Data Exchanges - The Opportunity for International Financial Market Redistribution
*Shanghai, China **/ ACCESSWIRE / November 6, 2020 / *Big data have been driving unprecedented innovations in sciences and technologies worldwide, as well as historical changes in business models and management ideas. Data resources have been increasingly becoming one of human society's critical production factors and strategic assets, and the abilities to gather, analyze and use data are also rapidly becoming the focus of international competition.
In the current international financial market, the three financial centers- the New York, London and Hong Kong exchanges take up the lion's share of the global financial products. Financial assets and values created and owned by these centers are the envy of other countries and regions. Any city or country would love to be presented with an unprecedented opportunity to have one global assets trading center or platform.
With the dawn of the era of big data, data is becoming a financial asset more valuable than oil, while data trading will become the next giant international financial market. When it comes to the emerging industry of big data exchanges, countries and regions currently are pretty much at the same starting point, and whoever can establish a big data trading platform first will be able to occupy the commanding ground in the next landscape of global financial trading.
It is obvious that data resources' value realization cannot be separated from data openness and circulation, i.e., the establishment of a data exchange. It is against this backdrop that third party data trading platforms were born to meet the demands in the data market.
In China, the first national trading platform for big data - the Beijing Zhongguancun Data Ocean Big Data Trading Platform - was established as far back as in 2014. Since then, more than 10 trading platforms have been established successively, including the Guiyang Big Data Exchange (where the Apple China Cloud Data center is located), the Shanghai Data Trading Center, the Chongqing Big Data Trading Platform, and the North Bay Big Data Exchange Center.
In recent days, the Beijing Municipal Government's Outline of Actions to Promote the Innovative Development of the Digital Economy (2020-2022), the Beijing Municipal Government's Implementation Plan for Building Digital Trade Experiment Zones, and the Implementation Plan for Building the International Big Data Exchange in Beijing have been promulgated successively, marking the acceleration in China's push for building big data trading infrastructure, as well as another step in the integration of big data exchanges into the market.
Why are big data exchanged needed?
To talk about the necessity for big data exchanges, we have to first look at the problems besetting big data trading.
When cloud computing and IOT make human beings and everything increasingly intelligent and digitized, the wide spread and immense application potential of big data, together with the great prospect of the market, have spurred new business models and driven the formation of big data value industry chain.
The value of big data has been gradually recognized by society, decision-making based on data science has become the consensus in government and in business, and there is increasingly urgent demand for data openness and sharing. Together with these trends, however, issues such as how to turn data into commercial products, as well as how to turn data assets into financial products (which involves data pricing and trading) also rose, which are the pain points constraining the commercial and financial utilization of big data.
On the one hand, there are extremely rich and wide ranging potential big data resources in every country - from cross-disciplinary fields such as telecommunication, finance and healthcare, to traditional businesses in manufacturing and education, to new industries such as e-commerce and social media. However, even if there have been breakthroughs in the storage and mining of big data, there are huge amounts of "data silos" out there. This is primarily because various parties, out of individual interests consideration, have not worked together to let their data become part something bigger. This is particularly true in democracies, where there are big ideological and legal conflicts between individual privacy and big data commercialization.Such conflicts mean that big data owned by different entities are dispersed at different places as "data silos", which has been constraining big data commercialization.
In fact, data circulation is not something new. However, the lack of laws and regulations governing big data trading market has led to the absence of laws and regulations on big data trading, whether in common law countries or continental law countries. It is precisely because of the lack of trading rules and of pricing standards, as well as information asymmetry between the two parties in a transaction, big data transaction costs are high and data quality cannot be assured, which significantly constraints the flow of digital assets. Further impact of this is that Internet giants, governments and big businesses are gaining more and more abilities to control data sources, and data oligarchs are holding and controlling immense amount of data. All these have led to barriers to free market competition, and mounting difficulty in consumer protection.
On the other hand, there is the inherent Arrow's Paradox in information economics. As early as in 1963, in his "Uncertainty and Healthcare Economics", the Nobel Economics laureate Kenneth Arrow proposed that information (data) is very different from regular goods, that information has this elusive nature: the buyer, prior to making a purchase, cannot determine the value of a piece of information prior because he does not know the information (data); however, as soon as the buyer obtains this information (data), he can make copies of it and therefore won't purchase it.
