Data Spaces between Co‑ordination Challenges, Financial Viability and Public Policy

Annex A. OECD Workshop on “Assessing the Sustainability of Business Models in Data Spaces and the Role of Public Policies”

Context

The OECD Expert Workshop on “Assessing the sustainability of business models in data spaces and the role of public policies” was held on 11 March 2025 at the Data Spaces Symposium 2025 in Warsaw, Poland. The workshop brought together practitioners, policymakers and industry experts from Europe, Japan and North America to critically examine the financial sustainability of data space business models on the basis of concrete use cases, both successful and failed. For the agenda, see Data Spaces Symposium (2025[1]).

Business models

No single business model for data spaces exists. The value proposition, cost structure and revenue model depend on the sector, the use case, the participants involved and the regulatory context. A central finding was that value is created at the level of individual use cases, not at the level of the infrastructure itself; prioritising use-case value over infrastructure development was consistently associated with stronger prospects for sustainability.

An imbalance frequently arises between those who bear the costs of contributing data and those who capture the value of using them; this was identified as a persistent challenge. Each stakeholder tends to understand its own business model but often lacks a view of the overarching business value. At least one orchestrator or champion who understands both the overall value proposition and the addressable market is therefore necessary. However, distributing the benefits created by a data space requires complex negotiation among members, including orchestrators who may also have their own business interests.

Regulatory compliance is a primary driver of participation, but building a business model primarily on compliance carries risks. In markets dominated by small and medium-sized enterprises (SMEs), regulations may not serve as an effective incentive: SMEs often need to digitise their operations first, and additional requirements only increase costs and reduce their capacity to innovate with shared data.

Data spaces face a classic chicken-and-egg bootstrapping problem. Strategies discussed included attracting participants who act on both sides of the market; leveraging existing platforms; deploying high-value initial use cases (“seeding”); securing “marquee users”; and cross-subsidisation during early adoption. One approach involved keeping the data space operator deliberately compact, connecting “collaborative services” (shared infrastructure guided by public guidelines) with “competitive services” offered by third-party providers through open application programming interfaces. A recurring challenge is “reinventing the wheel”: consortia whose members vary across initiatives spend excessive time debating technical components from scratch.

Use-case insights

Automotive and battery traceability (Japan). A data space established by Japanese automotive manufacturers to compile carbon footprint data for EU Battery Regulation compliance was presented as a model of sequenced expansion: starting with battery carbon footprint traceability (“collaborative services”), then expanding to other vehicle components and eventually to “competitive services” such as battery passports and supply chain resilience. Its revenue model combines membership fees with transaction-based data-use fees. The operator adopted an existing Open Data Spaces Reference Architecture Model (former Ouranos Ecosystem) for technical standards and obtained accreditation as a “public interest digital platform operator.”

Maritime data sharing (Finland). The Finnish Maritime Data Space was highlighted as an example where the business model was designed before the technology. Five sequential steps were identified: (i) identify ecosystem champions from shipping companies, charterers, ports, cargo owners and relevant public entities; (ii) develop systemic service design by mapping stakeholders’ challenges, roles, data holdings and contractual frameworks; (iii) map the real-life contractual model and value model; (iv) build the business model, including a revenue-sharing arrangement among participants; and (v) then enable the solution through data space technology. This process took eight months. Key factors for industry buy-in were estimated savings of EUR 45 million in fuel and EU Emissions Trading System costs per year (2022 baseline for Finland) and CO₂ emission reductions exceeding 24%.

Agricultural data (France). The bankruptcy of a prominent agricultural data space operator in November 2024 served as a cautionary case. The operator had focussed on building technical infrastructure without demonstrated use-case value sufficient to sustain participation. The agricultural sector’s fragmentation into many SMEs made it inherently harder to organise into a data-sharing ecosystem than sectors with concentrated global companies sharing common supply chains.

Policy opportunities and challenges

Participants distinguished between two purposes of public funding: the development of schemas, protocols and interoperability frameworks (pre-competitive research and development), and the creation of actual data space ecosystems, where the case for public funding is more conditional. Not all data spaces can be considered public goods; where many depend on public funding, this may indicate that viable business models have not yet been found rather than that they constitute public infrastructure. Some participants cautioned that long-term public funding may create dependency effects that prevent data space stakeholders from being efficient and innovative.

Japan’s certification system for “Public Interest Digital Platform Operators,” administered by the Information-technology Promotion Agency (IPA), was discussed as a potential model for other jurisdictions seeking to distinguish data spaces that serve a public interest from those that are purely commercial. IPA has also invested in capacity building (data space guides, handbooks for Chief Data Officers, data maturity frameworks). The rationale is that businesses first need to understand what data spaces are before they can participate, given that low awareness, limited skills and low data maturity were identified as significant barriers among SMEs.

Participants drew an analogy with the Internet, arguing that standardisation discussions should focus on the protocol layer rather than on how protocols are implemented or used in business. This implies a “coopetition” model in which co‑operation on protocols coexists with competition on services. Concern was expressed about a trend of de-globalisation, a shrinking willingness to share data across jurisdictions. Differences in regulations between countries were identified as having a potentially negative impact on global manufacturing industries. Participants observed that intra-organisational data ecosystems in the United States appear to exist in business but are not named as data spaces. This suggests the concept may need to be framed differently outside the European context.

References

[1] Data Spaces Symposium (2025), “Memories of Data Spaces Symposium 2025”, https://www.data-spaces-symposium.eu/program-2025 (accessed on 1 March 2026).