An In-depth Discussion on the Process of Data Asset Recognition and Data Value Enhancement

The Full Lifecycle of Data Value Realization

——An Interpretation of Data Asset Recognition and Value Enhancement Processes

Live Stream Review - January 6, 2024

On the evening of January 6, 2024, Dr. Wu Dayou, Professor Peng Guochao, and Mr. Ma Huan jointly conducted an online live stream sharing session. Professor Peng Guochao centered his presentation around the full cycle of data value realization, discussing practical issues and opportunities associated with data asset recognition; Dr. Wu Dayou, in conjunction with the latest policies, provided comprehensive insights into personal data application, paths for data asset recognition, and how to enhance data values, addressing questions from the audience; Mr. Ma Huan, from a professional data governance perspective, explained that data governance is a prerequisite for data asset recognition and an essential part of data value enhancement that cannot be overlooked.

The live stream bridged policy with core data concepts, focusing on the process of data asset recognition and the more critical process of data value enhancement behind it. The recognition of data assets is complementary to the comprehensive digital transformation of enterprises.It is hoped that this live stream will provide valuable perspectives and deepen understanding of the processes of data asset recognition and value enhancement.

Dr. Peng Guochao: In our current world, there are various domains rich in data, all of which can be projected into virtual space, truly demonstrating that everything can be quantified. With the proposal of the "Data Element x" initiative by the National Data Administration, the question of how to amplify data value has attracted significant attention from the industry. I would like to share some thoughts on the entire cycle of data value realization.

The evolution of data can be divided into four stages: raw data, data resources, data assets, and data capital. This is also a viewpoint presented in the book "Data Trading: A Guide to the Full Cycle of Data Value Realization," co-authored by me and Dr. Wu. Each transition between two stages corresponds to a process of data value realization.

Focusing today on data assets, we can compare their definition to that of general assets. However, data assets have their uniqueness. Traditional physical assets depreciate over time and use, and their ownership is clear. In contrast, data is intangible and can be reused by multiple entities simultaneously, with its value potentially increasing through processing. Issues regarding data ownership are much more complex. Speaking of data ownership, one naturally thinks of the tripartite mechanism of data rights outlined in the Data Law's 20 provisions, including data resource holding rights, data processing usage rights, and data product operating rights. This mechanism allows different entities to better develop and utilize data repeatedly on the same data carrier, effectively releasing its value.

Data valuation and pricing is another crucial step. Some companies equate valuation with pricing, which is a common misunderstanding.While we hope that data transactions will create higher value and command higher prices, the actual value of data does not solely reside in thedata itself but rather in its potential for development and utilization. Even the same set of data can generate vastly different economic values and market effects in different scenarios. Moreover, the integration of diverse data can influence data value, often creating a chemical effect greater than the sum of its parts, though negative effects are possible as well. Challenges abound in data valuation and pricing. When valuing data in practice, cost approach, income approach, and market approach are commonly discussed methods, with numerous case studies available for reference, such as Everbright Bank and Hengfeng Bank. Now, the most advocated method is to base valuation on the cost approach, integrating factors such as data classification, data quality, data costs, and potential data usage value for fair valuation. However, algorithms and indicators can only serve as references, and specific enterprises should conduct specific analyses.

Q: How can personal data be traded? Is there any connection with business scenarios?

Dr. Wu Dayou: Let us first interpret the "Data Element x" concept recently proposed by the National Data Administration. Reflecting back on the "Internet+" concept introduced in 2018, both advocate the combination of two different fields. During the "Internet+" era, we spoke about adding data to a certain product or application to become internetized, while "Data Element x" refers to leveraging multiple scenarios to multiply the realization of data value. All policies start from public scenarios and gradually deepen to concrete personal scenarios. For instance,data asset recognition currently leans more towards public data or state-owned enterprise applications, but later on, it will likely shift more towards personal scenarios and personal applications.

In fact, some overseas countries have already used blockchain concepts to categorize and bundle personal data, charging based on usage frequency. However, China has yet to reach this level. Rather, businesses must take the lead before delving into personal data.

Businesses face the issue of "insufficient data volume." The degree of digitization within a company affects its ability to recognize assets. If asset recognition benefits a company, would it then need continuous recognition? Would it require continuous inventory of data and intangibleassets? However, traditional companies have limited capabilities for data accumulation, making it difficult to continuously complete the path ofasset recognition. Therefore, when observing policies, one finds that the country not only discusses the digital economy but also enterprise digital transformation.

For small and medium-sized enterprises to recognize assets, they need digital capabilities to accumulate digital assets, find appropriate scenarios for projection, and activate the market. Personal data assets will inevitably flow freely in the future.

Q: Will the digital economy replace the real estate economy in the future?

Dr. Wu Dayou: Although China leads globally in the digital economy, it still occupies a relatively low percentage of total GDP. If it continues to develop and flourish in the future, there is certainly an opportunity for it to appropriately fill the gap left by land economics and land assets. However, completely replacing them may still be a long way off.

Q: What is the method for including digital assets in financial statements?

Dr. Wu Dayou: Companies looking to include digital assets in their financial statements must first complete data governance, ensuring that data is standardized. Second, the data must be legally compliant and entitled; in China, only qualified law firms or authorized entities can conduct compliance checks and entitlements. Third, data valuation needs to be performed by state-designated professional institutions, so it's important to identify certified organizations.

