The well-instrumented process industry collects vast amounts of structured and unstructured data from its assets in real time. Some of this data gets stored as conventional time series data, while some is processed to generate alarms, alerts, and other types of unstructured data. Managing this big data which is rich in diversity, volume, veracity, and velocity, to generate actionable insights is a challenge that is best tackled through the use of advanced analytics. The area of advanced analytics has been expanding with the rapid rise of artificial intelligence (AI) tools that are capable of processing complex data types such as video and audio in real time. In this article, applications involving operational data and advanced analytics tools that are used to generate predictive insights are discussed. The case studies illustrate the different data types present in industry-time series data, alarm and event data, and image data- and the machine-learning methods used to analyze them in order to generate insights. The applications discussed cover a spectrum of advanced analytics techniques ranging from conventional time series analysis, spectral analysis, clustering, convolutional neural networks, to text analytics. In conclusion, some perspectives on the future role of advanced analytics and AI technologies in the process industry are shared.