Pricing for Data Assets Based on Data Quality, Quantity and Utility on the Perspective of Consumer Heterogeneity

Abstract

It is an inevitable trend for the development of global digital economy to transform data into data assets and realize their transaction circulation. Aiming at the release of data value and the development of its transaction process, the concept of integrated score of data is proposed by combining integrated quality index containing four dimensions with data quantity. On this basis, data assets are priced according to the principle of profit maximization by constructing a nonlinear programming model. Among them, three types of pricing models are divided according to the heterogeneity of consumers’ utility sensitivity, and the consumers’ wiilingness to pay are adjusted based on business parameters using FAHP system. The proposed model is verified with the data of China's carbon emissions as the original data, combined with the KNN machine learning algorithm and a series of simulation analyses. In addition, multiple sets of heterogeneous data are tested. The results show that the quality, quantity and utility of data have an important impact on the pricing of data assets, and it is necessary to divide the utility sensitivity of consumers as well as take business parameters into consideration. The model proposed can also provide decision-making reference for data platforms.