Paper Sharing:Practical Exploration of Data Assetization Governance

1. Implementation Status of Data Governance in Enterprises
Today’s corporate data governance is like searching for treasure in a fog—full of opportunities yet fraught with risks. Driven by the National Data Administration’s push for market-oriented reform of data factors, more companies recognize the importance of a data assetization governance framework. However, they still face persistent challenges such as data silos, sunk data assets, and circulation bottlenecks when implementing it. For example, a major manufacturing firm operates over 20 business systems, with production data stored in ERP, sales data in CRM, and logistics data in WMS. This fragmented structure inherently limits the value of data.

1.1 Governance Strategies from Typical Cases
In the race for digital transformation, Guanqin Information offers a textbook example. By integrating a digital asset management system, it consolidated scattered departmental assets—contracts, drawings, process parameters—into packaged digital products, tripling the scale of data assets within six months. Their success lies in establishing an inventory-based data management system, using standardized processes to clarify ownership and application scenarios for each data asset. This approach functions like an “ID card” for data, resolving ownership issues and laying the foundation for cross-organizational data circulation. This “data bank” model is now being emulated by many manufacturers, as no one wants their data assets to remain dormant like raw ore.

The healthcare industry’s efforts are even more groundbreaking. A tertiary hospital, where medical images were previously stored in departmental servers, adopted privacy-preserving computation technology to enable hospital-wide sharing without data leaving local domains. Their two-tier data sovereignty mechanism is innovative: doctors access data with timestamped electronic signatures, while patients control access via dynamic authorization codes. This approach not only addresses ethical constraints in medical data sharing but also reduces privacy leakage risks to below 0.1%. Such a balance between utility and compliance opens new paths for data commercialization.

The financial sector has taken data governance to new heights. A joint-stock bank combined blockchain notarization with an intelligent decision support system, compressing credit approval time from seven days to two hours. Their key weapon is data adaptive optimization technology, which captures over 200 types of enterprise data—such as utility payments and tax records—in real time. A dynamic pricing model automatically generates credit plans, and each transaction’s hash is recorded on the blockchain, ensuring compliance and building trust between buyers and sellers in the data factor market. This real-time decision support system turns data into truly liquid capital.

However, effective data governance requires more than just building a data platform. Surveys show that over 60% of enterprises make the mistake of prioritizing technology over institutional design. For instance, an e-commerce platform invested heavily in a data warehouse but unclear data ownership left user behavior data worth billions as sunk cost. Only after introducing a market-oriented data transaction mechanism—with internal pricing and circulation of data usage rights—were they able to revitalize these data assets. This case illustrates that institutional innovation can sometimes be more critical than technological upgrades.

1.2 Lessons from Trial and Error
Three key lessons emerge from these cases: First, data governance requires both technology (e.g., privacy-preserving computation) and institutional arrangements (e.g., inventory management). Second, overcoming circulation bottlenecks depends on creating a trusted data transaction environment, where blockchain and dynamic pricing models prove highly effective. Third, reducing compliance costs cannot be done by enterprises alone—policy support, such as data sovereignty credentials, is essential.

Nevertheless, the biggest challenge remains making data actionable. Some enterprises spend millions on data platforms, yet business units still rely on experience-based decisions. This is like driving a sports car only within a residential compound—a tremendous waste. The future breakthrough likely lies in building an end-to-end data value chain, ensuring every piece of data finds its role in the business ecosystem. After all, the ultimate goal of data governance is not to restrict data but to turn it into a living resource that nourishes the entire commercial environment.

2. Financial Effects of Data Asset Capitalization
Data asset capitalization is a key aspect of corporate digital transformation, reshaping the structure and interpretation of traditional financial statements. According to the Interim Accounting Treatment Provisions for Enterprise Data Resources, data assets are defined as “data resources legally owned or controlled by enterprises that are expected to generate economic benefits.” The capitalization process includes cost aggregation from data collection, cleansing, processing, rights confirmation, and pricing. Compared to traditional intangible asset capitalization, data assetization shows greater flexibility in cost recognition and value assessment, offering new directions for enhancing financial statements.

