Enterprise Value and the Value of Data

Abstract

Data is widely acknowledged as the essential capital asset of the modern economy, yet its value remains largely invisible in corporate balance sheets and understated in national economic accounts. This paper argues that conventional valuation approaches—particularly those based on the costs of datafication—capture only part of the story. While such expenditures enter GDP as investment in intangible assets, they do not reflect the substantial economic rents generated by the effective use of data within firms. These rents arise from data’s distinctive economic characteristics, including non-rivalry, combinatorial scalability, and its role in creating information asymmetries that give data-rich firms a competitive advantage. As a result, data contributes to enterprise value not through direct transactions, but by enhancing profitability, accelerating innovation through machine learning, and enabling the creation of machine knowledge capital. Drawing on trends in the US economy, the paper estimates that data rents alone account for over 2% of GDP—representing a layer of value in addition to the investment flows currently captured in GDP. This has profound implications for national accounting methodologies which underestimate the value contribution of data. It also flags risks for economic policy in small open economies that lack the scale to effectively capture data rents, since investing at less than critical scale may not recover costs and result in negative productivity outcomes. 

Keywords: data-driven economy, data valuation, enterprise alue, intangible assets, economic rents, machine knowledge capital, information assymetry, productivity and scale, national accounts

JEL Classification: E01, O33, L86, D23, D24, O38