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Data has become one of the defining assets of the 21st century—on par with financial and human capital in shaping the prosperity and resilience of societies. It is both a resource and an enabler, powering innovation, informing policy, and unlocking new opportunities for sustainable growth. Yet, like any powerful asset, data has two faces. It can be used to empower individuals and communities, solve public challenges, and create economic value—but also to extract, surveil, and marginalise.
Despite its ambitions, the European Data Strategy has struggled to fully realise the transformative promise of data, while preventing possible harm. It has yet to achieve a dual imperative: protect people from extractive and opaque data practices, and unlock data in a way that systematically advances societal goals.
Part of the challenge is that the strategy has been predominantly supply-driven, emphasising technical infrastructure without matching this emphasis with robust human, organisational and governance infrastructure, and without adequately aligning supply assets with pressing societal needs or building the capacities required to turn data into insight and impact. In addition, a data strategy and a solid digital industrial policy for the EU need to embed legitimacy, sustainability and integrity at its core and to tap into its thriving ecosystems by going beyond state-driven and top-down approaches.
In this brief, we respond to the European Commission’s plan for a new strategy, the Data Union strategy, introduced in the new AI Continent Action Plan. We consider these plans alongside other announced and related initiatives such as the EU Cloud and AI Development Act and the EU Democracy Shield. Our proposed actions, which we further develop below, can be summed up under the following points:
Recommendation 1. Articulate open innovation as innovation that is purpose-driven and avoids monopolistic capture
Without a shared purpose, openness can still lead to extractive, inequitable outcomes—for example, when big players capture value from smaller contributors. Articulating open innovation as purpose-driven also entails rethinking governance frameworks and funding mechanisms so that cross-border knowledge and data flows allow innovators from all sorts of localities, sizes and languages to succeed in recombining state-of-the-art data and technologies for the public interest. The following recommendations refer to concrete ways of articulating this vision of openness.
Recommendation 2. Prioritise high-impact and tangible purposes
Focus data-related investments on urgent and tangible societal challenges—such as meeting the EU’s net zero targets, preventive health, a resource-efficient and competitive economy, preparing the young generations through skills and education, information integrity and civic engagement, and sustainable urban and rural development—so that the Data Union serves visible, public-interest use cases that guide innovation and collaboration. Advancing Europe’s digital industrial strategy needs to go hand-in-hand with meeting its environmental and climate goals. The development of data centers, AI and the infrastructure for emerging technologies needs to be done within principles of sufficiency and equity.
Recommendation 3. Build the missing human infrastructure
Upskill people and organisations. Invest in data stewardship competences - skills and roles within organisations across sectors that understand the data governance and data sharing sector, can implement data reuse policies, and contribute to data reuse ecosystems such as data spaces or data intermediaries (data trusts, data cooperatives, etc). Embed these roles within the broader EU Union of Skills agenda and support them through policy, funding, and training.
Recommendation 4. Update data governance: beyond consent towards a public mandate and new licensing forms
(i) Evolve current frameworks by complementing consent-based approaches with more meaningful democratic governance and participatory processes for a legitimate social license or public mandate for data reuse.
(ii) Develop in collaborative ways the legal and institutional innovation required, including next-gen impact and responsible licensing –licensing that overcomes the current limitations of copyright in the context of generative AI, so that data governance is fit for cutting edge and emerging technologies.
Recommendation 5. Adopt polycentric and participatory governance
Enable the dynamic, decentralised governance of data labs and of data flows across data spaces—avoiding monopolistic capture and top-down control—by empowering local, regional, and cross-sectoral actors through participatory and plural models of governance. The Data Union strategy needs to come with a clear governance model for its implementation and for the governance of the planned infrastructure and data spaces.
