Invested in Data Assets—Legality, Hidden Dangers, and Risk Mitigation

数据资产具有不同于常规出资物的特性,而在数据产权制度尚未明晰的情况下,需要对数据资产被用以出资入股的合理性和合法性进行考察。数据资产符合公司法对出资形式的要求,其价值实现具有稳定性和持续性,可以被用以出资入股。但是,由于数据资产的价值评估方法存在争议、价值实现依赖于特定的应用场景、需要持续采取安全措施,公司须对数据资产出资入股采取必要的合理性判断和风险预防措施。为符合资本安全的要求,公司接受数据资产需要对出资义务是否完全履行进行评估和监督,建立健全公司内部的安全保障机制,从而使得公司可以对数据资产兼顾安全利用和有效利用,从而实现数据资源的高效流通。Data assets have different characteristics from conventional capital contributions, and in the absence of a clear data property rights system, it is necessary to examine the rationality and legitimacy of data assets being used for capital contribution and shares. Data assets meet the requirements of the Company Law for the form of capital contribution, and their value realization is stable and sustainable, and can be used to contribute capital and shares. However, due to the controversy over the valuation method of data assets, the dependence on specific application scenarios for value realization, and the need to continuously take security measures, the company must take necessary reasonable judgment and risk prevention measures for the investment of data assets. In order to meet the requirements of capital security, the company needs to evaluate and supervise whether the capital contribution obligation is fully fulfilled when accepting data assets, and establish and improve the internal security guarantee mechanism of the company, so that the company can take into account the safe and effective use of data assets, so as to realize the efficient circulation of data resources.

Authors

Publication Details

Journal
Open Journal of Legal Science
Published
2025-01-01
DOI
https://doi.org/10.12677/ojls.2025.135146
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Invested in Data Assets—Legality, Hidden Dangers, and Risk Mitigation

敬民 李
Open Journal of Legal Science
Scientific Computing and Data Management
article

Invested in Data Assets—Legality, Hidden Dangers, and Risk Mitigation

敬民 李
article en

Abstract

数据资产具有不同于常规出资物的特性,而在数据产权制度尚未明晰的情况下,需要对数据资产被用以出资入股的合理性和合法性进行考察。数据资产符合公司法对出资形式的要求,其价值实现具有稳定性和持续性,可以被用以出资入股。但是,由于数据资产的价值评估方法存在争议、价值实现依赖于特定的应用场景、需要持续采取安全措施,公司须对数据资产出资入股采取必要的合理性判断和风险预防措施。为符合资本安全的要求,公司接受数据资产需要对出资义务是否完全履行进行评估和监督,建立健全公司内部的安全保障机制,从而使得公司可以对数据资产兼顾安全利用和有效利用,从而实现数据资源的高效流通。Data assets have different characteristics from conventional capital contributions, and in the absence of a clear data property rights system, it is necessary to examine the rationality and legitimacy of data assets being used for capital contribution and shares. Data assets meet the requirements of the Company Law for the form of capital contribution, and their value realization is stable and sustainable, and can be used to contribute capital and shares. However, due to the controversy over the valuation method of data assets, the dependence on specific application scenarios for value realization, and the need to continuously take security measures, the company must take necessary reasonable judgment and risk prevention measures for the investment of data assets. In order to meet the requirements of capital security, the company needs to evaluate and supervise whether the capital contribution obligation is fully fulfilled when accepting data assets, and establish and improve the internal security guarantee mechanism of the company, so that the company can take into account the safe and effective use of data assets, so as to realize the efficient circulation of data resources.

Open Journal of Legal ScienceVol. 13(05)
Openalex Percentile: Top 88%
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.