Data quality plays a crucial role in the data governance of organizations, as it is essential to ensure that dataare fit for the purpose for which they are intended, whether for operational activities, decision-making processes, or strategic planning. As data silos begin to be integrated to form data spaces, guaranteeing data quality becomes a necessity to achieve a reliable collaborative ecosystem. Nevertheless, the concept of data quality remains ambiguous, with various definitions and interpretations offered in the literature, despite its importance. This lack of consensus has led to the need for a thorough review of the different data quality criteria used in scientific work. Therefore, this paper serves as a systematic survey aimed at exploring and consolidating diverse perspectives on data quality. By thoroughly analyzing existing literature, this study compiles a comprehensive set of 30 agreed-upon data quality criteria, with their respective names and definitions. These criteria act as avaluable resource for organizations seeking to establish effective data quality monitoring practices. Then, we expose challenges raised by collaborative data processing and highlight possible research directions where data quality plays a major role.