TL;DRLast week, at the cross-border data symposium held by the China-Hong Kong Financial Elite Exchange Center in Admiralty, Hong Kong, experts from the Ministry of Commerce Research Institute, HKUST, enterprises, associations, and other elites jointly discussed cutting-edge issues related to the cross-border flow of data. I have organized and supplemented the speeches and minutes to create an article on digital assets and RWA for your reference.
In recent years, there has been extensive discussion and policy development around data elements and digital asset transactions. The establishment of the National Big Data Bureau and related local data bureau institutions signifies that data elements have become a key focus, and discussions surrounding data asset exchanges are becoming more prevalent. RuiHe Capital is primarily focused on building an RWA investment bank, closely communicating and cooperating with licensed exchanges, licensed Hong Kong financial institutions such as brokerage firms, asset management companies, trusts, and others.
RWA, or Real World Assets, represents Web 2.5, a combination of traditional real-world assets and tokenization. It merges traditional assets and finance with Web 3.0 and crypto assets, acting as a transitional and compromising intermediary state. During the symposium, President Wang of HKUST recommended Hong Kong’s RWA as an ideal state for data assets.
The promotion of data asset listing and data mortgage financing in mainland China is essentially a new financing channel for state-owned enterprises (SOEs). This is because most companies that possess large-scale personal or industrial data are SOEs. Listing and lending essentially means banks are extending credit to SOEs, involving “assets” that are largely illiquid.
Data: Trade or Transaction?
If it is trade, it implies that data is a commodity. If it is a transaction, it implies that data is an asset. The asset is not the data itself, as data cannot be directly bought or sold, especially considering data privacy protection regulations. We rarely say we buy data for the sake of buying data; often there is an indirect purpose, such as reaching consumers with certain attributes, achieving more accurate credit assessments for loans or contracts. Additionally, data can be divided into 2C (consumer) and 2B (business) data: personal data includes bank, WeChat, healthcare data, etc., while industrial data includes corporate, complete sets of parts, manufacturing, market sales inventory, and so on.
From the perspective of asset securitization, data assets are more about the financial assets derived from the revenue or cash flow generated by achieving specific purposes. Analyzing it using the ADF (Assets-Deal-Finance) industry analysis framework makes it very clear. How can data be assetized? The RWA (Real-World Asset) model is a good direction and model. The RWA model does not involve direct transactions of physical assets but rather the assetization and tokenization based on the cash flow or expected returns from underlying assets, with liquidity in the secondary market. Therefore, RWA is particularly suitable for the “transactions” of data assets.
Hong Kong has extensive policies related to RWA. Regarding data, Hong Kong has the “Policy Statement on Facilitating Data Circulation and Ensuring Data Security,” which mentions several points: first, ensuring the anonymity of accessed assets, and second, using blockchain technology to build the infrastructure for data circulation.
So, how does data become a valuable financial “asset”?
First, there are highly digitalized application scenarios. Data can achieve value through on-chain confirmation of rights and value isolation (SPV), creating a “SPV + smart contract + cash flow” data asset. For example, the core business of Vobile Group – streaming media content copyright services – is entirely online, and the cash flow and revenue distribution are digital and online, making it a typical RWA data asset.
Secondly, there are paid scenarios indirectly derived from data. Previously mentioned, data indirectly generates credit or enhances credit, such as DePIN projects that generate consensus-based data through distributed networks and bookkeeping, creating credit or credit enhancement value from a financial asset perspective, which institutions are willing to pay for. For example, the Domo project, with its BOM distributed network for automobiles, transforms personal data, driving habits, etc., into data assets valuable for personal credit and insurance pricing algorithms, with insurance companies paying for this.
Additionally, there are intermediary value scenarios for data. Experts mention data bartering. Previously, in overseas trade, there was a quota from the State-owned Assets Supervision and Administration Commission (SASAC), equivalent to a virtual large asset pool of overseas state-owned enterprises. This allowed bartering without complex and costly currency exchanges, thus reducing capital costs and improving procurement efficiency. This intermediary value, derived from detailed data and pricing algorithms, creates electronic quotas, which is another similar RWA product.
The RWA of data assets requires several steps: the first step is to package and design data assets as financial products, the second step is asset tokenization, and the third step is trading. In the future, this can further expand to tokenized cash flows and secondary financial derivatives.
For data assets in mainland China, there might be a pathway: mainland data assets, after obtaining approval, establish a VIE structure to a Hong Kong entity. The Hong Kong entity issues RWA data assets, trades, and invests in licensed exchanges in Hong Kong. Through the WFOE foreign wholly-owned enterprise structure of the VIE, it connects with mainland enterprises, forming a cycle.
Data assets are not just data itself but are part of a digital asset ecosystem: from data desensitization, labeling, and asset confirmation, to application coordination, pricing algorithms, trading, and liquidity pools. Compared to personal data, industrial data may be easier to assetize. Industrial data, often combined with Industry 4.0 digitalization, can generate industrial credit value and provide value for trade, supply chain finance, and industrial capital, making data assetization scenarios and cash flow sources richer.
The complexity of industrial data assets requires dynamic data asset pricing algorithms based on data asset pools combined with technologies such as AIGC. This ensures that different industrial chains and data can reasonably and dynamically form asset and intermediary value pricing.
Thus, the final data asset ecosystem will be rich, not only involving buyers and sellers of digital assets but also liquidity providers (LPs) of data assets, data asset incubation and investment funds, speculators, and arbitrage institutions, as well as RWA investment banking institutions for data assets.
(CHatGPT mapping)
A friend on-site asked which industries are suitable for data assets. Here, Ye Kai summarized several industries:
Cultural Content Streaming Media: The core is online streaming content, not traditional box office films, but those content smart boxes, online video platforms, overseas cultural micro-dramas, and platforms like TikTok. These streaming contents are already completely online with subscription and recharge payments.New Energy Distributed Networks: China’s capacity for solar storage accounts for 80% of the global total, but it mainly focuses on hardware, with a relatively weak software side. The market potential for green electricity data assets generated by fully marketized distributed network devices is enormous. We should avoid a situation where hardware manufacturing capacity is in China, but the asset confirmation, pricing, and financial transactions of the software are in the US and Europe.Computing Power: Principal Wang mentioned that AI computing power mainly involves data calculation and processing. We are currently the largest purchaser of AI computing power, with both centralized training of large models and a significant demand for inference and rendering in various application scenarios. These can form effective AI computing power data assets based on the scale of procurement needs.Healthcare and Wellness: With the spread of digitization and electronic prescriptions, diagnostic and nursing smart wearable devices, and chronic disease smart devices, a distributed network of data assets is formed. These can be combined with personal health assets and service institution assets.Industries with High Levels of Industry 4.0 Integration: Such as smart home appliances, smartphones, and intelligent robots. These deeply integrated data with households, individuals, and specific application scenarios can also be designed into valuable data assets.
In summary, data assets are very suitable for RWA. Data asset RWA can achieve digitization, securitization, and globalization of data.
Finally, we are actively working with leading institutions and platforms in the Web3.0 and RWA fields to create a series of professional investment banking services for RWA, providing diversified encrypted financing services for high-quality assets and entrepreneurs. We welcome all interested parties to join in the construction. You can add WeChat YekaiMeta to join the RWA practice discussion group and participate in discussions.
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