data visualization

“We are overwhelmed by information, not because there is too much, but because we haven’t learned how to tame it,” per Stephen Few, well-known authority on data visualization.

Stephen Few, along with Edward Tufte, another prolific expert on the topic, have been providing thoughtful dialog on the worth of data visualization long before the explosion of data visualization within business intelligence (BI) tools. Nonetheless, their insights are widely considered best practices, even for data visualization in BI dashboards.

Endless possibilities for combining disparate data sets previously deemed too costly and time-intensive to tackle, now exist via comprehensive BI tools that enable you to create simple dashboards. The challenge is selecting a tool that provides clear value in an easily understandable format once all that data is merged.

Enter data visualization. Data visualization makes sense of rows and columns of data by representing it in charts and graphs that are much more easily digested. The way in which the data is visualized, however, makes a significant difference. Some data visualization outputs are still difficult to translate, causing vague or unclear takeaways and delays in decision-making.

Key Principles of Effective Data Visualization

The list below is a summary of the core concepts that make data visualization most useful, as identified by Few and Tufte.

  • Clarify – set a clear objective that people care about
  • Simplify – present only the visualization style that is most appropriate for the type of data being analyzed
  • Compare – display side-by-side comparisons for easy absorption
  • Attend – draw the viewer’s attention to the important/relevant data
  • Explore – create visuals that leads the viewer to discover new things, not simply answer a specific question
  • View Data Diversely – enable multiple views of the same data to discover various insights
  • Ask Why – question why something is happening, don’t simply note that it is happening
  • Be Skeptical – encourage more question-asking vs. accepting the simple answer provided by the initial query
  • Respond – share the data you uncover to gain alternate perspectives and build collaboration
  • Detail – make large data sets coherent and reveal data at several levels of detail
  • Validate – data visualization graphs should speak for themselves but also provide access to backup information and raw data as proof points

The most productive BI tools cause you to think about the meaning of the data you’re looking at and not focus on the tool, mechanics, images, or anything other than the information at hand.