The big data trading market is going through a new turning point. A few days ago, at the forum of the 2020 China International Fair for Trade in Services, Beijing Mayor Chen Jining announced the establishment of a big data exchange. A few days later, the "Implementation Plan for the Establishment of Beijing International Big Data Exchange" was officially released, among which becoming a "data trading platform widely recognized by the market" is one of its functional positioning.
Earlier in 2014, China's first big data exchange settled in Guiyang. A few years later, Wuzhen and Shanghai also established their own big data trading centers or exchanges. " and "pricing" issues, the big data trading market has been advancing in bumps. After these years of development and the new signals revealed by Beijing, it may mean that the big data trading market is ushering in a new turning point.
In April of this year, the "Opinions of the Central Committee of the Communist Party of China and the State Council on Constructing a More Complete System and Mechanism for the Market-oriented Allocation of Factors" (referred to as "Opinions") was announced. factor of production. The establishment of a big data exchange may truly liberate data and become a factor of production that drives economic growth.
secondary title
Big data exchange market resolution method: price discovery and multi-party reciprocity
Recently, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Constructing a More Perfect Factor Market Allocation System and Mechanism", which put the content of the factor market-oriented trading platform into the part of "improving the operation mechanism of the factor market". Therefore, according to the "Opinions" , the big data exchange is positioned as a market-oriented trading platform for data elements, so it should be defined as the concept of a narrow market, that is, "a place where buyers and sellers exchange commodities."
In a broad sense, the data element market also includes all parties involved in the exchange (data open subjects, data application subjects, data value-added service providers, market supervision and operation subjects, etc.), as well as supporting procedures, laws, regulations, etc. .
After clarifying the concept, I think that big data exchanges can: from the narrow market level:
1. It can solve the contradiction between supply and demand in the data industry and the liquidity of data elements, benefiting both data supply and demand. There is a popular metaphor, if there is no farmers' market, how can the food grown by farmers be effectively sold to as many people who need food as possible? In addition to food, how can a wide variety of crops such as vegetables and fruits be effectively traded? The same is true for data elements.
In the past few years, big data, AI and other technologies have developed rapidly. The financial industry has started the process of digital transformation. The value of data elements in improving the efficiency of enterprise operations has been fully verified, but only self-produced and self-sufficient "Digital transformation" is not market-oriented digitalization. To enhance the social value of data elements, pioneers who have "more than enough power" will inevitably need to open up the data elements they hold and the surplus data capabilities. It can be given to more economic entities, and the big data exchange provides a more convenient matching and trading mechanism for both supply and demand, which can better improve the liquidity of data elements;
2. Avoid the problem that the top-level design has insufficient market visibility, which leads to the reduction of policy effectiveness, and benefits regulators. Compared with the scattered, spontaneous, and "private" forms of data transactions, big data exchanges can also help regulators such as the government more conveniently and flexibly conduct overall supervision and regulation of the data element market;
3. Promote the division of labor in the industry and benefit more main players in the data industry industry chain. For example, in addition to data interface products, big data exchanges in Guiyang, Shanghai, and Zhejiang also have data application products, which are very valuable innovations. The introduction of data application products has broadened the scope of data element market participants. In the traditional sense, data open subjects (sellers) and data application subjects (buyers) have added the possibility of data value-added service providers joining the transaction. These subjects provide data processing, data analysis, data modeling, data application and other capabilities and Services, the agile realization of the value of data elements is very valuable;
secondary title
The big data trading industry still needs to solve the problem of "disintermediation"
From my observations in the past few years, the exploration and attempts around data opening and data trading have never stopped. It can be said that the domestic big data industry has still experienced a very difficult process of searching up and down.
Long before the big data trading centers in various places, in fact, some commercial companies (mainly Internet and technology companies) relied on their own data resources and data capabilities to build "data markets". On the one hand, it also serves as an entrance to collect more data. However, after the market is cultivated, data holders generally realize the value of data. In the absence of credibility injection and related rule design, the data market initiated by commercial companies is not as effective as expected;
As a result, a data element market in the form of government open data platforms and big data exchanges in many places emerged, which solved the problem of credibility, but as Mr. Lai Lei, vice president of Zhejiang Big Data Trading Center, said in an interview: " In most cases, people only contact some customers through the trading center, and the trading process itself does not rely on the trading center to carry out, and buyers and sellers run their own models", that is, the exchange "disintermediation" occurs. According to the research of a market research organization, open government data platforms and big data exchanges once became the standard equipment for the digital transformation of governments in various places, but more than 80% of the platforms consume less than 100 pieces of data per day.
Since last year, domestic relevant policies, laws and regulations, etc. have frequently received good news, which has alleviated the problems encountered by government data open platforms and big data exchanges to a certain extent. However, I think the problem of "disintermediation" of exchanges at this stage still needs to be resolved The problem.
