A Study On Valuation Of Data Assets With And Without Transaction Scenarios

With the rapid development of the digital economy era,data analysis,algorithms and other new generation of intelligent technologies are constantly applied in multiple application scenarios such as internal management,precision marketing,and new product development,giving rise to changes in the entire data market.In the past,the valuation of data asset-rich enterprises in the capital market was much higher than the value disclosed in their financial reports.This not only requires enterprises to strengthen the authorization and management of data,but also to focus on the disclosure of the valuation of the data assets they own.In this context,valuation of data assets becomes the most effective way to narrow the gap between the value disclosed in an enterprise’s financial statements and its valuation in the capital market.At present,most of the academics use the cost approach,income approach,and BS option model to value data assets.These valuation methods mostly cover the value of data assets floating on the surface,and do not flexibly capture the full value of data from generation to circulation.Through the study,it is found that the value of data assets is greatly influenced by the presence or absence of transaction scenarios,and its value is presented in different forms in different application scenarios,and dividing different application scenarios can flexibly capture the full value of data assets in different scenarios.For this reason,when valuing data assets,this paper divides the application scenarios not into two types with or without transactions for the valuation study of data assets.This study first summarizes and combs the relevant concepts of data asset definition,data asset process and application scenarios;secondly,by analyzing the process of data assetization,we find that the value of data assets is highly influenced by the application scenarios,and then,from the perspective of whether the data is used for trading,the application scenarios of data assets are divided into two types,with and without trading scenarios,and respectively A value assessment model is constructed.Finally,using enterprise A as the research case,the constructed valuation model was used to evaluate the value of data assets in two application scenarios with and without transactions.It is found that when valuing data assets,it is more reasonable to use the B-S option pricing model for non-transactional scenarios,and it is better to use the hierarchical analysis combined with the excess return method for comprehensive valuation for transactional scenarios.This study enriches the theory of data asset valuation and restores the value of data assets themselves by categorizing valuation by scenarios with or without transactions,which is a new step for the early inclusion of data assets into accounting statements in the future to release more data dividends and bring into play the role of data asset elements.