The development of lithium-ion batteries (LIBs) is characterized by a unique level of complexity in the manufacturing process. In particular, cause-effect relationships (CERs) between process parameters have a strong influence on the quality of a manufactured cell and thus on the ramp-up time. First approaches for discovery CERs in LIBs were expert-based and thus afflicted with a high degree of uncertainty. Therefore, data from a real battery production line has for the first time been systematically processed and analyzed using CRISP-DM. However, the approach shows shortcomings in the involvement of domain expert knowledge as well as in the accuracy of the applied models. Addressing these shortcomings, an interdisciplinary data analytics framework is presented using human-computer interaction (HCI). Moreover, the framework aims to improve data analysis with the help of expert knowledge and, conversely, sharpen the knowledge of experts through data analysis. Thus, the model provides a basis for automated fault detection, diagnostics, and prognostics. Implementation and validation of the framework was conducted using the data of an assembly line for prismatic LIBs at the BMW Group in Munich.