Are data assets suitable for RWA?

Original title: "Are data assets suitable for RWA?"

Original author: Ye Kai (WeChat/Twitter: YekaiMeta)

Last week, at the Data Cross-border Symposium at the Hong Kong-China Financial Elite Exchange Center in Admiralty, Hong Kong, many elites from the Ministry of Commerce Research Institute, Hong Kong University of Science and Technology, enterprises, associations, etc. discussed cutting-edge issues such as cross-border data flows. I combined the speech with the minutes and supplemented it into an article on digital assets and RWA for reference.

In the past two years, there have been many policies and discussions around data elements and digital asset transactions. The establishment of the National Big Data Bureau and relevant data bureaus in various places means that data elements have become an important direction, and discussions around data asset exchanges have also begun to increase. RuiHe Capital mainly focuses on the establishment of RWA investment banks, and is in close communication and cooperation with licensed exchanges and licensed Hong Kong-funded financial institutions such as securities companies, asset management, and trusts. 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 compromising intermediate state. At the symposium, President Wang of the Hong Kong University of Science and Technology recommended the Hong Kong RWA, which is very suitable for data assets.

The inclusion of data assets and data mortgage financing promoted in the mainland are basically a new financing channel for state-owned enterprises, because most companies with large-scale personal data or industrial data are state-owned enterprises. The inclusion of data assets and loans are nothing more than banks releasing money to state-owned enterprises, which are basically "assets" with no liquidity.

Data, is it trade or transaction? If it is trade, it means that data is a commodity, and if it is a transaction, it means data assets. Assets are not data itself, and data cannot be bought and sold directly, especially when it comes to data privacy protection regulations; we generally rarely say what data I buy, and often have indirect purposes, such as reaching consumers with certain attributes, in order to have a more accurate credit assessment for loans or contracts. Moreover, these data are divided into 2C and 2B data: personal data, such as bank, WeChat, medical health and other data; industrial data, such as enterprise, complete set of accessories, production and manufacturing, market sales inventory and other data.

From the perspective of asset securitization, data assets are more financial assets based on the income or cash flow brought by this purpose. If analyzed from the ADF industry analysis framework (ADF: Asset-Transaction-Finance), it is very clear. How to assetize data? RWA is a good direction and model. The RWA model is not a direct transaction of physical assets in the real world, but is based on the cash flow or expected income brought by the underlying assets, and is assetized and tokenized, and has liquidity in the secondary market. Therefore, RWA is particularly suitable for "trading" of data assets.

There are many introductions to the relevant policies of Hong Kong RWA. Regarding data, Hong Kong has the "Hong Kong Policy Declaration on Promoting Data Flow and Ensuring Data Security", which mentions several points: one is to ensure the anonymity of access assets, and the other is to use Blockchain technology to build the infrastructure for data flow.

How can data become a valuable financial "asset"?

First, it is an application scenario with a high degree of digitization. Data is a data asset that can realize the on-chain confirmation and value isolation (SPV) of value, and realize a "SPV+smart contract+cash flow" data asset. For example, Fubo Group's core business - streaming content copyright service, because the streaming content is completely online, and the cash gift and income distribution are also digital and online, this can be designed as a typical RWA data asset.

Second, the payment scenario indirectly derived from data. The data mentioned above indirectly generates credit or enhances credit. For example, the DePIN project generates consensus data based on distributed networks and accounting, which generates credit or credit enhancement value from the perspective of financial assets, and institutions are willing to pay. For example, the Domo project, a distributed network of BOM for automobiles, converts personal data, driving habits, etc. into data assets that are valuable to personal credit and insurance pricing algorithms, and insurance companies pay.

In addition, there are intermediary value scenarios for data. Experts mentioned the bartering of data. In fact, there was a state-owned enterprise quota of the State-owned Assets Supervision and Administration Commission in overseas trade before, which is equivalent to a virtual large asset pool of overseas state-owned enterprises. Instead of complex and expensive foreign exchange settlement and then going out for procurement, it can be directly bartered, which 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 package data assets into financial products, the second step is asset tokenization, and the third step is trading. In the future, tokenized cash flows and second-layer financial derivatives can be further expanded.

For data assets in the Mainland, there may be such a path: data assets in the Mainland, obtain a road permit to set up a VIE structure to a Hong Kong entity, the Hong Kong entity issues data asset RWA, trades and invests on a licensed exchange in Hong Kong, and forms a cycle through the WFOE wholly foreign-owned enterprise in the VIE structure and is associated with Mainland enterprises.

Data assets are not just data itself, but a digital asset ecosystem: from data desensitization, labeling and asset confirmation, application coordination, pricing algorithms, transactions and liquidity pools, etc. Compared with personal data, industrial data may be easier to assetize. Because industrial data, often combined with the digitalization level of Industry 4.0 of the industry, can not only generate industrial credit value, but also provide value for trade, supply chain finance and industrial capital, so the scenarios and cash flow sources of data assetization will be richer.

The complexity of industrial data assets requires the implementation of a dynamic data asset pricing algorithm based on the data asset pool and in combination with technologies such as AIGC, so that different industrial chains and industrial data can reasonably and dynamically form the value pricing of assets and intermediaries.

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 assets, speculators and arbitrage institutions, RWA investment banks for data assets, and so on.

(Chart by CHatGPT)

Some friends asked which industries are suitable for data assets? Ye Kai summarized several industries here: 1) Cultural content streaming media, the core is online streaming content, not the traditional film and television box office, but those content smart boxes, online video platforms, short dramas of cultural exports and Tiktok, etc., the streaming content of these is already completely online subscription recharge payment; 2) New energy photovoltaic storage and charging distributed network, China's photovoltaic storage and charging capacity accounts for 80% of the world, but it is mainly hardware, and is relatively weak in software. The green electricity data asset space generated by fully marketized distributed network equipment is very large. Don't let the hardware manufacturing capacity be in China, and the soft asset confirmation pricing transaction finance is in the United States and Europe; 3) The computing power mentioned by President Wang, AI computing power is mainly in computing and processing data. We are currently the largest purchasing country for AI computing power. We have both centralized training of large models and reasoning and rendering needs for a large number of application scenarios. These can be based on the scale of procurement needs to form an 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., the data assets generated by the distributed network can be combined with personal health assets and service agency assets;

5) Manufacturing industries with a high degree of industrial 4.0, 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.

Finally, we are actively building a series of services for RWA professional investment banks in conjunction with the leading institutions and platforms in the fields of Web3.0 and RWA, providing diversified encryption financing services for high-quality assets and entrepreneurs. We welcome people with lofty ideals to participate in the construction, and you can also add WeChat YekaiMeta to join the RWA practice seminar group to participate in the discussion.

This article is from a contribution and does not represent the views of BlockBeats.

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