The amount of spatial data collected in agricultural fields has been increasing over the last decade. Advances in computer processing capacity have resulted in data analytics and artificial intelligence becoming hot topics in agriculture. Nevertheless, the proper processing of spatial data is often neglected, and the evaluation of methods that efficiently process agricultural spatial data remains limited. Yield monitor data is a good example of a well-established methodology for data processing that could be used as a guide to determine data processing strategies. However, data processing methods for proximal soil sensors (PSS) are not as well-known as for yield, even though sensors are widely used in precision agriculture and their data often applied in predictive models for soil spatial variability characterization. The main objective of this study was to identify suitable methodologies for processing PSS data and apply them to a particular dataset. It was determined that properly processing any spatial dataset required that the following steps must be taken: (1) data projection, (2) position offset correction, (3) global and (4) local filtering, and (5) interpolation. Based on a literature review, the most suitable methods for each step are listed and discussed, and frameworks are proposed. These methods were applied to 4 different types of PSS data (gamma ray spectrometry, ground-penetrating radar, galvanic contact and electromagnetic induction soil apparent electric conductivity). To evaluate the accuracy of each processing framework, a cross-validation procedure was used. Overall, the proposed batch processing framework improved the value of PSS data by highlighting spatial variability in the field that was previously masked by the presence of erroneous data. Also, when performing an analysis in the measurement’s maps, discrepancies were found between the raw versus processed data, thus, emphasizing the need for properly processed PSS datasets.