As industrial production progresses toward digitalization, massive amounts of data have been collected, transmitted, and stored, with characteristics of large-scale, high-dimensional, heterogeneous, and spatiotemporal dynamics. The high complexity of industrial big data poses challenges for the practical decision-making of domain experts, leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis. Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines, including data mining, information visualization, computer graphics, and human-computer interaction, providing a highly effective manner for understanding and exploring the complex industrial processes. This review summarizes the state-of-the-art approaches, characterizes them with six visualization methods, and categorizes them based on analytical tasks and applications. Furthermore, key research challenges and potential future directions are identified.