The uncertainty in geography (and in many other sciences) is one of the problems which clearly structured the cartographic debates in the last 20-30 years. One of the causes of cartographic uncertainty stems from inconsistent data series, a frequent source of inaccurate visual representations of statistical data. Our discussion begins with a specific case (the data series between 2011-2020 referring to the suicide phenomenon in Romania) and explores the ways in which inconsistent data series can be processed and represented through GIS tools to minimize the uncertainty included in the final maps. Framed within a wider field of thematic cartography focused on socio-economic variables, the discussion tests a series of methods for processing, discretizing, and representing information for the purpose of identifying good or poor practices of using the options offered by GIS. The research is based on the examination of several geostatistical analysis methods as a way to manage inconsistent data series and on the comparison of several methods capable of diminishing uncertainty and leading to coherent geo/cartographic representations. In addition, the research brings forward an examination of different cartographic techniques for representing statistical distributions and a comparison between advantages and disadvantages of using specialized programs such as PhilCarto and ArcGis Pro. PhilCarto and ArcGIS Pro are two software programs that can create graphical depictions, each having specific advantages and disadvantages relative to the user's needs and level of experience, and especially to his final targets. To achieve these targets, it is essential to choose the right software, and when using it, to select the most suitable cartographic representation method for the data series, especially if they exhibit the characteristics of statistical inconsistency. The achieved results show that the use of appropriate methods of statistical analysis and graphical representation can be very useful for working with inconsistent data series in GIS, but it is important to pay attention to good practices and avoid deficient ones, in order to obtain a correct and coherent representation of the data. These results can be useful for researchers, data analysts, and professionals working in the GIS field and beyond. With that being said, the researcher must be alert to less recommended practices in the analysis, processing, and representation of inconsistent data series (selective extraction of data to support a certain conclusion, elimination of exceptional data, incorrect or inaccurate use of statistical or cartographic techniques).