To develop the data economy, experts believe that a measurement methodology is needed, along with learning from leading markets such as the US, China, and the EU.
At the International Conference on Data Economy, organized by the Ministry of Public Security on December 13th in Hung Yen, experts offered various opinions on the importance of the data economy and the direction for this industry in Vietnam.
Emphasizing the importance of data as a production factor "on par with land, labor, and capital," Major General Nguyen Ngoc Cuong, Director of the National Data Center, stated that the country that manages data better and exploits it more intelligently will have a superior competitive advantage globally.
"Vietnam is not outside this trend," he said, noting that Vietnam is making progress in the field of data economy, such as building a modern, secure, and interconnected national data system; developing and operating many core data platforms; and calling for experts and scientists to cooperate on joint research programs, fostering long-term collaboration between Vietnam and the global academic community in the field of data economy.
Major General Nguyen Ngoc Cuong delivered the opening remarks at the workshop. Photo: Hoang Anh.
Directions for Data Economy Development:
The data economy is understood as an economic system in which data is the core resource for creating value and innovation. This system includes activities such as collecting, storing, sharing, and commercializing data, thereby opening up new business and service models, increasing competitiveness in many fields, but also raising questions about ownership, governance, and benefit allocation.
At the workshop, several models of data economy development and AI applications were analyzed, including those of the US, China, and Europe. The US is considered a leader in commercial innovation, but lacks a unified legal framework at the federal level. China is strongly implementing centralized data strategies, linked to strict localization laws, while the EU pursues a 'human-centered' model, focusing on trust, digital sovereignty, and interoperability. A series of EU policies, such as the Data Governance Act (DGA) and the Data Act, have been emulated in many parts of the world.
According to Associate Professor Ali Al-Dulaimi, Head of the Department of Technology and Computer Science at the British University Vietnam (BUV), each model has strengths that Vietnam can learn from and risks to avoid. For example, a market-led model can bring rapid development and strong private capital, but it also risks being dominated by technology corporations. A people-centered model helps build social trust and ensure fairness, but it comes with too many legal barriers.
Photo: Hoang Anh)
From this, he recommended that Vietnam could form a "hybrid" model. "Vietnam can combine the guiding role of the State, the innovation capacity of the private sector, and mechanisms to protect citizens," he said.
One of the challenges in developing the data economy and AI is policy. According to Al-Dulaimi, Vietnam's development of an Artificial Intelligence Law with risk levels could be a "sweet spot" in policy, balancing regulatory requirements without stifling innovation, creating a foundation for the safe development of the data economy and the use of AI.
Another approach suggested by Professor Tran Ngoc Anh from Indiana University and President of the Vietnam Institute for Innovation is to develop the data economy on three key pillars: Digital infrastructure such as 5G/6G and data centers; Digital human resources in data science, AI, and cybersecurity; and Digital institutions, such as personal data protection, open data sharing, cybersecurity, and policy sandboxes.
He proposed the "4T Value Creation Cycle" model for data, consisting of: Collection, Refining, Deployment, and Regeneration. In this model, data is used for AI to create intelligent products and services, optimize operations, and continuously generate new data, constantly expanding economic value.
Drawing on international experience, he suggested an ecosystem approach involving the State, businesses, research institutions, and localities, with the Government acting as the "conductor," building reliable data infrastructure, measurement systems, trading platforms, and a human resource ecosystem for data.
Measurement in the Data Economy:
One of the key factors determining the development of the data economy is the need for measurement. According to Associate Professor James Abdey from the London School of Economics and Political Science (LSE), data has become a core intangible asset that fuels the competitiveness of modern economies. However, the lack of a comprehensive measurement system means that much of the economic value derived from data is not reflected in GDP.
This can lead to underestimation of the productivity and scale of the data economy, hindering policy planning, monitoring digital transformation progress, and attracting investment. For Vietnam, Associate Professor Abdey argues that the lack of a measurement framework will limit the ability to identify the potential of the data economy, compare competitiveness within the region, and attract investment.
Drawing on the UK's experience, he suggested that Vietnam build a measurement framework combining international standards with a practical approach, starting with pilot programs in three data-intensive sectors – healthcare, education, and finance – to quickly demonstrate economic value before nationwide rollout.
According to Professor Chu Hoang Long of the Crawford School of Public Policy at the Australian National University, Vietnam needs to simultaneously measure three groups of factors: data exploitation, data provision, and the policy and business environment. He suggested that
data providers, in addition to building robust data infrastructure, should promote data sharing through mechanisms such as open data portals, trusted data exchange networks, and sector-specific data spaces. Furthermore, it is necessary to create data such as large public data repositories, data collection programs, and data generation units.
Professor Tran Ngoc Anh also proposed a roadmap for the 2026-2030 period, suggesting that Vietnam should focus on perfecting the institutional framework and measuring the data economy, alongside promoting data sharing, testing frameworks, expanding AI-data human resource training, and developing industry-specific data ecosystems.
"AI will lead the global economy, but data is the 'lifeblood' of AI," he said. "If we leverage infrastructure, institutions, and human resources in a coordinated manner, Vietnam can absolutely achieve a breakthrough."
Luu Quy,
according to VnExpress