By producing information in this manner we enable the data to be quickly and easily understood. A computational simulation is not, without modification, as capable of producing such results. At best it can produce output in the form of numerical data. This is a significant impediment to most areas of research as a method is required to transfer numerical results into coherent visual images that allow us to absorb the information in a more natural manner. To date, the most significant and effective technique for interpreting, and communicating the results of a computational simulation is with the aid of 3D computer graphics and animation.
Visualisation serves a vital role in the understanding of information.
As humans we interpret data in a slightly obtuse manner, we don't see specifics, we observe differences. We describe a conceptual idea rather than a specific incident. Thus, when analysing data, if we want to see errors in a single field of a multi-dimensional dataset, it might be possible just to look for the abnormalities in the data. If, however, we wanted to see how these abnormalities were changing over time, or what the contributing factors were, then the results could not be obtained as easily. We would instead need to express this information, with the aid of visualisation, in such a manner as to make these trends visible.