Data, and hence data quality, transcends all boundaries of science, commerce, engineering, and policy. Data quality has historically been addressed by controlling the measurement processes, controlling the data collection processes, and through data ownership. For many data sources being leveraged into data science, this approach to data quality may be challenged. To understand that challenge, an historical and disciplinary perspective on data quality, highlighting the evolution and convergence of data concepts and applications is presented.