Data Assets and their Impact on social Governance - Dr Ding Ru
Data is the key element of digital economy. As basic resources and means of production, it has great value in social production and life. It has been more than 40 years since the emergence of the term "data asset" in 1974. During this period, data, as a new asset, has been constantly explored and created. Data assets have gradually become the national strategy of the world's leading countries in the field of big data layout.
What is a data asset?
"Data assets" was first proposed by Richard Peters and mainly refers to the holdings of government bonds, corporate bonds and physical bonds. As time goes by, people pay more and more attention to data assets, and their research and understanding are deepening, and their connotation and scope are also expanding. Research on data assets started late in China. In July 2015, Zhongguancun Shuhai Data Asset Evaluation Center was established to solve the problem of data asset ownership confirmation, integration, evaluation and other major businesses. On April 28, 2016, Zhongguancun Shuhai Data Asset Evaluation Center and China Academy of Information and Communication Technology jointly established zhongguancun Data Asset Entrepreneurship and Innovation Platform to carry out financial services of entrepreneurship and innovation based on data assets. At the establishment ceremony of the platform, Guizhou Dongfang Century Technology Co., Ltd. obtained the first "data loan" loan. Zhongguancun Shuhai Data Asset Evaluation Center, together with Gartner, the world's most authoritative information research and consulting company, released the world's first data asset evaluation model, marking that data assets bid farewell to the era that cannot be accurately evaluated and quantified, and the comprehensive and standardized evaluation of data assets becomes possible. In April 2018, China mail tunnels institute of cloud computing and big data released the data assets management practices (version 2.0), the white paper, defines data assets as "owned or controlled by the enterprise and can bring the future economic benefits for the enterprise, such as data resources, most data assets in the form of a physical or electronic records, such as data, electronic data file", This is currently the most complete definition of a data asset.
Who owns the data?
(1) Property rights of data assets
Data can be copied, and the unwritten rule of "who collects who owns" is widely followed in today's Internet, which leads to privacy infringement, data leakage and other problems everywhere. When consumers do online shopping, consumer data will flow through multiple channels such as payment platform, bank, shopping platform and merchants. Who is the final owner of the data has become a legal issue worth discussing. Unclear data property rights have hidden dangers for data privacy and security, and concerns about ownership, privacy and security have also led to difficulties in data opening and circulation. If data is not circulated, islands will form, which will not be able to meet the scale and density of data demand, and the validity of data will be greatly reduced.
(2) Property rights of data assets
Often, most assets such as bank deposits, real estate or antiques have clear title to them. The asset owner can obtain corresponding financial return through the asset. Therefore, in the process of asset trading, the relationship between owners, managers and users is owned by each related party of the asset. However, for data assets, such a clear property right relationship has not yet been formed. In general, the company that essentially owns the data is the biggest and even the only one to benefit, and business partners can also benefit from resource exchange. However, the theoretical data users not only cannot obtain benefits, but will even encounter price discrimination due to the possession of personal information, or suffer losses due to the disclosure of personal information. At present, there is no perfect legal system to deal with the property rights of data, making it difficult for data to become real assets.
Iii. What kind of legal support is needed for data capitalization?
(I) The technical dilemma of data becoming assets
The value of goods and resources is determined not only by their use value, but also by demand and supply, and it is difficult to maintain a high price for something that can be easily replicated and mass-produced at very low cost. Information data itself is cheap to copy, almost zero cost, so it is difficult to maintain high value. The non-competitive and infinite sharing characteristics of data make the potential application value of data very large, and data has a long long tail value chain. The demand of each link of the value chain is different, and the value can be mined from the data is also different, which makes the data value has great uncertainty.
(2) Data becomes the security dilemma of assets
Data is hardly an asset because it is cheap and easy to copy. First of all, the data in the cloud has certain security risks, and after the data is centralized, the impact of leakage events is great, and one event may leak the data of tens of millions of people. If data is distributed, the benefits and costs of data acquisition become high, and if data is obtained illegally in large quantities and cheaply, and cannot be traced or held accountable, it undermines the entire market. At present, there is still no realistic and feasible way to effectively restrain illegal data acquisition behavior in the market, and the fact that the market is destroyed is difficult to change. Secondly, the massive sale of data by data platforms to third parties will also cause data security problems. Therefore, data is more likely to become valuable assets by strengthening legal constraints and giving data the attributes of physical assets to prevent data from being copied and sold at will.
