Research on Big Data Information Service Pricing Based on Uncertainty Analysis

Abstract

In the era of big data, more and more people use big data technology to provide users with massive data or provide information services based on the results of processing and processing massive data to meet the needs of users for specific information in specific fields. However, with the realization of internal benefits and external effects of big data, the uncertainty of big data information service and the difficulty of quality evaluation make the pricing decision and pricing game of the demander and supplier more complex and difficult to model. Based on the uncertainty of decision matching degree and service quality of big data information service, this paper analyzes the cost-benefit of data asset pricing model through index extraction and mechanism hypothesis definition, and extracts the key factors of data asset service pricing. Through the modeling of return on investment, the important influence factors in the pricing model are analyzed. From the perspective of quality evaluation, it is proposed that quality control and consumer satisfaction need to be balanced by adjusting the benefit function. That is, through the uncertainty analysis of influencing factors and price modeling analysis, the pricing research of two different business characteristics of big data information service is carried out.