The Concept and Characteristics of Data Assets
DOI:
https://doi.org/10.54691/dhqez912Keywords:
Data Assets; Valuation Models; Income Approach; Hybrid Valuation; Asset Securitization.Abstract
This paper systematically investigates valuation models for data assets, exploring their conceptual characteristics, theoretical foundations, valuation methods, and associated challenges and prospects. First, it defines the core features of data assets, including legal controllability, scenario dependency, and value derivability, and reviews their classification systems as well as current management challenges related to ownership, valuation, and circulation. Second, building upon traditional asset valuation theories (cost, income, and market approaches), the study analyzes the unique attributes of data assets across technical, economic, and institutional dimensions, proposing an integrated "Technology-Economics-Institutions" tripartite valuation framework that incorporates dynamic adjustments and interdisciplinary methodologies. Furthermore, the paper elaborates on five valuation models: the income approach (e.g., excess earnings method enhanced by grey forecasting), the market approach (relying on the maturity of data trading platforms), the cost approach (focusing on historical and replacement costs), the risk-based approach (employing system dynamics to assess uncertainty), and hybrid models (optimized via machine learning). Finally, the research highlights that data quality, technological advancements, legal-ethical considerations, and financialization trends are pivotal to the future evolution of valuation systems. Recommendations include standardized governance, blockchain and AI applications, legislative clarity on ownership, and securitization innovations to advance dynamic and market-driven data asset valuation. This study provides theoretical and practical insights into value discovery and capitalization of data assets.
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