数据产品交易的定价研究:进展评述与方法比较

Data Product Pricing:A Review of Research Progress and Comparison of Pricing Methods

数据定价是数据交易中的一大难点问题,需要综合运用计算机科学、数据科学和管理学等方面的基础知识。本文选择计算机科学、数据科学和管理学视角下典型的数据产品定价方法进行综述,介绍了管理学视角的“基于人机协同的数据产品交易参考价格(区间)方法”。得出结论:现有方法均各自需要一定启动条件或是只针对特定的应用场景,具有一定的局限性。数据定价是基于多方之间的估值和均衡来决定的,不同的数据主体对数据产品有不同的估值。因此,数据产品定价需要系统地为数据市场中的各方,包括数据供方和数据需方等建立能够达成共识的价值评估原则。此外,通过数据价格向数据市场中的不同主体传递信号也很重要。

Data pricing is a significant challenge in data transactions,requiring the integration of essential knowledge from computer science,data science and management.This paper reviews typical data product pricing methods from the perspectives of computer science,data science and management,with a specific focus on the management perspective's“human-machine collaborative-based reference pricing(range)method”for data product transactions.The main finding is that existing methods have certain start-up conditions or are limited to specific application scenarios.Data pricing is determined based on the valuation and equilibrium among multiple parties,where different data entities have varying valuations of data products.Consequently,data product pricing requires the systematic establishment of value evaluation principles that can achieve consensus among all parties in the data market,including data suppliers and demanders.Moreover,signaling to different entities in the data market through data prices is crucial.