信息资源管理学报 ›› 2024, Vol. 14 ›› Issue (5): 4-13.doi: 10.13365/j.jirm.2024.05.004

• 特约稿 •    下一篇

三大数据资产化路径探析

马费成1,2,3 孙玉姣1,2,3 熊思玥1,2,3 王文慧1,2,3   

  1. 1.武汉大学信息管理学院,武汉,430072;
    2.武汉大学信息资源研究中心,武汉,430072;
    3.武汉大学大数据研究院,武汉,430072
  • 出版日期:2024-09-26 发布日期:2024-10-15
  • 作者简介:马费成(通讯作者),教授,博士生导师,研究方向为情报学理论与方法、信息经济、大数据分析与应用,Email:fchma@whu.edu.cn;孙玉姣,硕士生,研究方向为数据要素、数字经济、社交媒体信息传播;熊思玥,硕士生,研究方向为数据要素、社交媒体信息传播、用户信息行为;王文慧,硕士生,研究方向为数字经济、数字人文。
  • 基金资助:
    本文系国家社会科学基金“加快构建中国特色哲学社会科学学科体系、学术体系、话语体系”研究专项项目“新时代中国特色图情学基本理论问题研究”(19VXK09)的研究成果。

An Exploration of the Data Assetization Paths for the Three Major Types of Data

Ma Feicheng1,2,3 Sun Yujiao1,2,3 Xiong Siyue1,2,3 Wang Wenhui1,2,3   

  1. 1.School of Information Management, Wuhan University, Wuhan, 430072;
    2.Center for Studies of Information Resources, Wuhan University, Wuhan, 430072;
    3.Big Data Research Institute, Wuhan University, Wuhan, 430072
  • Online:2024-09-26 Published:2024-10-15
  • About author:Ma Feicheng(corresponding author), professor, doctoral advisor, research interests include information science theory and methods, information economy, big data analytics and applications, Email: fchma@whu.edu.cn. Sun Yujiao, master candidate, research interests include data elements, digital economy, social media information dissemination. Xiong Siyue, master candidate, research interests include data elements, social media information dissemination, user information behavior. Wang Wenhui, master candidate, research interests include digital economy, digital humanities.
  • Supported by:
    This paper was supported by the National Social Science Foundation's special project "Research on Basic Theoretical Issues of Library and Information Science with Chinese Characteristics in the New Era"(19VXK09) on "Accelerating the Construction of Disciplinary, Academic, and Discourse Systems of Philosophy and Social Sciences with Chinese Characteristics".

摘要: 数据资产化是抓住数字机遇,实现数据价值,推进经济社会数字化转型的关键环节。本研究认为,公共数据、企业数据和个人数据三大类数据是数据要素的主体。目前对三大数据的资产化实现路径研究不足,阻碍了数据要素的价值释放。本文系统梳理了数据资产的相关概念,深入探讨了来自公共、企业和个人的三大数据资产化路径。研究结果表明,公共数据可以面向政府内部或供给社会使用,分别借助共享和开放、授权运营三种路径产生社会效益或经济利益,形成公共数据资产;企业在持有数据或不持有数据两种权属配置下,可以对数据进行不同程度的加工、处理,从而完成数据价值的深挖和数据利益的再分配,形成存货、无形资产等类型的数据资产;个人数据资产化的相关实践较少,主要依靠供需双方直接交易或委托数据中介机构交易两种路径完成市场化流转,形成个人数据资产。通过对这一议题的探讨,研究期望为数据要素价值实现和数据资源有效配置提供理论指导和实践借鉴。

关键词: 数据资产化, 数据主体, 价值实现, 数据要素, 公共数据, 企业数据, 个人数据

Abstract: Data assetization is a key stage in grasping digital opportunities, realizing the value of data, and promoting the digital transformation of the economy and society. This paper argues that public data, enterprise data and personal data are the major subjects of data elements. However, current research on the realization path of data assetization for the three major types of data is insufficient, hindering the release of the value of data elements. In this paper, we systematically review the relevant concepts of data assets, and delve into the path of data assetization from the three major data subjects: public, enterprise and individual. The results shown that public data can serve internal government needs or be supplied to the society, generating social benefits or economic benefits through sharing and opening, and authorized operation, thus forming public data assets. Enterprises, depending on whether they hold data ownership, can carry out different degrees of processing and handling of data, so as to complete the deep excavation of data value and redistribution of data benefits, forming inventory, intangible assets and other types of data assets. The practice related to personal data assetization is limited, mainly relying on two paths: direct transaction between suppliers and demanders or entrusted transaction by data intermediaries to complete market circulation and form personal data assets. Through in-depth exploration of this topic, this study aims to provide theoretical guidance and practical reference for the value realization of data elements and the effective allocation of data resources.

Key words: Data assetization, Data subjects, Value realization, Data element, Public data, Enterprise data, Personal data

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