大数据定价方法的国内外研究综述及对比分析

doi:10.11959/j.issn.2096-0271.2021063

摘要/Abstract

摘要：

大数据独特的价值特征导致数据定价问题复杂，尽管研究者对此展开了大量研究，但大多角度单一且缺乏实际应用性。鉴于此，对大数据定价方法进行了综述，梳理出成本导向、市场导向、需求导向、利润导向以及基于生命周期定价的5种定价类型，对比了成本法、协议定价、市场法、收益法、基于质量以及基于查询的定价6种主流定价方法的优劣势；最后通过大数据定价流程分析进一步展现了不同定价方法各自的特点，并对数据定价方向进行了展望，以期为今后的相关研究提供一定的参考。

关键词: 大数据产品, 大数据资产, 数据定价, 定价模型, 定价策略

Abstract:

Due to the value characteristics of big data itself, the problem of data pricing is complicated.Although researchers have conducted a lot of research on this, most of them have a single angle and lack a certain practical application.In view of this, the big data pricing methods were reviewed, five types of pricing were sorted out: cost-oriented, market-oriented, demand-oriented, profit-oriented, and life-cycle-based pricing.The advantages and disadvantages of the six mainstream pricing methods were compared: cost method, agreement pricing, market method, income method, quality-based and query-based pricing.Finally, through the analysis of the big data pricing process, the characteristics of the different pricing methods were further revealed, and the data pricing direction was forecasted.The article aims to provide some reference for future related research.

Key words: big data product, big data asset, data pricing, pricing model, pricing strategy

中图分类号:

刘枬, 郝雪镜, 陈俞宏. 大数据定价方法的国内外研究综述及对比分析[J]. 大数据, 2021, 7(6): 89-102.

Nan LIU, Xuejing HAO, Yuhong CHEN. A review and comparative analysis of domestic and foreign research on big data pricing methods[J]. Big Data Research, 2021, 7(6): 89-102.

图/表 3

表1

表2

主流大数据定价方法对比"

定价方法	定价原理	适用范围	优点	缺点
成本法	依据是成本价值论，将重新获取相同数据的重置成本作为数据的评估价值	价值难以量化且成本容易计量的情况；不以交易为目的的情况，如政务数据	容易把握和操作	· 成本量化较难，无法计算成本在单位数据上的均摊；
成本法	依据是成本价值论，将重新获取相同数据的重置成本作为数据的评估价值	价值难以量化且成本容易计量的情况；不以交易为目的的情况，如政务数据	容易把握和操作	· 成本和收益对应性弱，价格估算偏低
协议定价	基于博弈论，数据买卖双方通过协商，对价格达成共识	成本和收益难以量化的情况、买卖双方价值估值不一致的情况、数据针对性较强的情况	操作简单方便、易被双方接受、成交率较高	·交易效率低；
				·由卖方主导，忽视数据效用；
				· 买卖双方信息不对称，估值易出现偏差
市场法	依据均衡价值论，以市场中可比参照物的市场价格为基础进行调整得到估值	市场成熟、活跃、交易案例多、易找到可比案例的情况	能反映供求关系等真实市场情况，定价公平客观	· 目前数据市场不成熟，交易案例少；
				· 数据产品个性化程度高，难以寻找可比案例；
				· 修正系数难确定且难以修正全面
收益法	依据效用价值论，将数据的预期收益现值作为数据估值	预期收益确定且可量化的情况，更适用于数据买方对数据估值	考虑数据使用价值和时间价值因素，反映真实数据价值	·预期收益难预测、难量化；
收益法	依据效用价值论，将数据的预期收益现值作为数据估值	预期收益确定且可量化的情况，更适用于数据买方对数据估值	考虑数据使用价值和时间价值因素，反映真实数据价值	·定价偏主观，折现率确定困难
基于质量的定价方法	依据效用价值论，将数据质量作为数据成本和消费者效用的决定性因素	适用于对质量维度要求较低且质量维度容易量化的数据	考虑了数据自身的价值和消费者效用，定价透明公平	·质量非价值唯一的决定因素；
基于质量的定价方法	依据效用价值论，将数据质量作为数据成本和消费者效用的决定性因素	适用于对质量维度要求较低且质量维度容易量化的数据	考虑了数据自身的价值和消费者效用，定价透明公平	· 数据质量维度以及质量维度间的关系难以量化
基于查询的定价方法	本质是差异化定价，设定元组价格，由元组生成满足用户查询的最小视图，实现任意查询定价	存储在结构化或者非结构化数据库中的待交易数据	定价灵活，对任意查询自动定价，保证了用户的自由购买权	· 数据单元本身几乎无价值，以此生成的数据价格缺乏说服力；
				·算法复杂，可操作性低；
				· 预先设置的视图无法覆盖新生成的数据

表2

图1

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定价类型	DaaS	数据产品	数据资产
成本导向	固定费率、基于容量的定价、免费增值	捆绑定价	成本法
顾客导向	未发现此类定价研究	基于数据特征及质量的定价	收益法
		基于客户感知价值的定价
		信息熵定价
市场导向	未发现此类定价研究	基于博弈论的协议定价	市场法
		（拍卖、讨价还价等）
利润导向	公司订阅	基于版本的定价	未发现此类定价研究
	基于数据类型的定价	基于查询的定价
		捆绑定价
基于生命周期	未发现此类定价研究	生命周期分阶段定价	实物期权法
		生命周期动态定价