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How to Unearth the Value of Data?
Date:07.26.2022 Author:CF40 Research Department


Abstract: Data has become an important factor of production that is virtual,non-consumable, exclusive, increasing in value with scale, and industriallyambiguous. Based on the discussions on China’s data regulations, data security, rights and responsibilities, value assessment, exchange markets, and international rules, a recent CF40 seminar on “China’s Data Governance and Platform Economy” produced six policy recommendations on how to unearth the value of data.

Ⅰ. FIVE FEATURES OF DATA AS A FACTOR OF PRODUCTION

The development of the digital economy in China has empowered the country with substantial amount and types of data. Data has become an important factor of production in economic operations. The proper use of data serves to improve the quality and efficiency of companies, government departments' decision-making, organization, coordination, control, supervision, and public service, as well as the richness and enjoyment of life.

Currently, data elements have five features:

1. Virtual, i.e. data physically exists in a digital carrier; it is a real presence in virtual space;

2. Non-consumptive, i.e. the use or consumption of data does not result in data reduction.

3. Exclusive, i.e. the use of data by one party may restrict access and use by others;

4. Additive value of scale, i.e. the larger and more diverse the data, the more information it includes, and the higher the data's worth;

5. Industrial ambiguity, i.e. a piece of data can be accessed, processed, and used in a variety of sectors.

Ⅱ. SIX CHALLENGES FOR THE GOVERNANCE OF DATA ELEMENTS

1. Difficulties in data regulation. There are numerous regulatory subjects that lack cross-departmental coordination, uniform executive norms, or clear definitions of their respective tasks in complex scenarios, to say nothing of the system's dynamic environment and evolution.

2. Lack of data security. Data security requires that data production, storage, access, processing, and transaction are all compliant withrulesand secured, as well as that each participating subject could properly manage their own data. Over-collection, theft, leakage, illegal tampering, and illicit data transactions occur from time to time due to insufficient security measures. Data security is a systemic issue, and while China has put in place a legal framework to address it through relevant legislation, it still requires a huge number of specific technical solutions and operational guidelines.

3. Ambiguous data ownership. We still don’t have a refined and unified approach to data classification, and poorly-defined data ownership, storage, access and usage are making it hard for market participants to understand their rights and obligations properly. Ambiguous data ownership could disrupt the order of data governance, leading to data leakage and abuse and privacy infringement on one hand, and data hegemony and monopoly that could push up data circulation costs and undermine the value of data on the other hand.

4. Data valuation. Data is an important new production factor, and clear valuation and reasonable pricing are critical to its transaction. China now has encountered bottlenecks in building a sound data valuation system, partly because digital economy is different from traditional business models as data is usually derived from digital products and services provided for free, and party owing to the characteristics of data itself: first, data is highly heterogeneous, and only a very small proportion of it is standardized; second, data in flow tends to have much higher value than that in storage; third, data valuation could vary widely across application scenarios; fourth, data value fluctuations are nonlinear, an example of which is that as a dataset enlarges, the marginal cost of data collection decreases while the value of data increases.

5. Immature data circulation and transaction. The value of any data is realized only when it is circulated and traded. Experts believed that the Chinese market for data is still in the preliminary stage of development. Although several big data trading centers have been established, they have handled a very limited number of transactions that are neither market-based nor rewarding enough to form a sustainable business model. China still has a long way to go in fostering a sound data market and bringing data as a production factor into full play.

6. Global data rulemaking lacks coordination. Data governance and rulemaking have become an increasingly important area of global coordination that could directly affect cross-border data flow and digital transactions. Experts pointed out that among advanced digital economies, the United States is a proactive advocator of free flow of data, the European Union focuses more on human rights including privacy protection, while China puts a greater emphasis on digital safety. Countries are still negotiating and competing on global data rules, and those who fail to properly align domestic rules with international ones may lose their voice in global negotiations or even become excluded which could reshape the global competition landscape.