The reason lies in the fact that the value of data is not absolutely fixed. Data exhibits different market values to different entities using it, and depending on different processing and analyzing techniques. From the perspective of market demand, the same data's market value might be night and day to a business that has a demand for it and a business that has not; from the perspective of data processing and analysis, the variation in the depth and breadth of data mining and integration mean a world of difference in the scope of use of the resulting data products, which consequently lead to the difference in their corresponding market values.
For the above reasons, in a data transaction, the party with the demand may not be able to obtain data that can reach its expected goal despite paying a big price, because it is hard to judge the quality and value of the data; the party that supplier the data, on the other hand, could end up giving a low quote for its data due to the lack of demand information, not to say it has also to worry about the security and overuse of its data.
As a result, there is a lack of transparent and controllable transaction bridge to connect many data suppliers and data customers, and information asymmetry and communication barriers abound. It is against this backdrop of big data transactions in an environment of mismatch of social resources, big data exchanges have become the focus of different countries - big data exchanges that can guide the reasonable distribution of data resources while at the same time regulate transaction processes, therefore promote the virtuous cycle of data flow and create new values. Big data exchanges also present a historical opportunity for the redistribution of national financial markets.
*The Untraveled Road of Data Exchanges*
Let's take China as an example. The year 2015 was the period when big data trading platforms saw their most rapid growth. In April 2015, Guiyang Big Data Exchange opened with the support of the Guiyang State Assets Commission and completed its first transaction; in August, the first data exchange in central China - the Yangtze Big Data Trading Center - opened in Wuhan. Since then, the Central China Big Data Exchange, the Wuhan West Lake Big Data Exchange, and Hebei Beijing-Tianjin-Hebei Data Trading Center also opened successively.
During this period of time, the number and size of big data trading platforms grew rapidly, and their market shares kept expanding. These platforms also experimented with broadening their business scopes. However, it's worth pointing out that the explorations and experimentations with big data exchanges in China have been to a large degree supported by the government, both in terms of policies and funding.
As third party, intermediary platforms, big data exchanges have been pushing the transformation of the previously one-to-one bilateral data market to one-to-multiple or multiple-to-multiple network data market; they are also enabling the further release of big data's potential commercial value, as well as the gradual formation of trading rules and quantitative trading indexes. However,despite the fact that a majority of these platforms had set up very satisfying data trading targets at the outset of their establishment, their operations have been rather difficult. Under the thriving development scene lies the fact that the trading has not been anywhere as active as expected. Surveys of big data exchanges in China have found that with 5 years now behind them, the majority of these exchanges have made very few deals, and still remain in the small scale exploratory phase. This has to do with the tough legal issue of data rights and risk assumptions.
First of all, the degree and extent of data openness and sharing by trading entities such as the governments, businesses and research institutions affect data trading platforms' business scope and trading quality. In the face of tremendous market demand, the commercial value and social value of the data themselves are still yet to be properly mined and utilized.
Currently, data openness and sharing entities in China are mainly governments, Internet operators and research institutions that own big data. What has been hindering the progress in data openness and sharing are multiple and at multiple levels, including the concept of openness and sharing, the platforms and their technical supports, systematic management and monitoring mechanisms, as well as the final feedback effects of openness and sharing.
Take the Chinese government for example. Although it has been consistently experimenting with developing big data driven smart city management models, in their actual application, government employees at different levels still do not have sufficient understanding of data based decision making and modern governance, so the idea of data sharing is yet to take root. Even if most government agencies have already realized the capacity of big data, there is no mature management mechanism, and more importantly, government agencies tend to avoid taking responsibilities for any mistakes made in exploring data sharing mechanisms. On the other hand, however, agencies and institutions that are at the forefront of data openness and sharing are facing the reality of technical difficulties, including such issues as insufficient maintenance and management of data. The barrier to openness and sharing, at the firm level, lies in the fact under a competitive relationship, firms are reluctant to open their data out of their business interests.
