In the digital economy, data has become a central driver of enterprise value, yet prevailing accounting frameworks fail to recognize or report internally generated data as an intangible asset. This omission creates a significant disconnect between the financial statements of data-driven firms and their underlying economic reality. This study introduces the Extended Asset Recognition Model (EARM), a conceptual framework designed to enable the recognition, valuation, and disclosure of proprietary data assets in corporate financial reporting.
Grounded in existing accounting theory and informed by stakeholder perspectives, the model integrates asset recognition criteria with flexible valuation approaches including cost, market, and income-based methods tailored to the unique attributes of data. Through hypothetical applications involving firms such as Amazon and Meta, the paper demonstrates how data can be reliably valued and accounted for using structured assumptions and revenue attribution analysis. It also addresses challenges of verification, auditability, and ethical concerns around privacy and data governance.
The study concludes with policy recommendations for international standard-setting bodies, including the IASB and FASB, and emphasizes the need for cross-disciplinary collaboration to align financial reporting with the realities of the digital age. By enabling data to be treated as a measurable and reportable asset, this research contributes to the modernization of intangible asset accounting and enhances the relevance and transparency of financial statements in the 21st-century economy.