Survey of data factor market： value， pricing， and trading

Abstract

With the rise of computing power networks, a novel infrastructure centered around computational power has emerged as a key driver in the development of the information economy. Data, recognized as a crucial production factor in the digital era, serves as a vital pillar for constructing new development paradigms. The data factor market, acting as the link between data and computational power networks, is particularly critical. Its robust operation directly affects the sustainable development of computational power networks and is provided with crucial support for the information economy system. Therefore, the data products were treated as the focal point, while the processes involved in the data factor market and the inherent connections among these components were introduced. Subsequently, the existing work on three key aspects of the data factor market was studied, including data value, data pricing, and data transactions. Regarding data value assessment, key aspects such as relative data value evaluation were discussed, introducing indicator systems, indicator weights, and value indices. For data asset value assessment, traditional methods such as the cost method, income method, and market method were discussed, as well as emerging methods based on machine learning. In terms of data pricing models, existing methods were categorized into two main types: pricing for dataset products and pricing for data service products. Seven pricing methods were summarized, and the advantages, disadvantages, and applicable scenarios of each method were further analyzed. Regarding data transaction systems, the applications of centralized and distributed architectures in the data trading market were analyzed from the perspective of system architecture. The current status and prospects of both on-exchange and over-the-counter trading modes were then introduced.

Keywords

• data trading
• data value
• data pricing
• data market