“Data is the oil of the new era” – since mathematician Clive Humby put forward this view in 2006, the value creation mechanism of data elements has gradually completed the three-stage evolution of resource precipitation, asset transformation and capitalization. The three-stage evolution of resourceization precipitation, assetization transformation and capitalization leap. In the digital economy and society, data assets not only become the core resources of enterprises, but also serve as the engine driving business model innovation and value creation, while realizing cross-domain circulation for the form of basic social resources.

Prof. Hongwei Wang, Associate Dean of Tongji-SEM ,is committed to the research of data assetization, and recently he was interviewed by Harvard Business Review. According to his judgment, “data in the table” is experiencing the leap from technical concept to strategic practice, which not only reshapes the new paradigm of asset management, but also promotes the capital market to reconstruct the enterprise valuation system and stimulates the collaborative innovation vitality of the industrial chain, and finally gives rise to the Metcalfe effect of the data factor market. This process will not only reshape the new paradigm of asset management, but also promote the reconstruction of enterprise valuation system in the capital market through data value mining, and then stimulate the collaborative innovation vitality of the industrial chain, which will ultimately give rise to the Metcalf effect of the data factor market.

Data into the table and data assetization

Data entry refers to quantifying the value of enterprise data resources through systematic methods and incorporating them into the financial statement system as a new type of asset. This process not only marks the completion of the institutional leap from production resources to legal assets, but also highlights its strategic positioning as a core production factor in the digital economy. Special attention should be paid to the fact that there are essential differences between data assets and traditional assets: its intangible carrier characteristics bring non-exclusive reuse advantages, so that a single data can support the parallel development of multiple business scenarios; its value dynamic characteristics manifested in the sensitivity of time and scenario dependence, resulting in asset valuation to follow the law of dynamic evolution.

As a systematic project, data assetization runs through the whole life cycle of data management and relies on the synergy of multiple links. The implementation framework includes six steps: data collection and cleansing, standardized governance, tenure definition, value assessment, financial inclusion and market circulation. Among them, value assessment, as the core link, has formed three major methodology systems, namely the present value of earnings method, the replacement cost method and the market comparison method, which are suitable for different business scenarios. Enterprises implementing data into the table need to build a dual protection mechanism: the front-end to establish a legal compliance framework for data rights, and the back-end to form a consensus mechanism for asset pricing recognized by multiple parties. Wang Hongwei emphasized that the identification of data assetization potential needs to establish three-dimensional assessment standards: firstly, the basis of legal rights, requiring data ownership relationship is clear and in line with data security regulations and privacy protection framework; secondly, the value attribute dimension, need to verify whether the data has the adaptability of the business scenarios, cross-domain scalability and value of multi-cycle reuse; and lastly, the dimension of data quality, requiring the data assets to meet the high level of accuracy, completeness and timeliness of the core indicators, Lastly, there is the data quality dimension, which requires data assets to meet high standards in terms of accuracy, completeness, timeliness and other core indicators.

Opportunities and Challenges

Under the triple effect of “technological change, institutional innovation and market drive”, the global data assetization process is restructuring the competition pattern of digital economy, accelerating the formation of “data sovereign economies” and “data dependent economies”. accelerating the formation of a new international stratification of “data sovereign economies” and “data dependent economies”. Relying on the institutional advantages of the national system, China has actively promoted the “China Solution” for the marketization of data elements, and gradually established a double competitive advantage in the scale of data storage and the growth rate of data transactions through the synergistic breakthroughs in policy innovation, arithmetic infrastructure and scenario application.

In 2019, China put forward the strategic positioning of “data as a factor of production to participate in the distribution”, and then issued the “Opinions on building a more perfect institutional mechanism for the market-oriented allocation of factors”, which included data factors in the sequence of the country’s basic strategic resources. Institutional innovation has given rise to practical breakthroughs, and organizations such as the Shanghai Data Exchange and Guiyang Big Data Exchange have constructed a full-chain service platform covering data registration, evaluation, and trading, and combined with clusters of professional data service providers, initially forming an ecological closed loop of “policy-platform-service”.

However, the development of data assetization still faces structural challenges. Wang Hongwei analyzes the four major bottlenecks in China’s data factor marketization process:

First, the governance system is not sound. The lag in data standardization, the difficulty of integrating heterogeneous systems, and the lack of quality control mechanisms seriously constrain the efficiency of data element circulation.

Second, there are double constraints on the confirmation mechanism. The legal level has not yet established a clear framework for data rights confirmation, which makes it difficult to clearly define data ownership, use and revenue. At the technical level, technical solutions such as blockchain depository have low popularity in the application of rights confirmation technology due to high deployment costs.

Third, the capacity of small and medium-sized enterprises (SMEs) is disconnected. Restricted by the lack of data resources, weak technical reserves and shortage of professionals, more than 70% of SMEs have insufficient knowledge of data assetization and are at the periphery of the data value chain.

Fourth, the ecosystem is not yet mature. Head enterprises hold the pricing power by virtue of data monopoly, which hinders the consensus on data assetization; there is a policy cognitive mismatch between the regulatory orientation of government departments and the practice of market players; although the auditing and taxation fields are exploring the criteria for data assetization, there is still a lack of cross-departmental coordination mechanism in the core areas of accounting recognition and tax treatment.

Consumer side first

Although data assetization is still in the exploratory stage, Wang Hongwei believes that its advancement will restructure business operations, industry patterns and even social ecosystems. Enterprises need to be forward-looking in their data assetization strategies to seize market opportunities and avoid losing competitive advantages. The government should cultivate new business models through pilot demonstration projects. In this process, enterprises should establish a dynamic risk-return assessment model to realize capital market value enhancement.

