The visualization of data is for making interpretation more intuitive. However, this is not the only reason for the method. More often than that, the difficulty of decoding data that the visualization possesses in it could vary, such as an academic data analysis which is not geared to the general non-professional public. Therefore, according to the reading, the essesnce of data visualization can be said re-processing data set to drive to the certain goal considering the nature of data visualization, which is needing those interprete it.
Taking the intent of visualization into consideration, there is two more agendas coming up. First is the methodology. Sometimes the goal of visualization is to make things memorized by reader. In that case the common belief that the simpler the data is, the better visualization it is. Often times, extra visuals considered to be a junk is helpful to make a strong mark on readers perception. That's not the only case for extra visual elements. When a visual deliverables is oversimplified, it rather sometimes hinders readers to grasp the data. Pursuing an optimized point where there is no excessive information but having proper amount of visual elements which lead readers to reach to a certain level of easiness is the key for visualization.
Second is the editing of the data set. It could raise the ethical issue. When a data set is processed based on an intent, the possibility of misleading increases accordingly. However, it doesn't mean that honest data is always the right and appropriate way. Being obssesed with the honest quality of data often times makes readers lose the focus on the most noteworthy point of the data. If the point could be highlighted efficiently by editing and cropping out unnecessary parts, the editting is not deemed as a misleading. Rather it is a guidance of data analysis.