Research on the Promotion Strategy of Marketization of Electric Power Big Data from the Perspective of Risk Cognition

Abstract

Electric power big data has great application value, the development and utilization of electric power big data with the help of big data collection, storage and mining is a momentous way for electric power enterprises to improve quality and practice sustainable development, as well as a necessary choice for the future development of social economy. At present, the marketization of electric power big data is not very respectable, the reason of which is in the circumvention of potential marketization risk by related market subjects. From the perspective of risk cognition, this paper makes a comprehensive observation and analysis on the factors that lead to the enhancement of risk cognition during the marketization of electric power big data, such as privacy leakage, rights and interests definition, security guarantee, etc., as well as the factors that lead to the reduction of risk cognition, such as the natural monopoly, high authenticity, high value, relatively weak privacy and natural public welfare of electric power big data. It is found that there are two problems during the electric power big data marketization: exaggeration of enhancing risk cognitive factors and neglect of mitigating risk cognitive factors. The marketization of electric power big data has its particularity in the industry, and it could be relatively easy to promote its marketization, but because of the existence of the above cognitive problems, this process has not become a reality. In conclusion, in order to promote the marketization of electric power big data, it is necessary to adopt reverse operation to the factors of enhancing risk cognition and mitigating risk cognition among them, and formulate the promotion strategy of marketization of electric power big data comprehensively, including detailing privacy protection measures, systematically improving the technology of data information protection ,actively strengthening laws and regulations of industrial big data application , promoting "public welfare" to reduce "data exploitation" sense of customers and strengthen enterprise responsibility awareness through self-pressure, etc.