Demystifying data spaces: The future of data sharing

Data spaces are gaining a lot of traction. As a decentralized approach to trustworthy data sharing and collaboration through commonly agreed principles, they form the foundation to the latest public and private data exchange initiatives. They promote breaking through the fear of sharing valuable data across organizations and sectors to unlock new business value from data.

Whilst the European Commission is estimating the economic impact of data sharing in the EU to increase by € 7-14 billion by 2028, commercial enterprises are recognizing the potential of data sharing as well. With the global data monetization market being expected to reach US $ 7.3 billion (GrandView Research, 2024), the promise of new revenue streams through data monetization is pushing them towards the trust framework that data spaces offer. Additional benefits such as operational efficiency, innovation and regulatory compliance support the business case for data spaces across all organizations.

But why do we need a framework emphasizing trust? What does a data space look like, and how does it differ from traditional data sharing? Let’s dive in…

Data spaces are an extension of well-established data sharing and collaboration practices with a strong emphasis on data sovereignty and unlocking data value. The core concept involves a decentralized infrastructure, enabling trustworthy data sharing and exchange based on commonly agreed principles. It connects data owners and data users through building blocks focused on governance, trust, interoperability, and data value.

To share data within a data space, several other actors and components take up a supporting and facilitating role.Data spaces are an extension of well-established data sharing and collaboration practices with a strong emphasis on data sovereignty and unlocking data value. The core concept involves a decentralized infrastructure, enabling trustworthy data sharing and exchange based on commonly agreed principles. It connects data owners and data users through building blocks focused on governance, trust, interoperability, and data value.

To share data within a data space, several other actors and components take up a supporting and facilitating role.

The goal of data collaboration is to derive value from data, and as such, the data users are central to the whole operation of the data space. These data users can be a variety of profiles, from analysts to researchers or policy makers. Given this potential diversity, it is important to provide them a user-friendly way of getting access to data. An intermediary data consumer, either organization or system, can support them.

By verifying their identity with an identity provider, they gain access to the data space. To find the data they are looking for, one or more brokers offer a data catalog with detailed information about the data available and how it can be used as defined by usage policies. They also include a (semi-)automated process for requesting access to data. Recurring data requests may be operationalized as an “app” or service in a marketplace-like app store to provide automated and integrated access.

The data owners provide the data, supported by data providers as an intermediary. They define the usage policies of individual data assets, including for example any pricing associated. Data is offered in an interoperable way to facilitate usage and linking, following agreed upon standards and vocabularies.

Once the data is exchanged as part of the data request, the data providers and consumers leverage secure connectors to share the data directly, bringing to life the decentralized nature of the data space. The clearing house follows up on these transactions, performing monitoring, logging, and settling any legal or financial matters.

None of these contributors need to be singular. Multiple actors with the same role can co-contribute within a data space, provided they collaborate within established cooperation agreements through process, technical and legal interoperability. The term “prosumer” is frequently used to highlight the fact that organizations often can assume the role of both data users as well as owners, benefiting from data collaboration in both directions.

In turn, data can be shared in and across multiple data spaces. Interoperability is the foundation for enabling cross-domain data collaboration across data spaces. Machine-to-machine interoperability ensures this can be automated as much as possible, streamlining operations and reinforcing trust within the data space.

Enabled by this trustworthy foundation, ecosystems can unlock value from data sharing in data spaces on the following 4 key value drivers:

• Innovation: New use cases and products are enabled through access to an increased variety of data, as well as larger volumes.
• Operational efficiency: Standardization of data integration with the outside world builds upon streamlined internal data operations.
• New revenue streams: Safeguarded by usage policies, data exchange and providing related services can become a new source of revenue.
• Regulatory compliance: With legislation working on public data spaces coming out at all levels, participation can become non-negotiable.

With almost half of the initiatives focusing on preparing data spaces, there are some actions to take before an organization is ready to join or initiate a new data space. We see three key strategic actions required for an organization to enable interorganizational data collaboration in data spaces as a first class within their overall operating model:

1. Embracing the benefits of data spaces within their overall strategy: Integrated within an organization at all levels, data spaces promise the possibility of streamlining both internal and external data operations and opening up new opportunities to derive value from data.
2. Balancing focus on boundaries and core (internal) data capabilities: By elevating the maturity of an organization’s integration at the boundaries of their systems with the outside world, data spaces enable pragmatic connections whilst accelerating future adoption to increase the core maturity. A sound data strategy must go hand-in-hand with stronger data management and operations capabilities.
3. Engaging their community: Data spaces require an outside-in look, evaluating how to derive value from sharing your own data with others, as well as data from others. Understanding who these others are, what they are looking for, and what they have to offer is key.

Through these actions, organizations can start on their journey to growing their own data space maturity and readiness.