基于“三环节”分析框架的数据资产审计风险研究

Research on Data Asset Audit Risks Based on the “Three-Link” Analytical Framework

摘要: 数据资产的无实物形态、可无限复制、价值动态波动等特殊属性，给审计工作带来严峻挑战。本文基于数据资产形成与入表过程，运用“数据资源化–数据资产化–数据价值化”三环节分析框架，系统识别各环节的审计风险类型与成因，并通过拓尔思案例进行实证验证。研究发现：数据资源化环节的核心风险在于数据来源合规性与权属清晰度；数据资产化环节突出问题是资产确认边界模糊，企业可能基于盈余管理动机进行选择性分类；数据价值化环节风险最为集中，涉及资本化条件判断、使用寿命确定及减值测试主观性。三环节风险层层传导，资源化环节的合规瑕疵会逐级放大后续环节的错报风险。拓尔思案例完整验证了这一传导机理，其大数据业务收入大幅下滑与数据资产账面价值持续增长的反向背离，构成重要预警信号。本文提出分层应对策略：资源化环节强化合规穿透与权属验证，资产化环节规范分类判断与资本化复核，价值化环节完善减值测试并引入专家工作。研究为数据资产审计实践提供了系统分析工具与操作指引。

Abstract: The unique characteristics of data assets—lack of physical form, unlimited replicability, and dynamic value fluctuations—pose significant challenges to auditing. This paper constructs a three-stage analytical framework of “Data Resourcing, Data Assetization and Data Valorization” based on the formation and balance-sheet recognition process of data assets, systematically identifies audit risk types and causes at each stage, and empirically validates the framework using the case of TRS Information Technology Co., Ltd. The findings reveal that: at the resourcing stage, core risks lie in data source compliance and ownership clarity; at the assetization stage, the primary issue is ambiguous asset recognition boundaries, where firms may engage in selective classification driven by earnings management motives; at the valorization stage, risks are most concentrated, involving capitalization condition judgments, useful life determination, and subjective impairment testing. Risks at the three stages are sequentially transmitted, with compliance deficiencies at the resourcing stage amplifying subsequent misstatement risks. The TRS case fully validates this transmission mechanism, where a sharp decline in big data business revenue contrasts with rising data asset book value, serving as a critical warning signal. This paper proposes tiered countermeasures: compliance penetration and ownership verification at the resourcing stage, standardized classification and capitalization review at the assetization stage, and improved impairment testing with expert involvement at the valorization stage. The study provides a systematic analytical tool and operational guidance for data asset auditing practices.