An overview of data veracity issues in ship performance and navigation monitoring in relation to data sets collected from a selected vessel is presented in this study. Data veracity relates to the quality of ship performance and navigation parameters obtained by onboard IoT (internet of things). Industrial IoT can introduce various anomalies into measured ship performance and navigation parameters and that can degrade the outcome of the respective data analysis. Therefore, the identification and isolation process of such data anomalies can play an important role in the outcome of ship performance and navigation monitoring. In general, these data anomalies can be divided into sensor and data acquisition (DAQ) faults and system abnormal events. A considerable amount of domain knowledge is required to detect and classify such data anomalies, therefore data anomaly detection layers are proposed in this study for the same purpose. These data anomaly detection layers are divided into several levels: preliminary and advanced levels. The outcome of a preliminary anomaly detection layer with respect to ship performance and navigation data sets of a selected vessel is presented with the respective data handling challenges as the main contribution of this study.