Enterprise Data Valuation—A Targeted Literature Review

ABSTRACT As digital transformation redefines business models, enterprise value increasingly depends on intangible assets, especially data, rather than traditional physical assets like buildings and equipment. Traditional accounting has long focused on valuing physical assets based on their anticipated future economic benefits, distinguishing between operating and capital expenditures. However, intangible assets, such as data, are more complex to evaluate due to their dependence on business context, lifecycle, and specific uses. This literature review examines data valuation as an intangible asset for accurate enterprise valuation, relevant in investments, mergers, acquisitions, and understanding enterprise worth. The article highlights multiple emerging valuation approaches, including customer transactions, lifetime value, shareholder value, and customer equity, which provide a more nuanced view of data's worth. Advanced techniques like cooperative game theory, Shapley Values, machine learning, and meta‐learning frameworks are also explored as tools to quantify data value more precisely. Data quality is emphasized as a critical component of data valuation, with ongoing challenges due to regulatory uncertainties and inconsistent reporting practices. These complexities in data valuation signal a significant research opportunity to refine valuation methods as data continues to shape enterprise value across industries.