer-supplier relationships are transforming from linear supply chains into networks and data-driven ecosystems. Efficient standards and technologies are key to this transformation.However, the biggest hurdle to sharing data continues to be the lack of trust in secure, transparent, and trustworthy information exchange. Concepts and instruments like the International Data Spaces are essential to guarantee data sovereignty.The SAP Asset Intelligence Network is therefore complemented by the core elements of IDS to maintain trust and transparency throughout the entire industry value network.

Traditional approaches to handling of data are becoming outdated. On the one hand, data storage is already costly today, and tomorrow’s growth will create a serious cost problem. On the other hand, certain advanced analytics applications require more data than many companies can hope to collet themselves. Therefore, a more federated system with just-in-time data and data sharing seems rational. Unfortunately, many companies worry about data sharing. They are concerned about a loss of data sovereignty, the right to have control over one’s data. Once a file is sent, anything could happen to it. This is where the International Data Spaces (IDS) standard comes in to enable a setting that can ensure data sovereignty in theory. To find out in practice, Deutsche Telekom has incorporated IDS technology into solutions and applied them in the automotive business and in industry. We report about first findings from real-world applications.

State-of-the-art cars are increasingly becoming computers on wheels, constantly collecting, storing, and transmitting data. This goes hand in hand with better market opportunities for certain service providers with access to the data.There is still no clear legal regulation on who the customer can share their data with—and in what way—nor how transparency and security can be guaranteed for such data in the process. Without legal regulation of data access, there will be no way to ensure a level playing field among providers and freedom of choice for consumers in the future.Three basic principles apply to the access to vehicle data: Third-party service providers should be able to develop new services without depending on car manufacturers. Independent service providers, such as independent workshops, insurers, and automobile clubs, should be able to reach customers through the same channels as the vehicle manufacturers. Vehicle manufacturers should not be allowed to monitor vehicle users or the service providers selected by vehicle owners.

Data spaces, simply put, are a functionality for users. They allow users to control their data while sharing it with others. This functionality is what makes data spaces so powerful. They allow data to be shared multilaterally. Those who join a data space can reach many others who have also signed up. At the same time, however, data spaces participants must also consider some aspects. On the one hand, companies have to make business considerations, as participation should, for example, lead to higher revenues, lower costs or better services. On the other hand, decisions must be made regarding measures and technologies for how data sovereignty is implemented in the data space, as this is a central functionality for data sharing. This article provides an overview of possible ways to implement data spaces, the business considerations and a practical approach using the iSHARE trust system.

Trusted data spaces supporting energy services and fostering collaboration between all stakeholders are a cornerstone of the decarbonization of the sector. Today, a broad representation of European energy companies and academic and technological partners has joined GAIA-X to build the European energy data space. The group represents all segments of the energy value chain and is from all around Europe.Through this data space, we aim to address the following challenges: accelerate the deployment of low carbon energy solutions, foster energy efficiency and sector coupling (power, gas, and heating, integration of mobility and building/heating systems, etc.), enable more flexibility and renewable energy integration to the European electric system, accelerate the sector digitalization, and ultimately support Europe competitiveness, thanks to low energy costs.To achieve these goals, strong collaboration between the actors is needed to identify and launch valuable use cases on key topics: renewables, hydrogen, nuclear, energy efficiency, electric vehicles, local energy communities, networks, or compliance and traceability. The article is a collaborative effort, initiated in February 2021, from the French, German, and Belgium energy communities within GAIA-X national hubs. The intention is to provide insight on the work of the GAIA-X energy Domain, to share widely our ecosystem’s expectations, and to provide an overview of use cases identified.