In an age where information is abundant and data is king, the ability to make sense of complex datasets has become a critical skill. Raw numbers and statistics can be overwhelming, but through the art of data visualization, we can transform this data into insights, patterns, and stories that are easily digestible and actionable.
The digital revolution has ushered in an era where data is generated at an unprecedented pace. Every click, purchase, and interaction online generates valuable information. Businesses, governments, and individuals are sitting on mountains of data, and the challenge lies in turning this raw material into knowledge.
Humans are inherently visual creatures. Our brains are wired to process visual information quickly and efficiently. In fact, studies have shown that the human brain can process images in as little as 13 milliseconds. Compare this to reading text, which takes considerably longer and requires more cognitive effort.
The power of Visualization
Data visualization harnesses the power of our visual processing capabilities to represent data in graphical or pictorial form.
By doing so, it allows us to:
Spot Patterns: Visualization can reveal trends and patterns that might not be immediately apparent in a spreadsheet full of numbers. A line chart, for instance, can show how sales have fluctuated over time, making it easy to identify seasonal trends.
Highlight Anomalies: Unusual data points or outliers are often easier to identify in a graph or chart. This can be crucial in detecting errors or uncovering hidden opportunities.
Tell a Story: Data visualization is a storytelling tool. It can convey a message or narrative more effectively than a written report. A well-designed infographic can summarize complex data and communicate it to a wide audience.
Support Decision-Making: In the business world, data-driven decisions are highly valued. Visualizations can provide decision-makers with a clear picture of the current state of affairs, enabling them to make informed choices.
Types of Data Visualization
Data visualization comes in various forms, each suitable for different types of data and purposes:
Line Chart
Line charts are one of the most commonly used charts for comparing two data sets. Use line charts when the number of data points is high, and you want to show a trend in the data over time.
Use cases for line charts:
A company’s quarterly sales for the past five years.
The number of customers per week in the first year of a new retail shop.
Changes in a stock’s price from opening to the closing bell.
Best practices for line charts:
Label the axes and the reference lines used to measure the graph coordinates. It is common to plot time on the x-axis (horizontal) and the data values on the y-axis (vertical).
Use a solid line to connect the data points to illustrate trends.
Keep the number of plotted lines to a minimum, typically no more than 5, so the chart does not become cluttered and difficult to read.
Add a legend, a small visual representation of the chart’s data, that tells what each line represents to help your audience understand what they are viewing.
Always add a title.
Column Chart
Column charts are positioned vertically, as shown in the figure. They are probably the most common chart type used to display the numerical value of a specific data point and compare that value across similar categories. They allow for easy comparison among several data points.
Use cases for column charts:
Revenue by country, as shown in the chart example.
Last year’s sales for the top four car companies in the US.
Average student test scores for each of six math classes.
Best practices for column charts:
Label the axes.
If the chart shows changes over time, plot the time increments on the x-axis.
If time is not part of the data, consider ordering the column heights to ascend or descend to demonstrate changes or trends.
Keep the number of columns low, typically no more than 7, so the viewer can see the value for each column.
Start the value of the y-axis at zero to accurately reflect the column's total value.
The spacing between columns should be roughly half the width of a column.
Bar Chart
Bar charts are similar to column charts, except the data is horizontally displayed. Bar charts also allow for easy comparison between several data points. The data point labels on the horizontal bar chart are on the left side and are more readable when the label contains text rather than values.
Use cases for bar charts:
Gross domestic product (GDP) of the 25 highest-grossing nations.
The number of cars at a dealership sold by each sales representative.
Exam scores for each student in a math class.
Best practices for bar charts:
Label the axes.
Consider ordering the bars so that the lengths go from longest to shortest. The data type will most likely determine whether the longest bar should be on the bottom or the top to best illustrate the intended pattern or trend.
Start the value of the x-axis at zero to accurately reflect the total value of the bars.
The spacing between bars should be roughly half the width of a bar.
Pie Chart
Pie charts show parts of a whole. Each slice, or segment, of the “pie”, represents a percentage of the total number. The total sum of the segments must equal 100%. A pie chart displays the different values of a given variable. Some use cases that illustrate comparing the information with a pie chart include:
Annual expenses categories for a corporation (e.g., rent, administrative, utilities, production)
A country’s energy sources (e.g., oil, coal, gas, solar, wind)
Survey results for favorite type of movie (e.g., action, romance, comedy, drama, science fiction)
Some best practices for pie charts include:
Keep the number of categories minimal so the viewer can differentiate between segments. Beyond ten segments, the slices begin to lose meaning and impact. If necessary, consolidate smaller segments into one segment with a label such as “Other” or “Miscellaneous”.
Use a different color or darkness of grayscale for each segment.
Order the segments according to size.
Make sure the value of all segments equals 100%.
Scatter plot
Scatter plots are very popular for correlation visualizations or when you want to show the distribution, or all possible values, of a large number of data points. Scatter plots are also useful for demonstrating clustering or identifying outliers in the data. Some use cases that illustrate visualizing the distribution of many data points with a scatter plot include:
Comparing countries’ life expectancies to their GDPs (Gross Domestic Product).
Comparing the daily sales of ice cream to the average outside temperature across multiple days.
Comparing the weight to the height of each person in a large group.
Some best practices for scatter plots include:
Label your axes.
Make sure the data set is large enough to provide visualization for clustering or outliers.
Start the value of the y-axis at zero to represent the data accurately. The value of the x-axis will depend on the data. For example, age ranges might be labeled on the x-axis.
Consider adding a trend line if a scatter plot shows a correlation between x- and y-axes.
Do not use more than two trend lines.
