What to read after Data Visualization?

Visualization is one of the most important components of research presentation and communication due to its ability to synthesize large amounts of data into effective graphics. It is easier for the brain to comprehend an image versus words or numbers making effective graphics an especially important part of academic literature. The increasing accessibility and quantity of data require effective ways to analyze and communicate the information that datasets contain in simple, easy-to-understand formats. Data visualization is a collection of two major components. First is data analysis and data presentation.

Many researchers in academia and industry spend their day sifting through data, combining multiple data sources, and finally getting data ready for the moment of truth: seeing it in a data visualization. Data visualizations are the culmination of all data crunching work—they are supposed to take long numeric lists and complicated Key Performance Parameters (KPPs) and present them in intuitive, easy-to-understand way, that is, if you choose the right visualization for your data. The problem is it is often challenging to choose the right visualization for the data people want to show. Do one want to compare values or analyze a trend? What is the best way to visualize one’s data so that it is easy to extract insights? Many people stop short there wondering if a chart, graph, or heatmap will best reveal the bottom line at a glance, or worse, default to a simple pie chart because that is what they are most familiar with. But, data visualizations need to clarify the information. Defaulting to the most common visualization can actually lead to a misinterpretation of data.