Q: How does including data assets in financial statements amplify their value?

Dr. Wu Dayou: Amplifying the value of data assets hinges on whether the business model of the company changes. Do not overestimate the act of inclusion; it is merely an accounting procedure. The cost and value of intangible assets, after estimation, will be reflected as is in the financial statements. Why do we say that including data assets in financial statements amplifies their value? It's because these costs were previously not capitalized but expensed, not recognized as assets. If a company's operations are data-driven and previous investments in data were not capitalized, now these overlooked capitals can be recovered. For traditional companies without inherent data assets, increasing data value is irrelevant; what they need is a data strategy. They should consider how to transform their existing business models to acquire data and form their own data assets.

Q: Is there still a need to include data assets in financial statements if a company doesn't require bank loans?

Dr. Wu Dayou: If you have included assets in your books, and your data has been made compliant, entitled, and valued, then even without seeking bank loans, you can obtain credit ratings. How much is this data really worth? Who decides? It's simple; the person giving you money decides. If I have included my assets in the financial statements and take them to the bank for valuation, they give me a line of credit and tell me my data is worth 5 million RMB—that's its true value. If I've only completed the valuation but the bank does not extend credit, I cannot prove my data is worth that amount. Therefore, inclusion in financial statements, bank credit lines, etc., are all critical to proving the value of data. If my data is proven to be worth 5 million RMB, when I develop new products using this data, their price will inevitably be multiplied based on this foundation, and this price will be certified. From this perspective, including data assets in financial statements is something businesses should do in the future or can do now. However, for small and medium-sized enterprises (SMEs), they must first transform their data business models and accumulate data assets before they can become products. For large, data-centric companies, they already have the foundation to include data assets in their financial statements.

Q: What are the difficulties for companies in including data assets in financial statements? What should be considered from a data perspective?

Mr. Ma Huan: Many companies ask if having a lot of data accumulation means they can include it in their financial statements. Actually, it's notthat simple. The difficulty lies in being able to determine data through some means, including the source of the data, the costs incurred in generating it, etc. This is typically part of the data governance process. Without a comprehensive governance process, many procedures and systems remain unclear, making the entitlement of data assets challenging. Secondly, estimating data costs is also difficult—how to transform it into a valuable asset is akin to the process of flesh falling off bone. For a company with good data governance, including data assets in financial statements is a natural progression.

Q: How can data strategies be applied? What are their functions and significance?

Dr. Peng Guochao: Everyone is now very focused on including data assets in financial statements, but the most critical thing for companies is to integrate their underlying business processes. Whether it's data entry into financial statements or the creation of data products, they are ultimately deeply tied to the digital transformation of the enterprise—a mutually reinforcing process. As Dr. Wu mentioned, our research on data strategies helps companies further drive their digital transformations and assists them in acquiring the data they need.

In my research on data strategies, I've noticed an interesting cognitive bias among many companies. They think business strategy is one thing, digital transformation is another, and then there's the inclusion of data assets in financial statements and data product transactions, which are all separate things. In our data strategy research, Dr. Wu and I have a core viewpoint that all these steps are interconnected and complementary. We emphasize the full lifecycle of data value release, with the critical point being consideration from the perspective of data strategy research.

Q: What entities are responsible for data entitlement and valuation?

Mr. Ma Huan: For data entitlement, companies usually engage law firms, where professional lawyers assess whether the collection and processing of data comply with relevant laws. If they meet the requirements, the lawyer will issue a certification, which is the entitlement. Pricing is similar; the costs incurred during data production are crucial. Original records of costs incurred in the production of enterprise data should be kept, and accountants from accounting firms or relevant consultants should be engaged to evaluate these costs and present them in a standard format.

Q: Can blockchain be used for data entitlement?

Mr. Ma Huan: Firstly, blockchain is a technology, and there are various types of chains in practical applications. When corporate data goes ontothe blockchain, what chain does it go onto? That's a question. Secondly, after data is placed on the blockchain, it clearly indicates the ownershiprelationship of the data, but there's still an issue with many black-box processes before data goes on-chain. Blockchain cannot completely cover the entire lifecycle of data; it's a tool that cannot entirely replace human assessment of ownership rights. It has natural advantages in the solidification of evidence, but it isn't a 100% substitute for entitlement.

Q: Are there more books, materials, forums, etc., about including data assets in financial statements for learning and reference?

Mr. Ma Huan: If you want to learn about topics related to data assets, the foundation is data governance. The most famous book on the subject is DMBOK, which is virtually a must-read for every data management professional. Of course, DAMA members have written many practical textbooks on the topic. Recently, Mr. Hu Benli, chairman of DAMA China, recommended and led us in translating a book called "The Data Literacy Handbook," an enlightening book that can help data managers expand their thinking.

January 18-20, 2024, the second session of the Data Asset Capitalization course will commence in Shanghai.

This course directly addresses hot topics such as the data element market and data assets:

How to transform data resources into data assets?

What significant impacts does data asset capitalization have on enterprises?

How can companies build a data asset management system?

And the influence of laws and regulations on data governance, security, entitlement, and trading...

Direct instruction from policy-makers allows for face-to-face discussions with experts on the transformation of data into assets.