(3) Application of data encryption technology
In 2014, Dr. Schmidt, the former CEO of Google, said, "Bitcoin is an amazing cryptographic achievement, and the ability to create unreplicable content in the digital world has tremendous value." In terms of preventing information leakage and abuse, blockchain technology encryption is a way to change the status quo of data utilization, and it is also the best tool known to solve the problem of data being copied at will. In terms of limiting the ability of data managers to abuse their power to copy data at will, blockchain can return the property rights to the data owners, who can authorize and benefit from the use of the data, which is consistent with the "asset" form to some extent. At the same time, blockchain can completely separate verification information from possession information. The user of data does not need to possess information, but can verify the authenticity of information or judge whether the information has certain characteristics under the condition of authorization.
(iv) Progress in data supervision and regulation
Technical measures need to be implemented in conjunction with legal measures to ensure that data assets become a reality. The European Union and The State of California lead the world in legislation governing data and regulating access to data.
In May 2018, the European Union implemented its General Data Protection Regulation (GDPR),
It stipulates eight data subjects' rights, including the right to know, the right to access and the right to delete, in order to restrict Internet and big data companies' processing of personal information and sensitive data. Under GDPR, data companies must be able to articulate where data is flowing and develop their own tools to track data usage so that owners and regulators can keep track of how it is being used. This rule has been copied in many countries and regions outside the EU. In the same year that GDPR began in Europe, California adopted the California Consumer Privacy Protection Regulation, or CCPA, which went into effect on January 1, 2020. The principle is in line with the GDRP, and since California is home to most of the country's largest Internet companies (with the exception of Amazon and Microsoft), it can basically regulate most of America's Internet companies, so it is considered the US version of GDPR.
What business value does data asset create?
(I) Why can't we rely on the self-restraint of big data platforms?
At present, companies that hold and use user data often oppose strict regulation for two reasons. One is that platforms help users manage their data, and they naturally enjoy access and partial ownership of the data. Second, if the use of data is restricted, the quality of products will be affected to a certain extent. But the quality of the actual product is not consistent with the amount of user data, and the platform does not need to know users' private data. Before GDPR came into force, Europe had some of the most stringent regulations on Internet data security in the world, but its Internet companies still collected a lot of "uncollected data". The GDPR, Europe's data regulation, requires every website to tell users what it collects when they visit, but most websites and software collect data unrelated to the services they provide.
In das Kapital, Marx quoted The British worker's activist Tommy Denning in Unions and Strikes as saying, "[Capital] becomes active when it has 20% profit; With 50% profit, it takes the risk; For 100% profit, it dares to trample all human laws..." Companies with big data will not give up making profits from it, and the only practical solution is to use public power to check the power of companies.
(2) What is the possible form of the new data business model?
Data can benefit more people and more businesses by restricting the power of big companies to use it and giving ownership back to individuals. So there is a need to change the business model of using data to make money, and some experts in the United States are also thinking a lot about this.
In the past, personal medical records belonged to hospitals, and it was difficult for patients to obtain them, and other hospitals could not make use of medical data for research, which was detrimental to both individuals and medical research. After the rise of blockchain technology, some statisticians and bioinformatics experts have focused their research on taking advantage of the separation of viewing and verifying information in blockchain to give the individual's medical records back to the individual and then ask the patient to authorize the doctor to use the relevant information for statistical research. At the same time, the medical information itself is encrypted so that it can only be seen by patients and doctors authorized to see it. The information is not generally available to the person doing the research, but can be asked questions from a person's medical record with permission to use them. Wang Yongxiong, a professor at the Department of Statistics and School of Medicine at Stanford University, once studied that most doctors in the United States are willing to pay $10 to $15 for access to medical records that they cannot access before. According to this calculation, patients with difficult and complicated diseases can obtain $10,000 to $20,000 in information authorization fees every year, enough to cover medical expenses and part of the cost of medical insurance. Medical records viewed by a large number of doctors can also increase the chances of finding a cure to some extent.
Due to poor information flow, and medical data involves the most private information of people, it is more difficult to share. If the query and verification of medical information can be solved through blockchain technology, it will be of great help to the treatment of difficult diseases and even all diseases. After the ownership and use of data are strictly separated, using blockchain or other technologies, data owners can put their data on existing big data platforms, and data platform companies can provide storage and other services for free in exchange for the use of users' data. Third-party companies can pay users to use the data, but unauthorized platform companies cannot interpret and use the data.
