Through the analysis of the big data industry chain, we can see that in the big data industry chain of various production links, the major companies have been opened to occupy the position, with the high-performance computers, the storage of huge amounts of data and the continuous optimization of the management of the process, the problem that the technology can solve will not become a problem eventually. We believe that the real constraints or bottlenecks in the development and application of big data are three links:

First, the legitimacy of data collection and extraction, the protection of data privacy and the trade-off between data privacy applications.

Any enterprise or organization that extracts private data from the crowd, the user has the right to know, and the use of the user's private data for commercial behavior needs to be recognized by the user. However, at present, a series of management issues in China and around the world on how user privacy should be protected, how business rules should be formulated, how violations of user privacy should be punished, how legal norms should be formulated, and so on, are greatly lagging behind the pace of development of big data.

Deloitte believes that in the future, many big data businesses will be in a gray area during the initial development phase, and that relevant laws and regulations as well as market norms will be forced to accelerate when business operations take shape and begin to have an impact on a large number of consumers and companies. It can be expected that, although the application of big data technology can be unlimited, due to the limitations of data collection, the data that can be used for commercial applications and serve people is much smaller than the data that can be theoretically collected and processed by big data. The limited collection of data sources will greatly limit the commercial application of big data.

Second, the synergistic effect of big data requires the enterprises in each link of the industry chain to reach a balance between competition and cooperation.

Big data puts forward more cooperation requirements for enterprises based on its ecosystem. Without a macro grasp of the overall industry chain, individual companies can't understand the relationship between the data of each link in the industry chain based on their own independent data, and the judgment and influence on consumers is very limited.

In some industries where information asymmetry is more pronounced, such as the banking and insurance industries, the need for inter-enterprise data sharing is more pressing. For example, the banking and insurance industries often need to create an industry***highly accessible database that allows their members to learn about individual users' credit histories, removing the information asymmetry between the guarantor and the consumer, and allowing transactions to run more smoothly. However, in many cases, competition and cooperation exist between these businesses that need to **** share information, and businesses need to weigh the pros and cons of **** sharing data before they do so, so that they do not lose their competitive advantage while **** sharing data. In addition, when many businesses work together, it is easy to form seller alliances that can lead to loss of consumer interest and affect the fairness of competition.

The most imaginative direction of development for big data is to integrate data from different industries to provide an all-encompassing, three-dimensional data mapping that seeks to understand and reshape user needs from a systemic perspective. However, cross-industry data **** to enjoy the need to balance the interests of too many companies, if there is no neutral third-party organization to step in and coordinate the relationship between all participating companies, the development of data **** and the application of the rules, will greatly limit the use of big data. The lack of an authoritative third-party neutral organization will constrain big data from reaching its maximum potential.

Third, the interpretation and application of big data conclusions. Big data can be analyzed from the level of data analysis to reveal the possible association between various variables, but how can the association on the level of data be visualized in the industry practice? How to develop executable programs to apply the conclusions of big data? These questions require practitioners to not only be able to interpret big data, but also to understand the linkages between the various elements of industry development. This link is based on the development of big data technologies but involves a variety of factors including management and execution.

The human factor is the key to success in this process. From a technical point of view, the executive needs to understand the big data technology and be able to interpret the conclusions of the big data analysis; from the industry point of view, the executive needs to understand the relationship between the processes of the various production processes in the industry, the possible connection between the various elements and the conclusions obtained from the big data and industry-specific implementation of the link one by one; from the management point of view, the executive needs to formulate an enforceable solution to the problem and ensure that there is no conflict between this program and the management process. From the management perspective, the executive needs to develop an executable solution to the problem, and ensure that this solution does not conflict with the management process, and does not create new problems while solving the problem. These requirements not only require executives to be technologically savvy, but also to be excellent managers with a systematic mindset, able to look at the relationship between big data and the industry from the perspective of a complex system. The scarcity of such talent will constrain the development of big data.