Even the simplest data set can be visualized from multiple perspectives. Not only our options are situation-dependent, but the criteria we use to evaluate them are subject to discussion. More than a set of algorithms to be applied in each situation, data visualization is a flexible visual language with multiple purposes, where “speakers” can develop their unique style. When paired up with technology, this openness somewhat blurred the traditional divide between exploration and explanation / communication in data visualization. Other fault lines emerged in recent years: tools (programming languages vs. point-and-click applications), interaction (dynamic vs. static), medium (analog vs. digital). In a context of an ever-increasing volume of data, and more types of data that could be visualized, more people are entering the field. Some have a less traditional profile (graphic designers, journalists), while others feel the need to add visualization and design to their core skills (statisticians).