This issue, I’ll be digging into the daunting world of big data visualization, sometimes called “data viz.” This represents one of the four major application areas of big data techniques to I-O psychology and HR, alongside data gathering, data storage, and data analytics (Landers, Fink & Collmus, in press). Importantly, I’m distinguishing data visualization in the big data sense (data viz) from data visualization in the traditional SPSS-ish sense. “Visualizing data” is something we’ve been doing for a very long time with histograms, scatterplots, pie charts and so on. Data viz, in contrast, is a specific type of data visualization, one that focuses on interactive exploration of highly complex datasets. When you create a scatterplot, you’re trying to illustrate to someone the relationship between two variables. When you create a data viz, you’re trying to empower the viewers of that data viz to explore whatever particular relationships they’re personally interested in without much, if any, expertise in statistics required. In either case, the creator of data visualization must have expertise in both the subject matter being visualized and also in the art of visualization itself; historically, the training of scientists has focused more on the former, which may explain why scientists have not generally been very good at creating visualizations (Gelman, Pasarica & Dodhia, 2002).