International Data Spaces

Data Sovereignty

Industrial Data Space

Digitization is both driver and enabler of innovative business models. Key resource for enterprises to succeed in this endeavor is data. A prerequisite for smart services, innovative value propositions and automated business processes is the secure exchange and the easy combination of data within value networks.

In this context, the International Data Spaces initiative (former Industrial Data Space) aims at creating a secure data space that supports enterprises of different industries and different sizes in the autonomous management of data.

The International Data Spaces initiative is not limited by any geographic boundary, but clearly has a European and international ambition.



Reference Architecture Model for the International Data Spaces

Data sovereignty is a central aspect of the International Data Spaces. It can be defined as a natural person’s or corporate entity’s capability of being entirely self-determined with regard to its data. The International Data Spaces initiative proposes a Reference Architecture Model for this particular capability and related aspects, including requirements for secure and trusted data exchange in business ecosystems.

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The intelligent data infrastructure for business

The world has never been as networked as it is today. But how are we to shape the digital future, a future in which data are the lifeblood of every company? The Industrial Data Space is the foundation for more secure and self-determined exchange of data. See why the Industrial Data Space is a decisive factor in the business models of the fourth Industrial Revolution. 

Data as the Key for Business Innovation

Digitization blurs the boundaries of traditional industries and changes the logic of today’s business models. In their endeavor to sustain their competitive position, enterprises leverage the innovation potential that comes with digitization. Data-driven innovation materializes in four aspects:

  • Product innovation: In the pharmaceutical industry, the use of health data facilitates more effective and more individualized drugs and treatment concepts. Making this work requires the cooperation of various actors in the medical ecosystem, namely the providers of pharmaceutical products, health insurers, health service providers, and patients. At the same time, patients must retain sovereignty over their own data at all times. The patient alone decides what happens with their data.

  • Service innovation: For vehicle route navigation, not only does modern traffic management use traditional information such as map resources and traffic reports, but routes are calculated dynamically from various data sources such as traffic management centers.

  • Process innovation: The retail sector prevents out-of-stock situations on supermarket shelves by coupling flows of goods to information flows at all times. Data about goods being shipped (location, condition, etc.) are constantly available to all partners in the value creation network, so that retailers, suppliers and logistics service providers can jointly control and monitor their supply chains. Data becomes a shared resource from which all value creation partners benefit.

  • Organizational innovation: Small-batch manufacturing in the automotive industry – as used e.g. for electric vehicles – is based on the autonomous control of vehicles and components. To this end, the master data relating to products, orders, transport details, etc. must be managed jointly and securely in an ecosystem composed of manufacturers, suppliers, and logistics service providers.

The Industrial Data Space helps enterprises in realizing these innovative potentials by providing basic data services such as anonymization of data, integration services and management of “expiration dates” for data.

In doing so, the Industrial Data Space makes a significant contribution to the digital transformation in enterprises.

Industrial Data Space Research Project

The research project pursues two goals:

  • Design of a reference architecture model for the Industrial Data Space
  • Piloting the reference architecture model in selected use cases

The research project started on October 1, 2015, and runs over a period of three years. The project consortium consists of twelve Fraunhofer institutes, thus leverages the expertise of large parts of Fraunhofer. 


The activities are organized in 9 work packages:

  • Reference architecture model
  • Software implementation
  • Use cases
  • Standardization
  • Certification
  • Digital Business Engineering methodology
  • Recommendations for action
  • Institutionalization
  • Project management

The work is closely aligned with the Plattform Industrie 4.0. Research project staff engages in the working groups of the platform. The research project activities are led by Prof. Dr. Boris Otto.

International Data Spaces Association

The Industrial Data Space initiative institutionalizes as research project on the one hand side and as a chartered association on the other hand. The chartered association was founded in 2016.

The chartered association aims at strengthening the user interests, in particular:

  • Pooling user requirements
  • Providing a forum where association members can exchange experiences
  • Setting up expert committees, task forces, and initiatives, particularly for scientific-technical standardization issues and certification processes
  • Information and further training measures
  • PR and communication
  • Helping to draw up and frame guidelines and regulatory processes  

The following organizations have already agreed on the foundation of the chartered association by signing a Memorandum of Understanding (MoU):       

  • Atos IT Solutions and Services GmbH
  • Bayer HealthCare AG
  • Boehringer Ingelheim Pharma GmbH & Co.KG
  • Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
  • KOMSA Kommunikation Sachsen AG
  • PricewaterhouseCoopers AG
  • REWE Systems GmbH
  • Robert Bosch GmbH
  • Salzgitter AG
  • TyssenKrupp AG
  • TÜV Nord AG
  • Volkswagen AG
  • ZVEI - Zentralverband Elektrotechnik- und Elektronikindustrie e.V.

The chartered association will be open for all enterprises, industry associations and non-for-profit organizations.

Reference Architecture Model

The Industrial Data Space architecture comprises all components necessary for the secure exchange and easy combination of data within business ecosystems. The overall architecture can be divided into four sub-architectures:   

  • Governance architecture lays down the "rules of the game" and addresses issues around the visibility of data sources, data quality, and the consideration of data in terms of value.
  • Security architecture addresses issues around the secure exchange of data, the recognition of anomalies, and data protection.
  • Functional software architecture identifies and describes the Industrial Data Space software components, which include the Industrial Data Space Connector, an app store for Industrial Data Space data services, and components for the registration and certification of data services and data sources.
  • Technology architecture comprises the technologies required to pilot the other three sub-architectures in the use cases.

All sub-architectures are described in the reference architecture model which is open and designed to be implemented also by third party technology providers.

Industrial Data Space Key Features

The Industrial Data Space materializes as the entirety of all endpoints, i.e. all Industrial Data Space Connectors. Thus, the Industrial Data Space is no central data lake, but rather follows a federated architectural approach.

Key features are:

  • Secure data supply chain from data capture to data use in smart services.
  • Flexible endpoint scenarios: in other words, the Industrial Data Space Connector can be implemented in traditional company IT environments, but also in cloud environments and in production and logistics devices and vehicles .
  • Lightweight semantics.
  • Easy combination of different data goods.
  • Configurable reference architecture model.
  • Support of domain-specific governance models.
  • Standardized collaborative data management processes.
  • Open development process . 

The Industrial Data Space pursues an ambitious goal. Thus, the following aspects are out of scope:

  • The Industrial Data Space does not aim at a central data storage instance.
  • The research project does not result in a market-ready product, but in a reference architecture model and its use case pilots. The latter, of course, form the foundation for marketable products.
  • The Industrial Data Space itself will not deliver functional smart services (such as freight exchanges etc.). It rather offers data services that form the infrastructural foundation for the easy and efficient development of the former.
  • The Industrial Data Space does not contribute to data transmission networks or to real-time applications on shop floor level. Instead, it uses existing approaches. The Industrial Data Space focuses on data and data services.
  • The Industrial Data Space itself does not drive domain-specific standardization (e.g. related to vocabularies or semantic standards). It rather makes use of existing standards and implements those in pilot applications.