The shortage of skilled workers is a barrier to applying data analytics. Augmented analytics is an approach to lower it by using machine learning to automate related activities and natural language applications to assist less-skilled employees. Public information about augmented analytics case studies in manufacturing is hardly available. Therefore, this article presents a related case study from the white goods industry. It focuses on a quality test lab in a production line where workers use a digital assistant prototype to interact with descriptive and predictive data analytics. This article derives a framework from this case study to organize how an assistant could augment analytics. The framework has five areas: training data modification, model training, starting an analysis, retrieval of results, and decision support. The latter is relevant to the other four areas and includes, for instance, suggesting options to customize analytics. Four scenarios of different complexity concretize the framework’s areas. Finally, this article outlines four questions for future research.