III. POLICY SUGGESTIONS

1. Improve top-level design and institutional arrangements for better data governance.

Experts suggested that a governance framework co-led by multiple departments could make it hard to coordinate in a comprehensive and forward-looking manner. For this purpose, they called for establishing a high-level data management committee tasked with overall data management, including: 1) clearly defining the rights and responsibilities of all related departments and coordinating their work, so as to improve regulatory efficiency; 2) implementing a data licensing regime so that all data-related activities are put under proper regulation; 3) clearly providing for and explaining data ownership, safety, valuation and trading, and urging efforts to formulate and implement related laws and regulations; 4) putting in place dispute settlement and coordination mechanisms to protect market participants; 5) exploring forward-looking approaches to data governance such as sandboxes or pilot programs that could boost regulatory efficiency.

2. Strengthen data security.

The first is to clarify the acceptable behaviors and unacceptable behaviors during the process of data production and circulation. The second is to strengthen the graded and classified protection mechanism for personal data, and take graded measures according to the different features of basic data, transaction data and behavioral data. The third is to make it clear that public data that does not involve business secrets should be made accessible to the public and subject to government or third-party supervision. The fourth is to implement algorithmic supervision, promote algorithm audit, clarify disclosure principles and requirements, and require developers to disclose key information such as algorithm-related technology, prediction target, prediction accuracy, and stakeholders to effectively protect personal privacy and prevent data abuse. The fifth is to promote data credit development and carry out credit supervision over data market entities.

3. Clarify the data ownership relationship.

Set grading and classification standards for various types of data, and clarify the principles and methods of data ownership. According to the principle that whoever creates the data is responsible for it, it is clarified that the natural data belongs to the individual user, and the data generated during processing can be jointly owned by the relevant parties of the transaction. The use of such data needs authorization from all parties. It’s essential to clearly define the change and transfer of data ownership during the process of transfer. For anonymized derived data, on the premise that the data cannot be traced back to a natural person, the data should be attributed to the processor and the latter can decide how to use it.

4. Improve the data value measuring system.

A data value measuring system should be established taking into consideration of data quality, data usage, and market value to lay a foundation for data pricing. It is recommended to explore a pricing mechanism that combines various measuring methods, and evaluate the accounting measurement methods for intangible assets in a comprehensive way by referring to the income method, cost method, and market method.For enterprise sector data, one can refer to the cost method to calculate its labor compensation, fixed capital consumption, intermediate consumption, other net production taxes, and net capital gains; for government sector data, the cost method and income method can be adopted together to calculate data collection costs, security costs, and public value that may be created; for personal data, the demand method and supply method among market transaction valuation methods can be applied to determine a relatively objective value range.

5. Improve data transaction mechanism and promote the development of the data market.

The first is to clarify the scope of data transactions. For platform economy-generated data involving economic security and public health, it is recommended to establish a specific transaction entity to be responsible for data sharing and circulation, daily organization and management. The second is to establish and improve the price adjustment mechanism for data, open up transaction channels, promote data flow, provide incentives for data owners, and encourage more tradable data to flow into the element market. For example, enterprises should be encouraged to make their public data open, which can help break through the data barriers between the government and the corporate sector. The third is to actively use technologies such as secure multi-party computation and federated learning to simultaneously improve market security and efficiency. The fourth is to study and summarize the characteristics of data transaction in the United States, Japan, Canada, Sweden and other countries, and learn from global experiences.

6. Actively participate in the formulation of international rules and strengthen international cooperation.

The first is to make domestic rules be in line with international ones, actively promote digital trade, and retain sufficient flexibility in international coordination; the second is to actively participate in the discussion and formulation of data management rules, promote the convergence and unification of international rules, and increase China’s voice in the global digital economy; the third is to further improve international data standards, and promote international cooperation in data value measurement, compliance disclosure, cross-border flow, etc.; the fourth is to encourage technological innovation in data-related fields and strive for initiative and leadership in global competition.