The "disintermediation" of the exchange is due to the fact that the trading platform's capabilities cannot help all parties reduce data transaction costs. In addition to the price of the data itself, the transaction cost of data is far from being directly solved by "data interface" and "data label", which also involves data security and model knowledge of all parties. Property rights...and so on.
secondary title
The Digital Assistance of Privacy Computing in the Big Data Trading Market
As mentioned above, from the concept of a broad market, the data element trading market requires the division of labor, coordination and participation of various subjects, including data open subjects, data application subjects, data value-added service providers, market supervision and operation subjects, etc. of. Judging from the various policies at the national level, the factor market must operate in a market-oriented manner. A more complete factor market-oriented allocation mechanism is a very complicated proposition. The country, local governments, and various enterprises need to base their It plays a corresponding role based on its own positioning and its value and positioning in the digital economy. With the deepening of the marketization process of data elements, I believe that the division of labor will become clearer and the collaboration chain will become more mature.
In addition to the gradual improvement of the industrial chain, the progress of digital transformation in various domestic industries is different, which is also a major feature of the current big data transaction industry chain. The transformation is still in its infancy. Therefore, when we talk about the technologies involved in the big data transaction industry chain, we must realize that the technologies that are truly valuable to the industry chain must cover the two major areas that can serve digital transformation and digital assetization. Technologies at different stages should not be biased towards one end.
For the stage of digital transformation, artificial intelligence, big data technology, cloud computing technology, IOT technology and other technologies that directly improve the degree of digitization and digital productivity can play a more direct role. For the stage of digital assetization, blockchain, privacy computing, etc. are used to form The technology of new digital production relations is particularly important.
For privacy computing, if we summarize the difficulties encountered in the previous big data transactions and the digital transformation of the industrial chain, we can generally look at the role of privacy computing in the following aspects:
1. Balance the contradiction between data security and data value realization: For a long time before, everyone was accustomed to centralized data storage, organization, analysis and application logic such as data warehouses, big data platforms, and data platforms. It is believed that in order to break the data island, the physical transfer and aggregation of data are necessary pre-actions, but these pre-actions can easily conflict with personal privacy, data security, and data ownership.
Before the privacy computing technology was paid attention to by people, everyone’s thinking did not break away from the mindset of physical concentration, so many related technologies, such as database auditing, personal data de-identification technology, etc., did not actually hit the core of the problem The essential.
The combination of privacy computing technology and blockchain technology is to fundamentally balance the contradiction between data security and data value from the perspective of "physical dispersion and logical concentration" of data. This is also where the value of privacy computing is most recognized.
However, I think that in addition to considering privacy computing under the framework of "data security", its role in the process of building a more complete market-oriented allocation mechanism of factors is also worth exploring:
2. Accelerate the marketization process of the big data trading industry: Once the aforementioned problems are resolved, a foreseeable trend is that the division of labor among the participants will accelerate differentiation. The subjects of value-added services will collaborate in an environment of mutual trust and high efficiency—data open subjects don’t have to worry about data leakage, data application subjects don’t have to worry about the high economic and time costs brought about by data product customization, and data value-added service providers don’t have to worry The value of the service provided is hard to measure...etc.
From this perspective, privacy computing technology plays the role of enabler of new data collaborative production relations.
3. Accelerate the overall digital transformation process of the industrial chain: At present, in the financial, medical and other industries, it has become a major trend and hot spot for leading enterprises to open up their capabilities to empower their peers and even related enterprises in other industries, such as open banking in banks and medical alliances in the medical industry , medical community interconnection, etc., are all typical examples of open empowerment.
secondary title
"Confirmation of rights" and "pricing" in big data transactions
Data right confirmation is an important prerequisite for data transactions, including the data right confirmation service of Guiyang Big Data Exchange, Zhejiang Big Data Trading Center confirmation platform, people's data right confirmation service, etc., I think they all belong to data right. Determination in the sense of rules or laws has also been a hot topic of discussion in the domestic academic, legal, and scientific circles recently.
Regarding data ownership confirmation, I would like to discuss it from the technical level. Data ownership also needs to be supported at the technical level. For example, there are two data sets with the same structure but different sources. Which data set does a piece of data come from, and which data provider does it come from? If it is not known with certainty, then when a dispute over digital rights occurs, it is difficult to determine the situation and related responsibilities in the process.
author:
author:
Mr. Zhang Yingchun is the solution architecture VP of Light Tree Technology. He graduated from Shanghai Jiaotong University with a master's degree in engineering. He has more than 10 years of experience in financial industry consulting services. He has led the data business construction, integration, and transformation of many large financial institutions. and other large projects. Before joining Lighttree, he worked as a senior pre-sales consultant in the Oracle financial industry, an IBM solution expert, and a consulting director in the financial industry of TalkingData.
Lighttree Technology is a world-leading innovative enterprise in the field of multi-party secure computing. It was established in Beijing in 2017 and currently has branches in Beijing, Shanghai, and Guangzhou. Tree of Light is committed to creating a high-performance, secure, configurable, blockchain-based privacy computing platform, empowering financial, government and other industry organizations to enable credible joint computing without exposing the original data Learning and federated computing. The company's core products include Tianji trusted computing platform, inter-cloud federated learning platform, data open innovation platform, etc., and have been implemented in finance, government, agriculture and other industries.
Please pay attention to investment risks
The opinions contained in the article represent only the author's own
and does not constitute investment advice
Please pay attention to investment risks
