Comparative Study on Valuation Methods of Data Assets: Based on the Perspective of Data Analysis Applications

Abstract

Although the existing valuation methods are showing a diversified development trend, the differences in theoretical logic and applicable conditions among them have not been systematically compared and examined, especially the special requirements for valuation methods in data analysis application scenarios, which still lack in-depth response in existing research. This article conducts a systematic typological comparative study on data asset valuation methods from the perspective of data analysis applications. Based on the underlying differences in valuation logic, this article divides existing methods into four types: cost oriented method, revenue-oriented method, market-oriented method, and value contribution method. A comparative framework is constructed from six dimensions: valuation logic, core assumptions, key methods, main advantages, main limitations, and applicable scenarios. Furthermore, the applicability of each method in three typical scenarios of descriptive analysis, predictive analysis, and normative analysis is examined, and the differentiated role path of data quality dimension in different methods is analyzed. Research has shown that the cost oriented approach is easy to operate but difficult to capture the value differences brought by data quality. The profit oriented approach has clear economic logic but is subject to subjectivity in parameter estimation. The market-oriented approach has high credibility but faces the reality of underdeveloped trading markets. The value contribution approach has the most prominent fit with application scenarios but has a heavy computational burden. The four methods show significant complementary relationships in terms of advantages and limitations. Method selection should be comprehensively balanced based on the characteristics of the analysis task, data conditions, and valuation objectives. This study can provide an analytical framework for enterprises to choose valuation methods reasonably in different data analysis scenarios.