Introduction
Bringing out the meaning of data is becoming increasingly crucial with growing data volume. Modern data, especially from multiple sources, requires perceiving multiple dimensions simultaneously. The good news: data visualization techniques are maturing rapidly. Thoughtful implementation can quickly separate significant findings from irrelevant details, exploiting the human visual system to extract information from data quickly.
Types of Data Visualization
Data visualizations are mainly categorized as either:
Explanatory Visualization
Communicates key findings from datasets to audiences, highlighting predetermined insights.
Exploratory Visualization
Facilitates user exploration of datasets, allowing them to unearth their own insights.
Making Visualizations Effective
Whatever visualization type, explanatory or exploratory, effectiveness requires identifying your goal and helping your audience make sense of data easily and quickly.
Identifying the Ultimate Visualization Goal
Data visualization is the only analytical component your audience experiences. It's crucial to ask questions and set the right goal. Start with:
- Why am I looking at this data?
- What's important about this data?
- What do I need my audience to know or do with this data?
These questions establish the viewpoint your audience will seek while examining data. Your responsibility is creating a comfortable experience for audience data browsing through visualization.
Understanding Audience Perception
Your audience may look at data for:
- Comparing values
- Associating elements
- Ranking items
- Searching for specific data
- Locating elements
- Verifying information
According to Edward Tufte, influential visualization theorist: "Effective analytical design entails turning thinking principles into seeing principles." Design architecture should assist analytical thinking.
Leverage Pre-attentive Attributes
Iconic memory allows perceiving things without conscious realization. Leveraging pre-attentive attributes in visualizations creates visual hierarchy, facilitating audience navigation. In explanatory visualizations, pre-attentive attributes draw attention to make key points.
Without conscious knowledge of following information, users can visually distinguish elements at a glance.
Avoid What Humans Are Not Good At
Humans struggle to judge quantitative values in two-dimensional representations. Avoid:
- 2D area representations
- Angles in pie charts
- 3D effects (especially 3D pie charts)
These don't represent data in proportions our brain can accurately perceive. In 3D pie charts, visually larger slices may actually represent smaller values, causing misinterpretation.
Leverage Human Visual Perception
Human visual systems function per Gestalt perception principles:
- Proximity
- Similarity
- Enclosure
- Continuity
- Connection
These principles define how audiences perceive visualization. Using background color to group data points creates organization, making messages clearer to audiences.
Reduce Cognitive Load
Every visualization element adds cognitive load. Multiple colors demand attention, creating clutter. Use colors strategically to draw attention where points need emphasis.
Proper direct labels reduce eye movement between legends and charts. Guide audiences to draw attention easily and quickly, minimizing cognitive effort.
Conclusion
Making points through visualization requires deep thinking. Different chart types ask different questions. Bar charts request comparisons; pie charts request angle, area, and arc comparisons. Multiple ways exist to display the same data; your choice determines audience interpretation.
The goal is helping audiences understand data quickly and easily while making intended points clearly.