Requirements for Data Valuation Methods

Abstract

Data is considered the most significant intangible asset for the 21st century enterprise. Serving as key asset for ever-increasing digital transformation and entrepreneurship, they ensure economic success through empowering new technologies, services and business models. Despite their high relevance, there exist neither consistent valuation methods nor specific requirements for developing such methods. Data valuation is crucial in order to better understand their value and, for example, incorporating them into financial statements. Existing literature indicates relationship between data value and quality. Thereupon, we conducted semi-structured expert interviews to gain insights on data valuation methods in connection with data quality. This results in 11 requirements for data valuation methods and seven value-driving quality criteria. Furthermore, several challenges for future data valuation are derived from the empirical results.