Data Visualization Research: How It Informs Design and Visual Thinking

Joshua Korenblat
Assistant Professor, Graphic Design
SUNY New Paltz

Design research aligns with the process of researching a data visualization project. Data visualization maps numbers to visual variables; many design projects, meanwhile, have concerns other than numbers and statistics. Yet the research process that contributes to a sound data visualization can offer valuable insights into visual thinking and storytelling. Data visualization is the end result of data analytics, an exploratory process that cultivates a mindset familiar to designers.

Curiosity guides this mindset: observational, descriptive methods allow the creator to understand a topic from multiple angles, ultimately honing clarity in communicating an idea. The process might at times proceed from details to a big picture; other times, from a big picture to details. This data analytics mindset dovetails with emergent processes in design thinking. In both processes, small sprints often yield results more optimal than a grand master plan.

Data analytics involves spatial visual thinking skills that designers—all of whom work with points, lines, planes, and color—have the ability to understand. One of the leading visualization packages for the open source statistic package R is called the “grammar of graphics,” akin to verbal and visual language. I will use an accessible information graphic that compares Presidential biographies at the time of first election. This case study will detail how the analytic process conducts along a circular track: gathering data, structuring it, finding an insight, and visualizing that insight in a memorable, authentic, and persuasive way for a specific audience. Designers, and designers interested in storytelling, can identify familiar experiences at each step of the process. For designers who have yet to work in data analytics and visualization, accessible methods of sketching with data can exercise observational skills and visual thinking processes that propel many design and teaching practices—even those unconcerned with data visualization as the end result.