Take the bank as an example to explain the new and new way of profit distribution: If 10,000 yuan is deposited in the bank, the bank provides it to a third party in the form of loan, and the third party needs to pay interest to the user, while the bank earns the difference between the deposit interest and loan interest as the cost and profit of managing money for the user. If the data company is regarded as a bank, if the company gains profits by using the user's data, it should pay 500 yuan to the user every year. However, because it keeps the data for the user, it needs to charge 300 yuan for the service fee. The difference of 200 yuan between the two is the authorization fee that the company should pay for using the user's information.
Therefore, the value of data can be reflected in price. At the same time, the more activity a person has on the Internet, the greater the amount of data they accumulate, the more valuable that data is likely to be and the greater the benefits, rather than the more things they buy, the more likely they are to be subject to price discrimination. Data becomes a data asset when its value is truly recognized by the market and it can benefit from it.
5. How to advance data assets?
It's not that data can't be an asset, but for that to happen, technology and the law need to advance together.
(1) Reasonably determining the ownership of data property rights
First, strengthen the top-level design of data rights confirmation. Through the data made by legislation and judicial work, combined with the new civil code, to regulate the data ownership as the goal, focus, scene, link elements such as data source, data resources classified guidance and application management regulations, clear the raw data, can trade data, such as scope, implement the data clear property rights, legal compliance. Second, realize data ownership and management rights separation. On the basis of determining the scope of tradable data, the ownership relationship of data ownership and management rights shall be clarified, and data ownership shall be owned by data producers and data management rights shall be owned by data operators, so as to effectively protect the legitimate rights and interests of various market subjects. Third, we should properly determine the right to dispose data, promote the benefit distribution to the data business entities, and improve the enthusiasm of market entities.
(2) Strengthening data protection and utilization
First, strengthen data security protection. Data security is protected by classification and classification protection system. Data sensitivity and security level are determined according to industry types, data types, scales and main uses, and corresponding data protection policies are formulated. Second, explore the role of blockchain, artificial intelligence, big data and other technologies in preventing data tampering and realizing data traceability, review and distribution, so as to solve the problem of data trust among different market entities. Third, promote the efficient use of data resources, build standardized data application scenarios of finance + data, commerce + data, culture and tourism + data and other industries, and build a new engine for the development of the digital economy. We will establish a feedback mechanism for government data and public welfare data, improve the quality of data opening, and promote better enabling social governance in the use of data resources.
(3) Innovate data investment operation mode
First, banks are encouraged to standardize the development of data pledge. On the basis of data pledge business, a data pledge customer management system is established to statistically analyze the data information pledged by users, establish a data pledge risk warning model, and determine the interest rate price according to the risk. Second, establish the data asset securitization mechanism, standardize the data trading platform, strengthen the supervision of data trading and application, promote the orderly development of the asset securities market, and provide a basis for data assets to participate in equity financing. Third, strengthen innovation of financial products of data assets. Explore the needs of potential customers, comprehensively use resources input, technology accumulation, talent training, application practice and other means to innovate insurance, credit, Internet finance and other products, establish personalized and characteristic product service system.
(4) Standardizing market transactions of data products
First, explore a reasonable data pricing mechanism, and adopt a dynamic comprehensive pricing method according to the actual situation of data assets and market expectations. Cost pricing method is adopted for integrated data in order to effectively cover the costs and reasonable profits of market operators. Second, standardize data trading platforms in accordance with the law, explore the establishment of a rating and evaluation system for data trading platforms, provide evaluation data for market subjects, strengthen operation supervision and punishment for trust-breaking of trading platforms, ensure compliance operation of data trading platforms, and regulate data trading behaviors.
(5) Establish a data element supervision and governance system
First, improve the law and system of data supervision and governance, coordinate the control of data development and utilization, circulation and transaction, assets and security management and other factors, establish a comprehensive centralized, unified, efficient and authoritative data security risk assessment and early warning mechanism, data security emergency disposal mechanism. Second, build and improve the system management system of data element market security transactions, strengthen the capacity building of data security technology and research and development of protection technology. Specialized departments and institutions should supervise and monitor data safety and risk early warning comprehensively from the legal system and technical dimension, and strictly control unfair competition behaviors such as data fraud, abuse and monopoly by combining market administrative supervision and anti-monopoly restrictions, so as to achieve healthy and orderly development of data market.