Analyzing data visualization involves a systematic process to understand the story the data is telling and extract meaningful insights. It's about looking beyond the colors and shapes to comprehend the underlying information and its implications.
At its core, analyzing data visualization requires carefully examining the components of the visual and interpreting the data presented within its context.
Steps for Effective Data Visualization Analysis
Based on established practices, including the steps outlined in the Wake Forest University Libraries guide on interpreting data visualization, here's a breakdown of how to effectively analyze data visualizations:
1. Identify the Main Purpose
- What is the chart meant to convey? Start by understanding the overall objective. What question is the visualization trying to answer, or what specific point is it trying to make?
- Example: A bar chart might aim to show sales performance across different regions. A line chart might illustrate trends over time.
2. Examine the Axes
- Identify information on each axis. Understand what variables or categories are being represented on the x-axis (typically horizontal) and the y-axis (typically vertical).
- Example: In a scatter plot, the x-axis might be "Product Cost" and the y-axis "Customer Rating."
- Identify the range covered by each axis. Note the minimum and maximum values on each axis. This helps understand the scale and the scope of the data being presented. Be mindful of truncated axes which can sometimes be misleading.
- Example: If the y-axis shows "Revenue" from $100,000 to $1,000,000, you know the visualization focuses on higher revenue figures.
3. Look for Patterns and Trends
- Identify significant patterns or trends. Observe how the data points behave collectively. Are there upward or downward slopes in a line chart? Are certain bars consistently higher or lower? Does data cluster in certain areas?
- Example: A line chart showing website visits might reveal an increasing trend over several months, or a cyclical pattern with dips on weekends.
4. Spot Averages and Exceptions
- Look for averages and/or exceptions. Identify typical values or central tendencies within the data. Simultaneously, look for outliers – data points that deviate significantly from the norm.
- Example: In a chart showing employee salaries by department, you might notice an average salary level for most departments, but one department shows a significantly higher average (an exception).
5. Pay Attention to Emphasis
- Look for bold or highlighted data. Visualization designers often use color, size, labels, or other visual cues to draw attention to specific data points, categories, or findings. Understand why these elements are emphasized.
- Example: A bar representing the most profitable product might be highlighted in a distinct color to make it stand out.
6. Read the Specific Data
- Read the specific data points. While patterns are important, sometimes the exact values are crucial. Look at data labels, tooltips (in interactive visualizations), or accompanying tables to get the precise figures.
- Example: Instead of just seeing a trend of increasing sales, you might need to read the label on a specific bar to know that sales in March were exactly $150,000.
By following these steps, you move from a superficial glance to a deep understanding of the data story embedded within the visualization.
Table Summary of Analysis Steps
| Step | Focus | Key Questions |
|---|---|---|
| 1. Main Purpose | Overall message/goal | What is this trying to tell me? |
| 2. Examine Axes | Variables, categories, and their scale | What is on X & Y? What are the min/max values? |
| 3. Patterns/Trends | General direction or behavior of data | Is it increasing, decreasing, cyclical, clustered? |
| 4. Averages/Exceptions | Typical values and significant deviations | What's normal? What's significantly different? |
| 5. Emphasis | Visually highlighted elements | Why are these points/sections standing out? |
| 6. Specific Data | Exact numerical or categorical values | What are the precise figures or labels? |
Analyzing data visualization is a critical skill for interpreting information effectively in various fields, from business to science. It empowers you to question the data, identify potential insights, and make informed decisions.
