Understanding the importance of effective data spaces

In an increasingly interconnected world, organizations need to be able to collaborate seamlessly across their ecosystems. A free flow of data is essential to underpinning these deeper working relationships. Sharing data externally delivers a range of critical benefits – it reduces risk, increases supply chain efficiency, enables the tracking of sustainability metrics, and simplifies regulatory compliance, for example.

However, to share data effectively it has to be high-quality, interoperable and easily accessible, while still protecting competitive advantage and intellectual property. This is driving the rise of data spaces, federated frameworks for cross-industry information exchange, especially within Europe. Drawing on the latest Gartner research, this article explains what a data space is and the benefits and challenges around their implementation.

Data spaces facilitate the secure, federated sharing of data between multiple data owners. In its report, Innovation Insight: Data Spaces Enhance Data Value and Sovereignty Across Industry Ecosystems, Gartner defines a data space as:

“Conceptual and technical frameworks designed to enable the secure, sovereign, and interoperable sharing of data among multiple stakeholders. Unlike conventional data repositories that consolidate data in a central location, data spaces provide a decentralized infrastructure where data remains stored within the systems of its respective owners, but is connected through standardized interfaces and governance protocols.”

This federated data architecture means that organizations remain in physical control of their own data, with information exchanged through clear, secure policies and connectors between systems. To ensure that data is both consistent and easy to find/understand it must be described using rich metadata and a common vocabulary to avoid potential confusion.

Data spaces overcome challenges such as data silos within organizations, and their adoption is being led by the European Union, which is increasingly mandating them for regional compliance initiatives. To support this it has created a range of resources such as standardized technical infrastructure, reference architectures, and interoperability specifications.

The three terms – data space, data exchange, and data marketplace, are often used interchangeably. However, they do have differences in their usage and structure:

• Data space – data is shared through a federated architecture, with data remaining within the systems of its owner
• Data exchange – data from multiple providers is shared and accessed through a centralized location with a standardized structure
• Data marketplace – data is securely shared by a single organization with one or multiple partners

Data spaces break down individual data silos, and allow organizations to share their data securely with their partners and wider ecosystem, while still retaining control and management of their data assets. Data spaces deliver benefits for organizations in five key areas:

• Regulatory compliance: Ensuring compliance with evolving regulations, particularly for organizations operating in the EU, under regulatory mandates like the EU Data Act (2025)
• Lowering risk: By providing real-time visibility into the wider ecosystem, such as monitoring supply chains for early warning of potential disruption
• Cost reductions: Increasing efficiency and business value by seamless data exchange and the elimination of silos
• New business models: Enabling organizations to differentiate themselves and offer new “as-a-Service” business models, such as subscriptions for machinery or services
• Enabling AI and advanced analytics: Supporting scalable, interoperable access to the largest volume and widest range of data, delivering depth and context to big data analytics, Large Language Models (LLMs) and AI agents
• Sustainability tracking: Enabling organizations to monitor and audit sustainability metrics, such as emissions across their entire supply chain

Examples of industries where data spaces deliver particular value include manufacturing, automotive, government, defense, healthcare and agriculture. Use cases include digital product passports (which track the lifecycle of components such as EV batteries), health data, mobility/travel and logistics, and in delivering efficiency and transparency across farming and food production.

Clearly, successful data spaces rely on the availability of understandable, easily accessible and high-quality data in consistent, well-governed formats. However, many organizations are still struggling with their internal data quality and governance, with information stuck in departmental silos. This challenge needs to be overcome through more mature data management practices.

At the same time, data has to be understandable to partners. That requires standardized metadata and an agreed glossary of how data is described and what it refers to. This avoids confusion between terms and ensures consistency.

Due to these factors, data space programs face ten risks, according to Gartner:

• A lack of machine-readable data holds back automated usage, particularly for AI
• Inconsistent semantic standards create roadblocks and a lack of understanding
• Given the technical and data requirements of data spaces, CIOs and CDOs need to work closely together. Failure to collaborate hampers adoption
• Data spaces require substantial, foundational investments in data management resources and skills to ensure data quality and robust infrastructure
• Data spaces rely on large-scale participation to realize network effects. This means that slow uptake will lengthen the time to achieving ROI
• Organizations may be concerned about safeguarding their confidential data, particularly when sharing with competitors
• Governance and monitoring of data use requires strong frameworks to ensure compliance
• Close collaboration between players in a single industry can be seen as anticompetitive behavior by regulators
• There is a lack of global consensus on data space standards, with differing national and regional legal regulations and cultural approaches
• Ecosystems may be dominated by major players, leading to risks around information asymmetry and uneven relationships

Whatever industry they are in, organizations can potentially benefit from rolling out or joining data spaces going forward. However, before embarking on these collaborations they need to put in place the right data foundations, which will also underpin other initiatives such as AI and data democratization.

That means first focusing on:

• Ensuring data quality and accuracy through cleaning and validation of all relevant datasets
• Creating strong governance frameworks with clear roles to ensure regulatory compliance
• Standardizing data formats and metadata with industry frameworks to enable interoperability
• Monitoring the ongoing accuracy and usage of data through lineage and anomaly detection tools
• Collaborating with small groups of peers to launch pilot projects that can then be extended after showing ROI
• Ensure data is easily discoverable and accessible through an intuitive interface that is both human and machine-readable

Once these foundations have been put in place, Gartner recommends that CIOs should look to extend their programs by:

• Widening collaboration beyond small groups to work with European/global data spaces
• Increase trust through smart contracts to safeguard data and automate compliance
• Ensure the right data is AI-ready by mapping objectives and needs against available information from across the ecosystem
• Build a culture of data sharing and collaboration through training and upskilling
• Explore new business models, piloting and rolling-out new opportunities

Gartner’s report includes Huwise (previously Opendatasoft) as a representative provider of data space technology.

Built on over 14 years of experience, Huwise’s self-service data product marketplace enables organizations to seamlessly build and extend data spaces across their ecosystem, focusing on consumption, quality, governance and collaboration.

An intuitive, e-commerce style interface and AI-powered search enables all business users to quickly discover, access and understand which data assets are right for their needs, They can then quickly consume them in their chosen format, whether through visualizations, downloads or via APIs. All types of data can be exchanged and consumed, including ready-to-use, high-value data products, with their quality and reliability governed through integrated, binding data contracts.

The ability to apply structured metadata, powerful processors and a common business glossary all ensure that data is consistent and standardized, whatever its source, increasing trust and encouraging usage. Connectors integrate data from multiple business systems and sources within partners to maximize available data.

Governance and security are key to protecting confidential information within data spaces. Huwise safeguards data and ensures compliance through granular access management controls, full lineage of where data is being used, and the flexibility to quickly grant access to specific data assets on a full or temporary basis.

Data spaces must encourage and drive collaboration across ecosystems. Huwise enables this through integrated workflows dedicated to sharing data, ideas, and feedback, enabling all stakeholders to work together more easily and effectively.

Extending data sharing to data spaces is a key opportunity for organizations to strengthen collaboration across their ecosystem and operate more confidently in an increasingly complex world. As Gartner stresses, success requires a focus on having the right technical foundations in place, ensuring that data is high-quality, easily accessible, understandable and above all trustworthy. This will drive data consumption, increase business benefits and enable new opportunities moving forward.

Want to find out how to get started with your data space strategy? Book a meeting to speak to our experts and tap into their knowledge and experience.