What are the bottlenecks in the future of the big data industry?

Through the analysis of large data industry chain, we can clearly see that in the large data industry chain of various production links, the major companies have opened up, with high-performance computers, massive data storage and management of the process of continuous optimization, technology can solve problems will not become a problem.

summed up, in the analysis of Deloitte, the real constraints or become a big data development and application bottlenecks are three links:

First, the legality of data collection and extraction, the trade-off between data privacy protection and data privacy applications. Any enterprise or organization extracts private data from the crowd, the user has the right to know, the user's privacy data for commercial activities, all need to be recognized by users. However, at present, a series of management problems, such as how to protect users ' privacy, how to make business rules, how to punish the privacy of users, how to make legal norms and so on, are lagging behind the development speed of large data.

Many of the big data businesses in the future will move in grey areas in the initial stages of development, and when business begins to take shape and start to have an impact on a large number of consumers and companies, relevant

Laws and regulations and market norms will be forced to speed up the development. It can be expected that, although the application of large data technology can be unlimited, but due to data acquisition constraints, can be used for commercial applications, services to people's data is far less than the theoretical large data can be collected and processed data. The limited collection of data sources will greatly limit the commercial application of large data.

Second, large data to play a synergistic effect of the industrial chain in all aspects of enterprises to achieve a balance of competition and cooperation. Large data put forward more cooperative requirements for the enterprises based on their ecological circle. If there is no macro-grasp of the whole industry chain, individual enterprises can not understand the relationship between each link of the industrial chain based on their own independent data, so the judgment and influence on consumers is very limited.

The need for data sharing among enterprises is more urgent in some sectors where information asymmetry is more evident, such as banking and insurance. For example, banking and insurance often need to create an industry-shared database that allows its members to understand the individual user's credit history, eliminate information asymmetry between the guarantor and the consumer, and make the deal smoother. However, in many cases, there are competing and cooperative relationships between enterprises that need to share information, and enterprises need to weigh the pros and cons before sharing data and avoid losing their competitive advantage while sharing data. In addition, when many businesses co-operate, it is easy to form a seller alliance and lead to loss of consumer interests, affecting the fairness of competition.

The most imaginative development direction of large data is the integration of data from different industries, providing a full range of three-dimensional data mapping, in an attempt to understand and reshape user needs from a system perspective. However, cross industry data sharing needs to balance the interests of too many enterprises, if there is no neutral third-party agencies to coordinate the relationship between all participating enterprises, the development of data commonality and application rules, will greatly limit the use of large data. The lack of authoritative third-party neutral institutions will constrain big data to its fullest potential.

Third, the interpretation and application of large data conclusions. Large data can reveal the possible associations between variables from the level of data analysis, but how does the correlation of data levels seem to be in industry practice? How do I draw the conclusion that the executable program applies large data? These problems require that the performer not only be able to interpret large data, but also be familiar with the linkages between the various elements of industry development. This link is based on the development of large data technology but also involves the management and implementation of various factors.

In this aspect, the human factor becomes the key to success. From a technical standpoint, the executor needs to understand the large data technology, able to interpret the conclusions of large data analysis; From the industry point of view, the executor should understand the relationship of the process of various production links, the possible association between the elements, and the conclusion of the large data and the specific implementation link of the industry one by one; from the perspective of management, The executor needs to work out an executable solution to the problem, and to ensure that there is no conflict between the program and the management process, without creating new problems while solving the problem. These conditions, not only require the executive to understand the technology, but also should be a superior manager, systematic thinking, can be viewed from the perspective of complex systems related to the relationship between large data and industry.

(Responsible editor: Mengyishan)