数据定价研究综述

Survey of Data Pricing

数据定价是把数据作为资产并对资产进行定价的行为。在当前的数据市场中,由于买家和卖家之间几乎没有透明度、信息严重不对称,造成数据定价的混乱。如果存在数据定价的标准流程和评估方法,买家就能够以合理的价格获得需要的数据,同时也能改善数据交易市场的效率。检索了近年来关于数据定价的相关文献,在此基础上,总结了数据定价的定义、特点、发展概况和应用场景;阐述了数据交易流程和数据交易成本;重点阐述了影响数据定价的两个重要研究方向——数据定价策略和数据定价模型,全面评价了现有六种数据定价策略和五种定价模型的机制、优缺点及运用场景;最后,从数据价值评估、数据交易规则和数据隐私保护三方面分析了数据定价面临的挑战,并展望了数据定价的发展方向。研究成果将为今后的相关工作提供有价值的参考和依据。

Data pricing is the behavior of taking data as assets and pricing the assets.In the current data markets,there is little transparency and information asymmetry between buyers and sellers,resulting in confusion in data pricing.If there is a standard process and evaluation method for data pricing,buyers can obtain datasets they need with a reasonable price,moreover,it would also improve the efficiency of data trading markets.This paper retrieves the relevant literatures about data pricing in recent years,and then summarizes the definition,characteristics,development and application scenarios of data pricing.It describes the data transaction process and data transaction costs,and focuses on describing two important research hotspots that affect data pricing,data pricing strategy and data pricing model.It comprehensively evaluates the mechanisms,advantages,disadvantages,and application scenarios of the existing six data pricing strategies and five pricing models.Finally,it analyzes the challenges of data pricing from three aspects:data value evaluation,data transaction rule and data privacy protection.And it prospects the future development trends of data pricing.The research results of this paper will provide valuable reference and foundation for future relevant work.