What began as a movement for transparency, scientific collaboration and knowledge sharing has become a foundational layer of today’s data-driven and AI-enabled economy.

Open data and open government data advanced the view that public information is a national asset that should be accessible to citizens, innovators, researchers and entrepreneurs. Its objectives included improving transparency, fighting corruption, strengthening accountability, encouraging citizen participation and stimulating innovation. I think we can all agree that most (if not all) of these objectives are being achieved.

What do we see now? Fuelled by standardisation and interoperability, a new business model for the private sector players has emerged out of the individual “national assets” by sovereign states.

“Big data” becomes accessible as these national assets become global assets.  

Tech innovators saw the opportunity and took advantage of it leading to the AI boom which is generating more public and private value globally.

Data was once treated largely as a closed asset controlled by governments, universities and corporations. The Open Data movement helped change this by promoting the idea that data could be freely accessed, used, shared and reused for public benefit. Its roots lie in science (openness supports verification, collaboration and discovery); democracy (strengthens transparency, accountability and citizen participation); and innovation (enables new forms of digital innovation and economic value creation).

The movement was further strengthened by the rise of the Internet and digital computing that made it possible to share large volumes of information globally. At the same time, the open-source software movement demonstrated the power of collaborative creation, transparency and free access to knowledge. This led to expansion of government digitisation programmes and free and open national assets determined by few leaders.

In the process, governments and organisations gradually recognised that publishing datasets alone was not enough. To create value, data needs standards, metadata and interoperability mechanisms. This shifted the conversation from simply opening data to making data usable, trustworthy and reusable. The FAIR principles helped formalise this next stage by requiring data to be Findable, Accessible, Interoperable and Reusable. New licensing frameworks, including Creative Commons and technical standards that made datasets easier to publish, discover and reuse emerged.

But why do few private companies build monarchies for themselves out of public information that are national assets?

The modern AI systems depend on large-scale, high-quality and well-structured data ecosystems. The Open Data movement helped create this foundation, but AI requires more than openness. Data must also be trustworthy, well-governed, interoperable, machine-readable and accompanied by clear information about provenance, quality, consent and usage conditions. Smart Data makes data useful, trusted and interoperable. AI transforms data into prediction, automation and intelligence.

Two Big question:

“Who benefits most from this large volume of data created by a global movement, backed by science and technology, endorsed by national governments, and funded by big donors and foundations?”

And

“Can the sovereign states that freely released their national assets to build the AI hegemonies, get voice in the regulation and governance of the products of the data – the artificial intelligence?”

A bottom-up approach is needed to regulate and govern AI because the data used to train and refine AI models often originates in individual countries, sectors and communities. This raises important questions about the relationship between source data, model performance, algorithmic bias, accountability and national data sovereignty.

Regional blocs and coalitions of national governments can therefore play a stronger role in shaping AI governance. While the powerful nations have technologies, small nations can control how the technologies work. Small nations with a coordinated approach, can move from being passive sources of training data to active participants in the governance of AI systems.

The Open Data movement created important conditions for the AI revolution by expanding access to data, promoting openness and normalising reuse. The next stage requires stronger national data governance so countries can govern data flows, protect public value and ensure that AI-enabled innovation serves national development priorities.