Theoretical and Modeling Research on The Valuation of Data Assets

Abstract

The era of big data has greatly accelerated the process of capitalization of data elements, and enterprises can fully release their intrinsic value and gain a foothold in the increasingly fierce market competition in the future only if they develop and utilize their data assets in a scientific and efficient manner. However, academic research on data assets is relatively small, and a unified and comprehensive theoretical framework has not yet been formed. How to objectively and accurately disclose the status of enterprise data assets and maximize the role of data assets in enterprise production and social development is a problem that enterprises need to solve urgently. Therefore, this paper further defines the concepts and characteristics of data and data assets on the basis of previous research, applies the traditional market method, income method, cost method, and innovatively borrows Chen model and artificial neural network model to measure the value of data assets, summarizes the challenges of data asset management, analyzes the problems of data assets in the process of applying them, and proposes appropriate It summarizes the challenges of data asset management, analyzes the problems of data assets in the process of application and proposes corresponding solutions, trying to provide a little reference for the practical handling of enterprise data assets in the era of digital economy as well as the theory and method of national economic accounting.