Therefore, in order to effectively stimulate more entities to participate, and to improve the usability of data, the first thing is to integrate structured and unstructured data, and eliminate "data silos". At the same time, firms' internal and external data need to be connected to eliminate data separation. In addition, participation of governments and businesses need to be increased through appropriate measures to protect information securities, so all entities can work together on data standardization and regulation improvement, and to unleash more potential of the data trading market through responding to all entities' needs for data.
Secondly, the confusing arrays of data form make it very difficult to fully release the immense value contained in data. When it comes to data quality and effectiveness, the lack of quality and effectiveness of the source data impacts the quality and accuracy of data trading. The reasons are, first, in a market without rules, the majority of the regional trading platforms become their own systems. Disparate data format, different ways of data categorization and different definitions of the same category makecommunication in the market very confusing and difficult; secondly, there is the issue of the truthfulness, integrity and consistency of data, which directly affect the value of data assets; thirdly, technical support abilities need to be increased. Can the data be fully harvested and queried? Will the format of the obtained data be good for subsequent interactions? Can real time and valuable data be obtained, updated and maintained in a timely manner? All these issues will impact data trading quality.
Obviously, with the improvement in data mining, analysis and utilization, the release of data value depends on reliable data trading to enable data to flow, and it is only through being continuously analyzed and utilized that data's immeasurable values can be realized.
Finally, protecting trading security is a part of protecting the overall security of big data. The lack of data trading security will cause immeasurable losses.
In terms of the exchanges, for one thing, when problems occur during any stages prior to data trading, it will cause the trading to fail in later stages, which will result in both the data supplier and the data buyer to assume contract breaching liabilities; secondly, there is the issue of the trading site's security. When there is large amount of data to trade, illegal trading in the black market will occur in the absence of a secure trading site. But how should a trading site be regulated? What types of rules should be followed in setting up such a site? There are still no clear and specific plans out there. Thirdly, data trading entities' qualifications and abilities should also be taken into account when considering trading security.
This also means that every stage prior to the completion of a trade should be tightly regulated. Otherwise those affected will not just be individuals. The impact will spread to societies and nations, therefore affecting overall security.
Looking from the above perspectives, the big data trading center established in Beijing will continue to face many challenges. How to build a big data trading system centered on big data trading platforms to meet the demand of the data market remains an issue to be studied and addressed.
Current reality demonstrates that there are five main challenges in trading of big data as commodities:
1. Data commercialization boundaries, i.e., the boundary between data security and openness, and the boundary between privacy and commercialization. What data shall fall under the national security category? What data fall under the open and commercial category? What data can be used for commerce, and what data cannot be used for commerce?
2. Data categorization standards, i.e., how to categorize data. There is the need to formulate clear data categorization standards to help define the boundaries of each category.
3. Data pricing, which is the next issue after data is categorized. How should we price these categorized data commercially?
4. Data trading mechanism and income distribution. Current data can be roughly divided into 5 main types: government governance data, industrial data, financial data, public services data, as well as business data centered on individuals. Data trading mechanism and income distribution are fairly straightforward for the first 4 types of data, that is, data owners have the rights to and enjoy the benefits of commercializing these data. However, data generated from individuals' commercial activities is a tough issue for data trading, as this type of data is essentially generated from users themselves, and fall into the category of users' individual private behaviors. Shall one part of the income from the commercial trading of this type of data be distributed to the users themselves?
5. Big data legislation, i.e., whether it is at the individual data privacy level, or at the commercialized data level, there should be special legislations to define and safeguard boundaries and rights, to enable both users and businesses to have clear laws and regulations to follow in data commercialization, and to prevent unnecessary harm to users and interference into people's normal life by improper data commercialization.
Big data exchanges are inevitable from the perspective of human society development,. However, to truly promote the growth of such exchanges, the above five problems need to be addressed as soon as possible in a fundamental way to ensure the healthy and orderly development of data exchanges.
For any country or region, data exchanges driven by the commercialization of big data present a rare historical opportunity. Whoever is able to lead both at the legal level and the standards level will be the first to establish an international data exchange, and gain a powerful voice in international discourses about data transactions.
About the author: CHEN Gen is a science and technology writer in China. He writes about new developments in the Internet, Fintech, biomedical and other fields. His email is: genchen2021@gmail.com.
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