Dokic,DusanStein,HannahDemmler, DanielKrupka, DanielFederrath, Hannes2022-09-282022-09-282022978-3-88579-720-3https://dl.gi.de/handle/20.500.12116/39593Data represent a key resource for firm success, being used for strategic decision making and increasing business process efficiency. Despite the large potential of data sharing within data ecosystems or markets, firms are reluctant to do this, due to fear of losing competitive edges, lack of trust and ambiguity regarding data value. According to prior research, data value vastly depends on usage and quality. This paper focuses on data quality, as the lack of methods for quantifying data quality is one main reason for missing comprehensible data valuation approaches. We analyze 15 existing data quality indices (DQI) from theory and practice, identify relevant data quality dimensions and discuss metrics for applicability in data valuation approaches for data ecosystems and markets. Based on a quantitative study, we propose a DQI concept for developing transparent, objective data valuation methods, while providing a better understanding of inter- and intra-organizational data value.endata quality indexdata quality metricsdata economy10.18420/inf2022_861617-5468