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In the context of the global digital revolution, data assets have become critical drivers of sustainable regional economic development. These assets encompass a wide range of resources, including big data, user profiles, and transaction records (Abid & Abid, 2023). Their scale and value within the digital ecosystem have grown significantly, playing a key role in stimulating economic growth. However, traditional accounting frameworks struggle to assess and quantify these assets accurately, often resulting in their incomplete representation in financial statements.
The process of developing data assets involves data acquisition, organization, purification, storage, and advanced analysis, extraction, and application. This complex process necessitates careful data collection, processing, and sharing, with a strong focus on data security and privacy (Andriani, 2022). Through the use of data mining, machine learning, artificial intelligence, and other technologies, large volumes of raw data are converted into actionable insights and valuable knowledge for businesses and institutions. Effectively utilizing data assets enables organizations to gain competitive advantages, foster innovation, and significantly enhance digital and sustainable economic development.
Addressing the gap in data-asset valuation requires the development of more precise and reliable evaluation and accounting methods. Improved accounting practices for data assets not only enhance the economic performance of enterprises and regions but also act as a catalyst for the maturation of the digital economy. Although entities are expected to disclose material data assets in compliance with accounting standards, the absence of a coherent valuation and accounting framework presents significant challenges (Baumgartner & Ambühl, 2023).
This paper investigates the treatment of data assets in the context of sustainable regional economic development and proposes a comprehensive evaluation method for their value. The research findings highlight that the optimized data asset processing method used for Guangdong Power Grid Co., Ltd. increased the value of data assets by 70% compared to intangible assets, with a 17.8% growth in total profit and a 3.67% reduction in asset-management costs. This method introduces innovations such as a comprehensive research perspective, consideration of data-asset characteristics, and the integration of risk management and privacy protection (Devamathan & Kamarasan, 2023).
In the digital economy, the optimized accounting treatment for data assets supporting sustainable development provides a theoretical basis for expanding data-asset theory and enriching the theory of a sustainable economy (Faridi et al., 2023). This paper refines asset-accounting methods and supplements the theoretical frameworks of sustainable regional economy and asset accounting. The practical application of the optimized method shows strong adaptability and practicality for effective decision-making and optimal resource utilization, promoting sustainable economic development and digital-economy transformation.
The study integrates the digital economy, sustainable regional economic development, and data-asset accounting into a comprehensive research framework. It also emphasizes data-asset disclosure, proposing relevant standards and guidelines to enhance transparency, which is crucial for enterprises and regulators in the absence of clear standards.
The aim of this study is to provide an in-depth assessment of the effectiveness and limitations of current data asset accounting treatment, with questionnaires revealing a number of shortcomings, including a lack of adaptability, unclear standards, and an inability to meet the needs of dynamically growing organizations. On this basis, we aim to comprehensively identify the shortcomings of existing approaches and lay the foundation for proposing a more robust and adaptable accounting framework. At the same time, we explore the feasibility and effectiveness of optimizing the accounting treatment of data assets through an example analysis, covering optimization measures such as capitalization and expensing of costs and adjustments to amortization methods. Our study aims to verify the usefulness of these optimization methods in improving the accurate treatment of data assets and promoting the sustainable development of the regional economy.