Lightning can cause human and livestock casualties, infrastructure damage, and flight disruptions all across the world. By timely and accurately predicting its occurrence, preventive measures can be put in place without over-disrupting the daily routines. Efficiently and accurately predicting the occurrence of lightning requires reliable data, specially when the model is developed using data-driven approaches. In our previous work, we have focused on the data from stand-alone weather stations all across Switzerland and we have evaluated their data quality by comparing the conformance of the collected data to Benford’s Law. We have used the Jensen–Shannon and Wasserstein distances as metrics to evaluate the similarities between the dataset and Benford’s law. In this work, we have focused on the data gathered by geostationary satellites, namely the Geostationary Lightning Mapper (GLM) sensor installed on the GOES-16 satellite stationed in the GOES-east position over the continent of America in 2023. The analysis show that when the number of flashes is grouped into intervals of about 20 seconds to 30 minutes, the data distribution follows Benford’s law. The findings of the work suggest that Benford’s law may potentially be used to assess the quality of lightning data recorded by satellite sensors.