However, data volumes may surge rapidly, productivity does not necessarily rise in tandem. Growth in many economies still mainly relies on capital expansion of capital and labour, while data remains fragmented and “siloed” by each sector, level, and organisation. As such, the question is no longer whether data matters but rather why it becomes a true growth driver only when the economy has enough endogenous capacity to master and exploit it effectively.
Data and the economy’s endogenous capacity
Unlike capital and labour, data does not increase through material stockpiling but through connection and exploitation. This characteristic gives data unique economic attributes, changing traditional growth logics and directly influencing endogenous capacity of an economy.
Data is non-rival in consumption: a dataset can be used simultaneously for multiple purposes at near-zero replication cost, without depletion if properly governed and shared. When integrated from diverse sources, data generates increasing returns to scale, significantly enhancing analytical, forecasting, and optimisation capabilities. At the same time, it produces powerful network effects: the larger the data ecosystem, the higher the quality of digital models and services, and the greater the level of personalisation.
However, these attributes translate into growth only when data is organised, standardised, and interconnected within an appropriate institutional and technological environment, where legal frameworks on data rights, sharing mechanisms, digital infrastructure, and analytical capabilities are developed in a coordinated manner.
The endogenous growth theory of Paul Romer (an American economist) emphasises that knowledge, research–development, and human capital are not only inputs but self-reinforcing resources that generate positive externalities and increasing returns, thereby maintaining long-term growth. In the digital economy, data, when mastered, operates under similar logic. The ability to master data thus becomes a decisive condition for transforming it into an endogenous growth driving force.
Mastering data goes beyond collection and storage; it encompasses the entire value chain: access management, cleansing, standardisation, interoperability, analysis, and exploitation. When data is systematically integrated into R&D, governance, and production, the economy enables a continuous, rapid, and large-scale knowledge accumulation mechanism. Conversely, when data is fragmented, “locked” within isolated systems, or dependent on external platforms, this knowledge resource weakens, undermining long-term growth potential.
When each link in the data value chain develops synchronously, an endogenous learning loop appears: operations generate data; data is transformed into knowledge; and knowledge feeds back to improve governance, production, and products, thereby generating higher-quality data. This is the core mechanism through which data becomes a sustainable growth driving force.
The digital era is shaped by the rapid and continuous increase of data volumes on an ever-growing scale. Beyond purely technological dimensions, this data explosion has created a new economic space — the data economy — where data is regarded as a distinctive intangible asset: directly participating in production process like capital and labour, while also underpinning innovation and productivity enhancement.
Viet Nam’s “window of opportunity” and the bottlenecks to address
The National Assembly’s approval of the Data Law (effective from July 1, 2025), together with major guidelines of the Party such as Resolution No. 57-NQ/TW and other resolutions and programmes on digital transformation, the digital economy, and digital society, has established a crucial legal foundation for governing, exploiting, and developing Viet Nam’s data market. The National Data Centre in Hoa Lac (Ha Noi) has been put into operations, forming a key infrastructure for national-scale data storage, integration, and analysis, enabling broader data sharing throughout the state, businesses, and society.
However, significant gaps remain between this legal foundation and tangible growth outcomes. The current data continues to be fragmented across sectors, levels, localities and operational systems. Many agencies and organisations hold data but lack interconnection mechanisms, resulting in duplication in collection, overlapping updates, and declining data quality. When data remains “closed”, the economy loses increasing returns to scale and network effects — the core value of the data economy.
Another bottleneck lies in inconsistent data and technical interoperability standards. Different data models, identifiers, data dictionaries, and technical conventions make integration costly and time-consuming, thereby limiting cross-sectoral data exploitation and the development of new products and services.
Furthermore, governance mechanisms for data rights and accountability remain insufficiently clear. Where controlled access, sharing, traceability, and quality measurement of data are inadequately designed, entities tend to adopt defensive positions, prioritising local safety over system-wide optimisation. As a result, it is difficult for data to become a liquid asset or serve as a large-scale input for innovation and total factor productivity (TFP) improvement.
Positioning data as a central resource
To transform the “window of opportunity” offered by the data economy into genuine endogenous growth momentum, it needs a focused and coherent approach, with four priority solution groups.
First, data standardisation and interoperability should be considered as a national productivity programme. Data standards, metadata, identifiers, and technical specifications must be regarded as productivity infrastructure, like electricity, roads, or ports. Effective standardisation reduces integration costs, shortens time-to-market for digital products and services, and accelerates innovation.
Second, controlled data-sharing mechanisms should be designed to balance openness and risk management. Sharing must be linked to data classification, access control, traceability and quality measurement, while also providing sufficient driving force for data providers. Without such driving force, data will remain “closed” and network effects cannot form.
Third, the data value chain must be reorganised, prioritising the completion of national databases and data-sharing platforms. Priorities should be placed on high-impact areas affecting TFP, including identification, land, enterprises, finance, healthcare, education, and logistics.
Fourth, analytical and modelling capabilities should be invested as a form of human capital for endogenous growth. Data becomes knowledge only when supported by competencies in analytics, statistics, data science, data governance, and AI deployment. Developing skilled personnel, robust processes, and appropriate tools is essential to convert data into decisions and innovation, while ensuring safety, ethics, and compliance.
In the context of growth models based on capital and labour approach their limits, data is emerging as the central resource determining an economy’s endogenous capacity. Transforming data from a fragmented state into a mastered, strategically interconnected data system is crucial for long-term growth. When well organised and closely linked with innovation, governance, and production, in a context where the capital-and-labour-based growth model is gradually approaching its limits, data is emerging as a central resource determining the endogenous capacity of the economy. The shift from a dispersed state to a mastered, strategically aligned data system is crucial for long-term growth. When data is well-organised and closely linked to innovation, governance, and production, the economy can develop a mechanism for continuous knowledge accumulation, enhance TFP, and move towards sustainable growth based on self-reliance platform.