On the balance sheet, data asset capitalization significantly optimizes corporate asset structures. For example, a manufacturing company incorporated costs related to its data governance platform—including hardware, algorithm development, and labor—into data asset accounting. This turned what was previously a ¥320 million IT expense into a long-term asset, reducing the debt-to-asset ratio by 2.7 percentage points. Such accounting practices mitigate the short-term profit impact of R&D investments while creating a depreciable digital asset reservoir. Studies by IASB indicate that the average amortization period for data assets is 5–8 years, providing financial flexibility for strategic transitions.

On the income statement, improvements manifest in dynamic adjustments to cost structures. The establishment of a data asset amortization mechanism enables enterprises to implement differentiated marketing strategies based on data lifecycles. A fintech company reported that using a dynamic data value decay model—amortizing high-frequency trading data over 12 months and customer profile data over 60 months—reduced annual net profit volatility by 18%. Data from pilot enterprises under the National Data Administration show that companies adopting graded data asset amortization reduced their R&D expense-to-revenue ratio by an average of 4.3 percentage points, easing financial pressure from innovation investments.

Improved financial metrics also enhance capital market valuation. Recognizing data assets in financial statements makes digital competitiveness visible. After an e-commerce platform disclosed the valuation of user behavior data assets in its annual report, its price-to-book (P/B) ratio increased from 2.3x to 3.1x. This revaluation stems from the network effects and decreasing marginal costs characteristic of data assets: once a critical mass is reached, unit costs decline exponentially while application scenarios generate linear revenue growth. Research by the Development Research Center of the State Council shows that every standard deviation increase in data asset scale raises Tobin’s Q by an average of 0.15.

However, data asset capitalization also carries value fluctuation risks. Due to the time-sensitive nature of data factors, impairment tests must be conducted more frequently than for traditional intangible assets. For instance, a logistics company’s freight flow data assets depreciated by 42% over two years due to market changes. This necessitates dynamic valuation systems and enhanced disclosure practices aligned with the Data Asset Assessment Guidelines. Currently, capital markets exhibit a 25%–30% valuation gap for data assets, reflecting both the complexity of data value realization and the need for further refinement of financial standards.

According to the Ministry of Finance’s 2023 corporate financial report analysis, listed companies implementing data asset capitalization saw an average increase of 0.3x in current ratios and a 12-percentage-point rise in R&D capitalization rates. These improvements demonstrate the unique role of data assetization in reshaping corporate value assessment systems and offer practical solutions to the “cost sedimentation problem” in market-oriented data factor reforms. As the National Data Administration promotes the development of trusted data spaces, the liquidity and tradability of data assets will further improve, shifting financial reporting mechanisms from structural optimization to value creation.

3. Business Models for Market-Driven Circulation of Data Factors
The business models enabling market-driven circulation of data factors are crucial for realizing data value. In today’s digital era, as the value of data becomes increasingly evident, exploring effective market-oriented circulation models is of great significance.

On one hand, data trading platforms provide essential support for market-driven data circulation. Numerous data exchanges operate nationwide, offering diverse types of data products, reflecting strong industry demand for external data procurement. These platforms facilitate the display and transaction of data products and services, promoting data sharing and circulation among different entities.

On the other hand, cross-organizational data circulation models continue to evolve. Some enterprises have established data-sharing alliances to enable interoperability and co-creation of value. In such models, companies can engage in targeted data exchange and collaboration based on their actual needs and available data resources, thereby enhancing data utilization efficiency and value.

Blockchain technology introduces new opportunities for business models in data transactions. As shown in Table 4.3.1 (Data on Business Models for Market-Driven Data Factor Circulation), empirical data reveal future directions and potential benefits of data assetization. The immutability of blockchain provides reliable notarization for data transactions, while dynamic pricing models make transaction prices more rational, flexible, and reflective of market supply-demand dynamics and data value. However, challenges remain: defining and protecting data rights remain challenging to define., potentially hindering transactions. Data security and privacy protection are also critical issues that must be fully addressed in business model design to ensure lawful and secure data circulation.

The International Institute for Advanced Data Management Study Limited —— abbreviated as IIADMS, is a non-profit, supplier-independent institution initiated by Mr. Hu Benli, the current chairman of DAMA China Limited, and others. IIADMS is committed to advancing research in data and data management-related fields and continuously exploring new knowledge and best practices related to data. It strives to become a world-class platform for the exchange of knowledge on data management theory and practice. IIADMS is willing to cooperate with famous forums at home and abroad in various forms to discuss traditional and frontier topics related to data management, sharing the research results of IIADMS with these forums.

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