Reading|"Data Asset Theory": How to capitalize data?

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Professional knowledge sharer in the fields of blockchain, privacy computing and financial technology.

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Since the author is engaged in technical research in the field of blockchain and privacy computing, and also deals with application scenarios in daily work, the author is concerned about issues such as data confirmation, pricing, capitalization, circulation logic technology, and law. Also maintain curiosity and attention.

Today I accidentally found _ a book in the field of data assets - "Data Asset Theory" _ author: Wang Hansheng, professor of Guanghua School of Management, Peking University.

So decided to find out.

reading ideas

According to the author's personal reading habits, I first browsed the catalog roughly. Chapters 1 to 7 are Professor Wang's views on the field of statistics and data analysis, and chapters 8 to 10 are about "data elements" in determining rights and pricing. , capitalization, logic, technology, law and other issues in circulation.

At first, I read the ratings and book reviews of Douban and WeChat reading, and the evaluation was average, but after all, this book was written by a professor from Guanghua, Peking University, so I must still have some insights.

After clarifying that my goal of reading this book is to understand the knowledge in the field of "data assetization", I decided to read this book quickly.

I searched the industry's interviews with Professor Wang and his own public speeches to quickly understand the ideas of this book.

I mainly read the following two articles:

1. Issue 41 Data Asset Theory

(https://www.gsm.pku.edu.cn/thought_leadership/info/1007/1276_1.htm)

2. Interview with the author | "Data Asset Theory" Wang Hansheng

(https://m.sohu.com/a/325254384_500658)

Viewpoint 1: Definition of data assets

First of all, I think the following two questions need to be clarified, and the definition of "data assets" can naturally be understood.

Q1: What is data?

A1: Electronic records are both.

Q2: What is an asset?

A2: According to the definition of accounting, resources that can generate expected economic benefits are called assets.

Since the definition of assets in accounting is "resources that can generate expected economic benefits", and data generally has a certain value and can generate expected benefits, data is essentially an asset.

Since data is an asset, how is the price of data assets defined?

Modern finance has a basic model: the capital asset pricing model (capital asset pricing model)

The production cost of any asset is not necessarily related to its corresponding commercial price, and the price is driven by the value it creates.

So the price of a data asset is driven by the value it creates.

Point 2: Data Analyzability

The author personally understands that the analyzability of data can only be reflected in the following two situations. One is when the amount of data reaches a certain level; the other is when this set of data is highly related to the predicted problem.

Good data analyzability must be continuously optimized to keep approaching the truth of the predicted problem. Of course, the problem of prediction accuracy cannot be perfectly approached, but can only be continuously approached to the truth.

Viewpoint 3: Challenges faced by data rights verification

In general, data-related rights and interests are particularly difficult to define.

Our country has also promulgated relevant laws and regulations on data security and personal information protection this year, but it is still at a relatively early stage, many details have not yet been formed, and there are no relevant trial cases related to data security in the society. awareness has not yet been formed.

In addition to the level of social awareness, there are obvious cross-border problems in data rights confirmation: technology is a major challenge for lawyers, because they cannot know how data is collected and used, who has permission, and under what circumstances can use it etc.; and legal provisions are a big challenge for technicians.

The other is the issue of interests. The platform always hopes to collect as much data as possible. Consumers are always worried that their privacy and other legitimate interests will not be protected. Therefore, we should find a balance between the reasonable interests of all parties. This requires sufficient Practice to run in.

If these problems are solved, there will be a market for data, and data assets will be priced when there is a market, so that data assets will truly circulate.

Viewpoint 4: The development direction of data asset transactions

Professor Wang pointed out: "Data asset transactions must be standard products, and data indexes are such standard products."

The "data index" refers to the data obtained after processing the original data. The purpose of data processing is to avoid leaking private information, such as the credit index required by financial institutions in specific business practices, the health index required by insurance companies, etc.

Because the copying cost of data is zero, the buyer is required not to resell the purchased data index.

Point 5: Data Quality and Data Governance

Improving data quality will have to rely on the market and is unlikely to be achieved through self-regulation or regulation. Because the market will set the price, there will be no market for data that is of poor quality and does not improve the business.

In terms of data governance, Professor Wang is not inclined to set up a unified data regulatory agency, but he does need unified laws and regulations on data regulation.

Laws and regulations can be regarded as a part of the social infrastructure platform. Anyone who violates the laws and regulations of data governance will be dealt with by relevant departments, instead of a separate data supervision department, let alone the data of each industry Governance is established by a regulatory body.