With the deepening of a new round of technological revolution and industrial transformation, the digital economy is becoming an important force in restructuring global factor resources, reshaping the global economic structure, and changing the global competitive landscape. Data is becoming a new key production factor in addition to land, labor, capital, and technology in the digital economy era, and data assets are becoming the most important assets for enterprises in the 21st century. How to correctly understand data assets and data assetization? How to use data assetization to promote high-quality economic development? These issues need to be studied and explored. The significance of promoting data assetization is that assets are resources with economic value and potential returns, and data assets refer to data resources with economic value and future returns. As a carrier of recording economic activity information, data assets are by-products generated from economic activities, such as consumer behavior data generated by browsing and purchasing, enterprise business data generated by daily operations, production and operation data generated by industrial machinery operation, logistics location data generated by vehicle driving, etc. The main value of data assets lies in alleviating or eliminating information asymmetry and uncertainty in economic decision-making, improving decision-making efficiency and accuracy, thereby promoting innovation and increasing total factor productivity. Data assetization is the process of managing and operating data as an asset. It systematically collects, processes, analyzes, and applies data to achieve the transformation of production methods and economic models that value data elements. The assetization of data is of great significance in empowering China's high-quality economic development. For example, by collecting and mining consumer behavior data, companies can gain insights into potential user needs, actively engage in product innovation, and develop innovative products that meet or even lead market demand and are highly differentiated. This can avoid vicious competition caused by internal competition, effectively expand domestic demand, and promote the transformation and upgrading of traditional enterprises. By collecting, sharing, and analyzing business data, financial institutions can provide more effective financial services to small and medium-sized enterprises and science and technology startups, better serving the real economy and technological innovation. By collecting and analyzing logistics and industrial machine operation data, enterprises can optimize production and logistics processes, save energy and reduce emissions, reduce costs and increase efficiency, promote green transformation, and also contribute to the integration and transformation of traditional manufacturing and production service industries. In short, data assetization enables data to serve as a new type of production factor, which can play a multiplier and amplification effect on improving the production efficiency of other factors, optimize the stacking and fusion effect of resource allocation between different factors, and thus provide the digital economy with a "dimensional upgrade" upward development space compared to traditional economic paradigms. Grasping the basic characteristics of data assets, data assetization differs from other types of traditional assets, as data assets have some unique features. On the one hand, data assets are a byproduct of economic activities. The value of data assets depends more on specific usage scenarios and users, with a high degree of personalization and a lack of market recognized common value. The same data may be valuable to one user or scenario, but may be of no value to another user or scenario. Due to the fact that data assets are by-products of economic activities, they are often the result of the joint participation and collaborative creation of multiple parties. For example, the consumption data of e-commerce platforms is the result of the co creation of consumers, merchants, and platforms. Traditional concepts of ownership or property rights are difficult to apply to data assets. On the other hand, data assets are non competitive. Data assets can be infinitely replicated and reused, and any number of businesses, individuals, or machine learning algorithms can simultaneously use the same data without affecting the use of that data by other businesses. It is precisely because of this non competitiveness that data sharing and circulation can bring about increasing returns, and the integration of multiple data assets can bring overall value and economic and social benefits far greater than the sum of individual data values. Therefore, the use of data is often more important than all of it. Given the above characteristics, data assetization needs to adapt more to the changes in business models and production relations in the digital economy era. Currently, the assetization of data in the global digital economy is increasingly shifting from direct trading of raw data to indirect trading and circulation based on highly scenario dependent and data barter transactions. A typical way is for users to contribute their data to obtain free or low-cost access to digital services such as social media, search engines, e-commerce, etc. After collecting and analyzing user data, data buyers provide data intelligence services to enterprises that are interested in users to help them better locate and meet their needs. This approach not only solves the data supply problem caused by overly complex data ownership, but also addresses the revenue pricing problem caused by the lack of market recognized common value for data assets. Correspondingly, this bilateral market platform economy involving three parties has increasingly become an important organizational form for data assetization in the digital economy era. In the era of industrial economy, enterprises and users are the main entities. Enterprises create value, users consume value, and the market acts as an "invisible hand" for resource allocation and value exchange. In the era of digital economy, relying on digital technology and networked organizational methods, platforms enable efficient and convenient matching between supply and demand sides. Timely and accurate data intelligence greatly reduces information asymmetry and market transaction costs, improves economic efficiency, and becomes the main regulatory mechanism for resource integration and value co creation in the market. Empowering high-quality economic development with data assetization. Currently, China's data assetization field is facing problems and challenges such as insufficient data supply, fragmented data markets, and relatively single pathways for data assetization. We should actively promote the assetization of data and the construction of data element markets, and empower high-quality economic development with the assetization of data. For many years, the forward-looking construction of digital infrastructure and the flourishing development of the digital economy by the Chinese government have transformed China's massive population and market advantages into massive data dividends in the fields of individual users and consumer demand. However, the data accumulation and supply of enterprise users, especially traditional enterprises and production and manufacturing fields, are still seriously insufficient. We need to vigorously promote the construction of national data infrastructure, advance the digital transformation of traditional industries, transform China's rich manufacturing scene advantages into data dividends, and connect the data chain between the upstream supply side and downstream demand side of the industrial chain. Especially in the policy design and implementation of new industrialization processes and large-scale equipment updates, it is not only important to focus on investment in digital hardware equipment, but also to pay attention to the collection of data resources and the accumulation of data assets in the production and manufacturing fields. In recent years, with the development of digital government and smart city construction, as well as the digital transformation of industries, a large amount of public and enterprise data has been generated in many places and industries. However, these data are scattered across various departments and enterprises, without forming an integrated data market, which cannot fully leverage the economies of scale and increasing returns of data. Under the premise of ensuring data security, we should vigorously promote the open sharing and interconnection of public data, encourage and guide local governments and industry leading enterprises to develop comprehensive digital economy platforms and industrial chain digital platforms, integrate comprehensive and industrial chain data, and build a "platform+application" platform economy ecosystem. At present, many local governments are highly concerned about data assets and data assetization work, and have introduced some policy measures to promote data assetization work, but most of them focus on direct data transactions. Some data subjects focus on "data collection and organization" rather than "data application and circulation", and data providers and third-party professional service agencies are relatively single, with insufficient differentiation. With the development and large-scale application of cutting-edge technologies such as artificial intelligence big models, it is necessary to prosper the data service ecosystem, vigorously cultivate and develop innovative indirect data trading and circulation models such as "data-as-a-service" and "model-as-a-service", encourage platform based enterprises to transform into data merchants, and empower traditional enterprises to transform and upgrade by opening up data intelligence services to small and medium-sized enterprises. Some domestic data intelligence service providers have begun to make some beneficial attempts, empowering small and medium-sized merchants through digital intelligence to share user insights on the demand side with upstream manufacturing enterprises, and achieve a win-win situation for the entire platform ecosystem. The value realization of data assets is not necessarily limited to the trading and sharing of raw data. It should focus on the sharing of data analysis and application capabilities, and pay attention to the value realization of data assets, especially the ability of data analysis and intelligent service applications. (Liao Xinshe) Author: Chen Yubo (Chair Professor, School of Economics and Management, Tsinghua University, Director of Internet Development and Governance Research Center)