数据的要素化与资产化:理论辨析与实践探索

Data Elementization and Assetization:Theoretical Analysis and Practical Exploration

数据的要素化与资产化是数字经济时代的一个关键议题,在此背景下本文系统性地探讨了数据的要素化与资产化过程。文章首先界定了数据的概念及其属性,随后分析了数据要素化的内在条件和流通瓶颈,提出了政策、技术与监管三个维度的优化路径,并在此基础上界定数据要素化与数据资产化的内在关联。文章进一步聚焦于数据资产化的确认前提和规范流程,通过政策和案例梳理探讨了数据资产化的实践动态,警示数据资产泡沫,分析泡沫成因机制并提出政策建议。主要结论:一是数据要素化可尝试从政策层面的深化与创新、技术标准与认证体系的建立与完善以及监管层面的精细化与前瞻性等进行优化;二是尽管数据要素化与数据资产化关注的是不同维度的过程,但是从数据流通价值角度来看,二者相辅相成,共同推动数据形态演化和要素价值的释放;三是数据资产金融创新是数据资产化探索阶段的亮点,但仍需警惕由价值评估偏差、刷单与关联交易等市场操纵以及质押融资风险引发的数据资产泡沫。

Data elementization and assetization are a key issue in the digital economy era.Against such a backdrop,this article systematically explores the process of data elementization and assetization.It first defines the concept and attributes of data,then analyzes the intrinsic conditions for data elementization and the bottlenecks affecting its circulation,and proposes optimization paths from three dimensions:policy,technology,and regulation;on this basis,the article defines the internal connection between data elementization and data assetization.The article then concentrates on the prerequisites and standard procedures for data assetization,explores the practical advancements through policy and case reviews,cautions against data asset bubbles,dissects the causal mechanisms behind those bubbles,and offers policy recommendations.The principal conclusions are as follows:First,data elementization can be optimized through deepening and innovation of policies,establishing and improving technical standards and certification systems,and enhancing regulatory refinement and foresight.Second,although data elementization and data assetization address different process dimensions,they mutually reinforce each other from the perspective of data circulation value,jointly facilitate the evolution of data forms and the liberation of elemental values.Third,while financial innovation in data assets is a highlight in the exploration phase of data assetization,vigilance is still required against data asset bubbles caused by value assessment biases,market manipulations such as data brushing and related transactions,and risks associated with pledge financing.