jakiro
Source: Ye Kaiwen
Last week, a cross-border data symposium on cross-border data flow at the Hong Kong Admiralty China-Hong Kong Financial Elite Exchange Center, many elites from the Ministry of Commerce Research Institute, Hong Kong University of Science and Technology, enterprises, associations and other elites jointly discussed cutting-edge issues such as cross-border data flow.I have compiled the speeches in combination with the minutes and added them to digital assets and RWA articles for reference.
In the past two years, there have been many policies and discussions on data elements and digital asset transactions. The establishment of the National Big Data Administration and relevant data bureaus in various places means that data elements have become an important direction, and discussions on data asset exchanges have also begun to increase.RuiHe Capital mainly focuses on the establishment of RWA investment banks, and is closely communicating and cooperating with licensed exchanges and licensed Hong Kong financial institutions such as securities companies, asset management, trusts, etc.RWA is Web2.5, a combination of traditional real-world assets and tokenization, and a fusion of traditional assets and finance with Web3.0 and crypto assets. It is equivalent to a transitional and compromise intermediate state.The Hong Kong RWA recommended by President Wang of Hong Kong University of Science and Technology in the symposium is very suitable for data assets.
Data asset entry and data mortgage financing promoted in the Mainland is basically a new financing channel for state-owned enterprises, because most companies with large-scale personal data or industrial data are state-owned enterprises, with entry and loans, it is nothing more than banks that lend money to state-owned enterprises, which are basically “assets” without liquidity.
Data, is it trade or transaction?If it is trade, it means data is a commodity, if it is a transaction, it means data assets.Assets are not the data itself, and data cannot be bought and sold directly, especially when it comes to data privacy protection regulations. We rarely say that in order to buy data, we often have indirect purposes, such as to reach consumers with certain attributes.To have a more accurate credit assessment, loan or contract.Moreover, these data are divided into 2C and 2B data: personal data, including data such as banking, WeChat, medical and health care, and industrial data, including data such as enterprises, complete sets of accessories, production and manufacturing, market sales and inventory, etc.
From the perspective of asset securitization, data assets are more of financial assets based on the benefits or cash flow caused by this purpose..If you analyze it from the ADF industry analysis framework (ADF: Assets-Transactions-Financial), it will be very clear.
How to asset data?RWA is a good direction and mode.The RWA model is not a direct transaction of real-world physical assets, but is based on the cash flow or expected returns brought by the underlying assets, and has the liquidity of the secondary market.Therefore, RWA is a “transaction” that is particularly suitable for data assets.
Hong Kong RWA has introduced a lot of relevant policies. Regarding data, Hong Kong has the “Hong Kong Policy Declaration on Promoting the Circulation of Data and Ensuring Data Security”, which mentions several points: First, ensure anonymous letters for accessing assets, and secondIt is an infrastructure for building data circulation using blockchain technology.
So how can data become a valuable financial “asset”?
First of all, it is an application scenario with a relatively high degree of digitalization.Data is a data asset that can realize value on-chain rights confirmation and value isolation (SPV) and realizes a “SPV + smart contract + cash flow”.For example, Fubo Group’s core business – streaming content copyright service, because streaming content is completely online, and the cash flow and income distribution are also digital and online, this can be designed to become a typical RWA data asset.
Secondly, payment scenarios derived from data indirectly.The data mentioned above indirectly generates credit or enhances credit enhancement. For example, if there are consensus data generated by DePIN projects based on distributed networks and accounting, they generate credit or enhancement value from the perspective of financial assets, and institutions are willing to pay.For example, the Domo project, the automobile’s BOM distributed network converts personal related data, driving habits, etc. into data assets that are valuable to personal credit and insurance pricing algorithms, and insurance companies pay.
Also, the intermediary value scenario of data.Experts mentioned the barter of data. In fact, in overseas trade, there was a quota of state-owned enterprises of the State-owned Assets Supervision and Administration Commission, which was equivalent to the virtual large asset pool of overseas state-owned enterprises. You can go out to purchase without complicated and expensive remittances.Direct bartering reduces capital costs and improves procurement efficiency.This intermediary value comes from the electronic quota generated by detailed data and pricing algorithms, which is also a similar RWA product.
The RWA of data assets requires several steps:
The first step is to design data assets as financial products, the second step is to tokenization of assets, and the third step is to trade. In the future, we can continue to expand tokenized cash flow and second layer of financial derivatives.
For mainland data assets, there may be a path like this:
Mainland data assets, obtain the VIE structure to establish a VIE structure to the Hong Kong entity, and the Hong Kong entity issues data assets RWA, trade and invest on Hong Kong licensed exchanges, and connect with mainland enterprises through the WFOE structure of the VIE structure, forming a cycle.
Data assets are not just the data itself, but aDigital Asset Ecology: From data desensitization, labeling and asset rights confirmation, application coordination, pricing algorithms, transactions and liquidity pools, etc.Compared with personal data, industrial data may be easier to be assetized.Because industrial data, often combined with the digitalization degree of Industry 4.0 of this industry, can not only generate industrial credit value, but also provide value for trade, supply chain finance, and industrial capital. Therefore, the scenarios of data assetization and cash flow sources will be moreRich.
The complexity of industrial data assets requires the implementation of a dynamic data asset pricing algorithm based on the data asset pool and combined with AIGC and other technologies, so that different industrial chains and industrial data can be realized, and the assets can be formed dynamically.and the value pricing of the intermediary.
In this way, the final data asset ecosystem will be very rich. Not only buyers and sellers of digital asset transactions, but also LPs that provide data asset liquidity, funds that incubate and invest in data asset, and speculators and arbitrage.Institutions, RWA investment banking institutions of data assets, etc.
A friend asked on the spot which industries are suitable for data assets?Here Ye Kai summarizes several industries:
1)Cultural content streaming, the core is online streaming content, not traditional film and television box office, but content smart boxes, online video platforms, cultural short dramas and Tiktok, etc. These streaming content are completely online subscription and recharge payment;
2)New energy photo storage full-distribution networkChina’s optical storage and charging capacity accounts for 80% of the world, but it is mainly hardware, which is relatively weak in software. The green electricity data asset space generated by fully market-oriented distributed network equipment is very large, so the hardware manufacturing capacity is not the same asChina and Soft’s asset rights confirmation, pricing and transaction finance are in the United States and Europe;
3) Principal Wang mentionedAI computing power, AI computing power is mainly used to calculate and process data. We are currently the largest purchasing country for AI computing power. We have both centralized training of large models and the inference and rendering requirements for a large number of application scenarios. These can be based on the scale of procurement requirements., form effective AI computing power data assets;
4)Medical care and health, with the popularization of digitalization and electronic prescriptions, smart wearable devices for diagnosis and care, smart devices for chronic diseases, etc., data assets generated by distributed networks can be combined with personal health assets and service institutions’ assets;
5)Manufacturing industry with a high degree of industrialization, such as smart home appliances, mobile phones, smart robots, etc., these data that are deeply integrated with family individuals and specific application scenarios can also be combined and designed into valuable data assets.
To sum up, data assets are very suitable for RWA, and data assets RWA can realize the digitization, securitization and globalization of data.
