Big data is one of the most important drivers of the digital economy and the fourth industrial revolution. The rational use of big data can greatly promote economic development, but if not used properly it can also cause many problems. When used correctly, it can help companies grow their size, increase efficiency, improve experiences, reduce costs, and control risk. These five positive effects can change some of the laws of current business operations. With the support of big data analyzes, for example, the limit of the 80/20 rule can become less important. The 80/20 Rule, also known as the Pareto Principle, is a theory that states that in a given situation, about 80 percent of the consequences come from 20 percent of the causes.
In other words, it is entirely possible for financial institutions to offer 80 percent of mass customers financial services under the premise of low costs and high efficiency through the use of big data. Thanks to the widespread use of big data and technology platforms, the widespread phenomenon of diminishing returns to scale may also change. The marginal costs for different companies will decrease or even reach zero. All of this can bring about revolutionary changes in business and finance. There is a concept of the production function in the economy that is determined by various factors of production, including land, capital and labor. Adding data to this production factor can change the future marginal power of each factor, which can eventually lead to changes in some basic characteristics of the production function.
For less developed countries, this could be a new opportunity as it usually takes a long time for factors, including human capital, to accumulate, but with big data, it is entirely possible for a country to skip the frog if it can collect and analyze, however, there are also differences between data and traditional production factors, in terms of the definition of rights, data factors are not scarce, so their use is not exclusive, it can turn into an advantage, but there are also objective difficulties in relation to transactions and prices. In addition, land, labor and capital can be assigned. While the land cannot be moved, labor and capital can be allocated to create new units of production and start new production. Some can be configured for data, but others cannot.
In short, data governance has a number of problems to solve, including validating rights, transactions, pricing, and usage. In order to collect and analyze data, it is also important to strike a balance between protecting rights and making the most of that data. Good governance can protect rights and interests, and achieve exchange, reasonable pricing and scientific allocation, thereby generating the greatest economic benefit. In reality, however, there could be more difficulties. I suggest that governments adopt a pragmatic strategy to strike a balance between protecting privacy, data security and the exercise of values.
First, find a balance between security and innovation. Data protection or data security in the broader sense includes national security and the protection of privacy. In terms of data protection, Europe has done the best job, but also because Europe does not have a platform economy or particularly successful digital economy companies. However, China’s digital economy has helped create a number of industries, but it has also posed some problems. The United States is in the middle, but it is hard to judge whether the US is in the best condition. Data protection must therefore be strengthened, but not overrated, as in Europe. You need to strike a fair balance and make a reasonable distinction between the different types of data.
Some data that includes private rights needs tighter controls, while other data is adequately controlled, as the ultimate goal is to make the most of the value of big data. Second, the need to strike a balance between sharing and efficiency. Big data is continuous in time and space and is not a single independent data element. Really meaningful data must be integrated and analyzed in order to break down information silos and achieve a data exchange. However, there are two bottlenecks when it comes to sharing data. The difficulty facing many financial institutions, micro, and small and medium-sized enterprises, is the information asymmetry and the lack of sufficient data to assess the creditworthiness of these companies.
Provinces like Guangzhou, Zhejiang, and Shandong have made good attempts to incorporate data to support financial services. You have set up a comprehensive financial services platform sharing information on social security, taxes, water, and electricity. More high-level guidelines are needed to solve the problem to ensure the security of the exchange of existing data so that we can achieve stability in the provision of financial services. The second bottleneck is that big data credit checks have to deal with data iteration and profit sharing. There are two big data credit research firms in the country but they are facing difficulties.
We worked with the International Monetary Fund and the Bank for International Settlements to investigate whether big data could be used in credit risk assessment. The answer is yes, but there are certain problems. It is limited to small and short term loans and it is more difficult to increase the amount. If the amount increases, these valuation methods may not be equally effective. In summary, the exchange of financial data must be adapted to different data conditions. For some data that can be shared, we create opportunities to maximize profit. If the data is not suitable for sharing, we should find mechanisms to maximize its economic and social benefits as much as possible. The global digital economy can be roughly divided into three markets, the US, China and the rest of the world. Among the top 20 unicorn digital technology companies in the world, China has half of them, which is quite remarkable.
However, if we do an in-depth analysis, we will find that China’s advantages are the demographic dividend, the separation dividend, and the opportunities for innovation from relatively inadequate data protection, if they can continue to support the development of the digital economy, the country is questionable. When integrating current developments into the global structure, it must be borne in mind that technology, not size, will determine the future development of the country. There is another interesting observation: the general public is often fed up with big corporations, especially when income distribution continues to deteriorate.
The general direction of big data governance is to support innovation and standardized behavior, but the general direction of shared prosperity should be emphasized. The result of innovation cannot simply produce thousands of billionaires. Therefore, data governance must be viewed in an innovative way. It may not be very appropriate to simply follow traditional governance methods or to learn from European or American methods. Using the example of cartel efforts, traditional assessment criteria for cartel efforts relate to market shares and prices should be reconsidered, as the fundamental characteristic of platform economy is size, Look at competitiveness, not market share.
