Theoretical Basis and Practical Path for Data Asset Valuation and Pricing

In the digital economy era, data has become a key production factor and strategic asset. However, due to its unique attributes—intangibility, non-competitiveness, dependence on specific scenarios and applications, and complex ownership—data assets face significant challenges in valuation and pricing, becoming a core bottleneck hindering the efficient circulation and value release of data factor markets. This article systematically explores the theoretical basis for data asset valuation and pricing, examining multiple theoretical perspectives and their applicability, including labor value theory, utility value theory, property rights theory, cost theory, and market supply and demand theory. At the practical level, this article deeply analyzes the advantages, limitations, and applicable scenarios of mainstream valuation methods, including the cost approach, market approach, and income approach. It also reveals key bottlenecks in current practice, including unclear data ownership definitions, lack of quality assessment standards, imperfect trading market mechanisms, and difficulty quantifying the value of application scenarios. Based on this, this article proposes a collaborative practical approach encompassing "theoretical support - methodological adaptation - institutional guarantees - and market improvement": deepening fundamental theoretical research to guide the development of data; promoting the integration and innovation of multi-dimensional valuation methods to enhance accuracy; accelerating the improvement of fundamental systems such as data rights registration, quality assessment, and security compliance to reduce transaction costs; and focusing on fostering a multi-tiered data trading market and exploring diversified pricing models to promote efficient circulation. Only through the coordinated advancement of theory, methodologies, institutions, and markets can we address the challenges of data asset valuation and pricing, fully unleash the value of data elements, and empower the high-quality development of the digital economy.