This paper presents a multivocal literature review (MLR) on DataOps, an emerging discipline that enhances data management and analytics through collaboration and automation. By analyzing academic and grey literature, we categorize DataOps concepts into four main areas: people, process, development, and infrastructure. We identify key benefits, including enhanced collaboration, reduced cycle times, and improved data quality. Additionally, we highlight significant challenges such as data silos, data quality issues, team structure complexities, and organizational culture barriers. This paper provides a foundational resource for researchers and practitioners interested in DataOps, outlining key implications for practice and future research.