By David Huer | June 2025
Recognizing Data as Inventory: A Step Toward Accounting Clarity
(2022) Xiong, F., Xie, M., Zhao, L., Li, C., & Fan, X. (2022). Recognition and Evaluation of Dats as Intangible Assets. SAGE Open, 12(2): https://doi.org/10.1177/21582440221094600
A Longstanding Gap in Financial Reporting:
As covered previously in this blog, internally generated data is routinely used to drive advertising, pricing, and business strategy—yet is often absent from balance sheets. Under IFRS and GAAP intangible assets must be identifiable, controlled, and expected to generate future economic benefits. Despite many datasets meeting these criteria, recognition has lagged due to challenges in valuation and disclosure practices.
In this 2022 article, Feng Xiong and colleagues proposed that enterprise data should be explicitly recognized under China’s accounting standards—similar to the treatment already permitted by IFRS and GAAP. Their analysis provides a structured approach to valuation and calls for formal inclusion of qualifying data assets in financial reporting.
Valuation Approaches:
The authors reviewed three standard methods for valuing data assets:
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Cost Approach: Captures direct and indirect costs to collect, clean, and maintain data. While straightforward, it may undervalue reusable or high-impact datasets.
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Net Present Value (NPV): Estimates the discounted future benefits of data-driven activities. This is particularly relevant for companies with subscription models or recurring data-based services.
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Market Approach: Derives value from data marketplace transactions or similar third-party pricing. This method is still developing, especially for proprietary or non-tradable datasets.
Case Example: Hithink RoyalFlush
Hithink RoyalFlush, a Chinese fintech firm, generates substantial revenue from data-driven services, including telecom tools, advertising, and software subscriptions. Yet its financial statements did not list data assets. Given the proprietary nature of its data, the market approach is unsuitable. The cost method underrepresents value due to repeated reuse. The authors proposed applying the NPV-based approach to more accurately reflect the strategic role and long-term value of these assets.
Implications for Financial and Strategic Reporting:
Recognizing internally generated data as intangible assets can improve reporting accuracy and better reflect a company’s economic reality. Benefits include:
- Closer alignment between business value and reported assets
- Improved transparency for investors and analysts
- More robust valuation in mergers and acquisitions
- Greater support for monetization and licensing strategies
However, recognition must be grounded in reliable valuation and meet existing accounting criteria, especially regarding measurability and probability of future benefit.
Key Takeaways
The authors argue that recognizing data as intangible assets improves the accuracy and usefulness of financial reporting, particularly for firms whose business models rely heavily on data analytics. They emphasize that existing accounting principles already support this recognition, and propose three valuation methods—cost, NPV, and market-based—each suited to different business contexts.
They call for further research to refine when and how such recognition should apply, and they caution that internal auditors will play a critical role in ensuring firms apply these principles ethically and within legal boundaries. Ultimately, their work advocates for accounting systems that reflect the strategic importance and monetizable value of data in the modern digital economy.
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Postscript: Regulatory Developments
In 2023, China implemented new accounting rules to allow enterprise data to be included on the balance sheet (1). The change is designed to help companies more accurately reflect the financial value of their data assets—particularly in the context of sales or licensing.
The new approach defines data as an intangible asset that is organized into two categories:
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“Intangible Assets”: Data used internally for strategic or operational purposes
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“Inventory”: Data packaged for sale (e.g., APIs, datasets, analytic products): Note: This classification is an accounting mechanism to support reporting and valuation. It allows companies to recognize saleable data assets as “inventory” for the purposes of revenue tracking, cost-of-goods-sold (COGS), and gross margin accounting.
Data's fundamental nature ("intangibility") does not change.
