在大数据时代,爆发式增长的企业数据蕴藏着巨大价值,数据资产的入表工作也掀起巨大浪潮。企业数据资产管理是众多学者讨论的议题,如何构建更加科学、专业的管理体系,如何保障企业数据能最终在经济活动中发挥价值,以审计视角构建管理体系可以更好地回答这些问题。本文从生命周期理论出发,探索“资源化–资产化–资本化”三阶管理体系在企业数据资产管理中的关键节点,构建审计框架进行应对,发挥审计在企业数据资产管理体系中专业的监督、评价职能,维护企业数据资产的合规性、安全性,为日后数据资产发挥价值提供专业保障。In the era of big data, the explosive growth of enterprise data holds enormous value, and the entry of data assets into the SOFP has also sparked a huge wave. Enterprise data asset management is a topic of discussion among many scholars. How to build a more scientific and professional management system, and how to ensure that enterprise data can ultimately play its value in economic activities, can be better answered by constructing a management system from an audit perspective. Starting from the life cycle theory, this article explores the key nodes of the “resource—asset—capitalization” three-level management system in enterprise data asset management, constructs an audit framework to respond, and leverages the professional supervision and evaluation functions of audit in the enterprise data asset management system to maintain the compliance and security of enterprise data assets, providing professional guarantees for the future value realization of data assets.
随着生成式人工智能(GenAI)技术的飞速发展,其在审计领域的应用逐渐显现出巨大潜力。研究表明,GenAI不仅提升了审计效率和准确性,还优化了审计报告的编写过程,减少了人工错误,增强了数据分析和决策的透明度等等便利。然而,AI审计的实施也面临诸如数据治理、算法可信度、审计人员独立性等问题。为了确保AI审计技术的有效性和公正性,研究提出了加强数据保密、提高审计人员技术水平以及独立性以及提升算法透明度等解决措施。以促进审计技术的可持续发展和社会公信力。With the rapid development of Generative Artificial Intelligence (GenAI) technology, its application in the auditing field has gradually demonstrated enormous potential. Research shows that GenAI not only improves auditing efficiency and accuracy but also optimizes the process of drafting audit reports, reduces human errors, and enhances the transparency of data analysis and decision-making, among other benefits. However, the implementation of AI in auditing also faces issues such as data governance, algorithm reliability, and the independence of auditors. To ensure the effectiveness and fairness of AI auditing technology, research suggests solutions such as strengthening data confidentiality, enhancing the technical competence and independence of auditors, and improving algorithm transparency. These measures aim to promote the sustainable development of auditing technology and societal trust.
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