Review on data valuation approaches

Abstract

Data is an essential asset of a company and has significant economic value. In order to fully exploit this value, it needs to be properly measured. Still, we are unable to numerically establish its worth. Challenges with data valuation are due to its values’ ambiguous and multi-dimensional nature. As a result, assessing the value of data is more complex than of conventional assets. Personal user data is an essential and topical aspect of data and is therefore used in this thesis as an example to illustrate challenges with valuation. There are no generally accepted methods for data valuation and there is especially little research on personal data valuation. To better understand the issue, this thesis investigates current data valuation approaches. This thesis is conducted as a literature review. To understand data valuation methods, the data value generation process, elements of data, its value drivers and economic laws effecting data will be discussed. Furthermore, the focus is on assessing the key models and their suitability to evaluating data. The key findings are that literature does not prefer any particular valuation model or approach but instead, the suitability of a method is dependent on use case and context. The intangible asset valuation framework stands out, as it is often discussed and used in data valuation context. In evaluating their data assets, companies should apply multiple methods from the intangible framework for the best result.