Information visualization is an increasingly important element of big data as it is the technology best able to convey the message emanating from the data. Here is a nice paper “Infovis and Statistical Graphics: Different Goals, Different Looks” (pdf) by Andrew Gelman (Professor of Statistics at Columbia University) and Antony Unwin that discusses the topic of information visualization. Gelman et al. write:
We are also disturbed that many talented information visualization experts do not seem interested in the messages of statistics, most notably the admonitions from William Cleveland and others to consider the effectiveness of graphical displays in highlighting comparisons of interest. We worry that designers of non statistical data graphics are not so focused on conveying information and that the very beauty of many professionally produced images may, paradoxically, stand in the way of better understanding of data in many situations.”
Gelman goes on to highlight differences in goals between information visualization and statistics: the former to “tell a story”, the latter to better highlight correlation and convey evidence. We can admire some of the beautiful visualizations that are produced these days (e.g. the New York Times has a stunning visualization of baseball data). But stories have an end, a lesson learned, and visualizations if used only artistically, can convey a misleading end. It’s exciting to think how powerful it could be if we could get sound/rigorous evidence based conclusions closely aligned with information visualization.