Over the past two decades, China’s Internet industry has shown a structural imbalance of “prosperity at the consumer end and lagging behind at the industrial end”, which is rooted in the difference in the difficulty of digitalization penetration in different fields. Relying on the natural advantage of “light assets – strong feedback – fast iteration”, consumer Internet has formed an enhanced circuit of “data factor input → business model innovation → capital market premium”, resulting in remarkable development results; while industrial Internet is constrained by the “heavy assets – long cycle” and the “long cycle” of the “heavy assets – long cycle”. The industrial Internet is constrained by the transformation dilemma of “heavy assets – long cycle – high threshold”, superimposed on the heterogeneity of industrial equipment, data siloing and other practical constraints, and is still in the gradual transformation period of “equipment digitization -> data resourcing -> scenario assetization”. Based on the evolutionary law of innovation diffusion, Wang Hongwei predicts that China’s data assetization will continue the dual-track path of “breakthroughs in the consumer side to lead → gradual penetration of the industrial side”: the consumer side will continue to emerge as a reference case, and the industrial side will steadily advance in the scenes of intelligent manufacturing, supply chain finance, and so on.

Consumer Internet enterprises have typical data-native qualities, and the essence of their business model is the commercialization of data value: online car platforms replace traditional vehicle assets through spatial and temporal data and dynamic pricing algorithms; e-commerce enterprises realize inventory-free operation by virtue of user profiles and demand forecasts; online travel service platforms reconstruct the accommodation service system on the basis of behavioral trajectory data. The data-driven attributes of these enterprises naturally fit the demand for assetization.

In particular, Wang Hongwei reminded that consumer enterprises should build a dual protection system of “legal compliance and technical protection”: not only should they meet the compliance requirements of the Data Security Law and the Personal Information Protection Law, but they should also introduce technological innovations, such as blockchain and privacy computing, as a paradigm for data security management.

Steady development of the industry

Due to the high technical complexity and large investment cost of data assetization on the industrial end, enterprises generally adopt a prudent promotion strategy. According to Wang Hongwei, releasing the value of industrial data assets requires breaking through the traditional thinking framework and building customized solutions and business models around vertical scenarios. The research team has observed the innovative practices of several benchmark cases in the field research:

In the field of industrial Internet, a technology enterprise in Tongxiang, Zhejiang Province, has developed an industrial data assetization system, which collects the whole process data of the production line in real time through sensors and synchronizes it to the central management platform, and dynamically optimizes the configuration of the equipment parameters with the help of machine learning, which significantly improves the quality of products.

In the field of public data application, Nanjing Yangzi Guotou has developed water data asset assessment model, cleaned and modeled the water consumption data of more than 4,000 enterprises, which has become a model for the practice of domestic public utility data into the table, and built a smart water management system on the basis of which to achieve significant improvement in the efficiency of the early warning of leakage of the pipeline network.

In the field of financial innovation, China Enterprise Cloud Chain, a professional supply chain financial platform under China Motor Bus Group, relies on the Group’s strong financial strength and credit endorsement to build a tamper-proof and traceable trade data deposit system through blockchain. After the platform is docked with the credit system of banks, the upstream suppliers can realize the authentication of accounts receivable in seconds with the electronic contract and logistics data, which has provided hundreds of billions of yuan of liquidity support for SMEs in total.

Future Outlook: Building a Cross-Industry and Multi-Dimensional Data Asset Ecology

Data assetization, as a systematic innovation of basic resources for digital economy, is promoting the migration of intangible data resources to standardization and capitalization, and restructuring the enterprise value system and global competition rules. Looking ahead, Wang Hongwei believes that the data factor market will launch a deep evolution in three aspects.

The first task is to build a cross-industry data fusion platform. When the value mining of data in a single field hits the ceiling, cross-border data combinations such as medical+financial, transportation+logistics, and industrial+insurance will give rise to exponential value fission. This requires enterprises to establish a quantitative model of data contribution and explore the data sharing revenue sharing system, and also requires the government to establish an innovative management mechanism of “negative list + sandbox regulation” to release the vitality of elements under the premise of safeguarding data sovereignty.

The core battlefield focuses on privacy computing technology breakthroughs. Enterprises rely on new technologies to build a perfect data governance system: based on the blockchain to achieve ownership certification, through the federal learning framework to achieve “data available invisible”, the use of homomorphic encryption technology to achieve “data immobility value movement”, and with the help of synthetic data technology to break through the sample bottleneck. A commercial bank has realized “blind interaction” with an e-commerce platform in user profile modeling, and improved the accuracy of risk control by 37% under the compliance framework.

Institutional safeguards urgently need policy innovation and iteration. It is recommended to promote in phases: in the short term, introduce operational guidelines for data asset accounting standards, and clarify the rules for depreciation and impairment testing; in the medium term, establish a “classification and grading” management system for cross-border data flows, and benchmark CPTPP digital trade provisions; in the long term, explore the pilot of data asset securitization, and build valuation and pricing centers in the Yangtze River Delta and other regions first.

The wave of change set off by data entry and data assetization can no longer be ignored. It not only manifests the invisible value of enterprises and enhances the attractiveness of enterprise financing; it also promotes deep changes in enterprise operation and management, strengthens cross-departmental collaboration and resource integration, and provides a new impetus for business innovation; more importantly, it promotes the data-driven decision-making paradigm, gives rise to new business models, accelerates the intelligent transformation of enterprises, and reshapes the value of enterprises. Although the game of data sovereignty and ethical risks are like the sword of Damocles hanging high, those enterprises that take the lead in accomplishing the “data resource->data asset->data capital” triple jump will surely win the strategic high ground in the era of digital economy.

Translated with DeepL.com (free version)