Pricing the Unpriced Asset: A Standards-Based Method for Valuing Enterprise Data under IAS 38 and IAS 2
2026-06-22 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors explain that current accounting rules make it hard to recognize and value data as an asset because data often doesn't meet specific criteria like being separable or having clear costs and benefits. They propose a two-step way to value data that is legally verified and contractually clear. The first step, called D-Val, calculates value based on the original cost adjusted over time, following existing accounting standards. The second step, A-Val, adds a commercial perspective by including factors like rarity and data quality, even though it can't yet be officially audited. This approach aims to help properly price data before mature markets for such data exist.
IAS 38intangible assetsdata assetscapitalisation criteriacost measurementfuture economic benefitsvaluationdata provenanceauditable valuationcommercial valuation
Authors
Natasha E. Blycha, James Myint, Dean Arden, Michael Small, Conor Blycha, Schellie-Jayne Price, Steve Bailey, Ryan Feng, Rachael Johnson, Adam Myer
Abstract
The recognition and measurement of data assets under current accounting standards presents significant challenges. While International Accounting Standard 38 (IAS 38) provides a framework for intangible asset recognition, data assets frequently fail to meet capitalisation criteria due to difficulties in demonstrating separability, establishing reliable cost measurement, and proving probable future economic benefits. The widespread failure to easily and reliably value data causes mispricing and allocative distortions across data and artificial intelligence markets. This paper introduces a two-layer valuation progression for authenticated data assets, that is, datasets that have met IAS 38 recognition criteria through established legal provenance and contractual boundaries. The first layer, D-Val, is the auditable cost-basis valuation consistent with IAS 38. D-Val is defined as D-Val = Cp * Avt, where Cp is the reliably measurable production cost and Avt is the appreciation or depreciation factor applied over time. Under prevailing interpretations of IAS 38, Av is constrained to values less than or equal to one absent an active market revaluation, rendering D-Val a strictly cost-less-amortisation figure. The second layer, A-Val, is a theoretically grounded commercial valuation that incorporates scarcity, rivalry, completeness, accuracy, and explicit premia for provenance authentication and independent audit. A-Val is not auditable as fair value under current practice but serves as a defensible commercial valuation during the period before active markets for authenticated data assets mature. As authenticated data markets mature parameter assumptions improve providing a foundation for iterative refinement of the model.