18 Types of Data Visualization Techniques to Tell Your Story

Nexa Lab Blog – Data visualization is a powerful tool to interpret complex datasets, allowing businesses and individuals to extract actionable insights more efficiently. Data visualization simplifies information interpretation by representing it in visual formats such as charts, graphs, and maps, making it more accessible to a wide range of people.

But what is the best way to present your information? Let’s look at the different types of data visualization techniques, as well as the four pillars that ensure your data speaks loudly.

Fundamental Types of Data Visualization

Some of the most popular forms of data visualization techniques are bar, chart, and map visualizations. There are, however, a lot of variations. For instance, there are numerous visualisation techniques under the bar and chart category, such as area charts, stacked bar charts, and line charts.

With so many types of data visualization, it’s understandable that you might feel overwhelmed at first. However, Atlassian categorises all types of data visualization neatly into 3 categories: the foundational four, common variations, and specialist charts.

Let’s keep reading to explore each type of it.

The Foundational Four

Stephen Few proposes four major encodings for numeric values in his book Show Me the Numbers, which use bars, lines, points, and boxes to indicate positional values. So we’ll begin with four basic chart types, one for each of the value-encoding methods.

1. Bar Chart: A bar chart displays values as the length of bars, each representing a measured group. Vertical bar charts, also known as column charts, can be oriented either vertically or horizontally. Horizontal bar charts are useful when there are a lot of bars to plot or the labels need more space to be legible.
2. Line Chart: This type of data visualization depicts value changes over time. The movement of the line up or down results in positive and negative changes, respectively.
3. Scatter Plot: This data visualization uses two axes to display values for two numerical variables. Scatter plots are a versatile way to demonstrate the relationship between the plotted variables, whether it is strong or weak, positive or negative, linear or nonlinear.
4. Box Plot: The distribution of values within measured groups is summarised using boxes and whiskers in a box plot. The areas where the majority of the data is located are indicated by the box and whisker ends’ positions.

Common Variations

Changes in the way encodings are used or the inclusion of additional encodings can result in new chart types. Secondary encodings such as area, shape, and colour can be used to add new variables to more basic chart types.

Adding that new area results in different types of data visualization techniques.

5. Histogram: A histogram can be created by pushing the bars together in a bar chart to represent groups that are actually continuous numerical ranges. The distribution of variables in your data is shown by the patterns of the bar lengths in histograms, which typically match counts of data points.
6. Stacker Bar Chart: A stacked bar chart is a variation of the standard bar chart in which each bar is divided into several smaller bars according to the values of a second grouping variable. This enables you to show a relative breakdown of each group’s total into its component parts in addition to comparing primary group values like in a standard bar chart.
7. Grouped Bar Chart: The grouped bar chart would be produced, however, if the sub-bars were arranged side by side into clusters instead of stacked. Though it does a far better job of allowing comparisons of the sub-groups, the grouped bar chart does not allow comparisons of the totals of the primary groups.
8. Dot Plot: Similar to a bar chart, a dot plot displays values for various categorical groupings but encodes values according to the location of the point rather than the length of the bar. When comparing across categories, dot plots come in handy, but the zero baseline is neither helpful nor informative.
9. Area Chart: A concept from the bar chart is added to an area chart by adding shading between the line and a baseline. An area chart begins with the same foundation as a line chart, which consists of value points connected by line segments. When paired with the idea of stacking, this chart is most frequently used to illustrate how a total has changed over time as well as how the contributions of its constituent parts have changed.
10. Dual-axis Chart: This data visualization technique combines two distinct charts together with a common horizontal axis, but they may have distinct vertical axis scales (one for each component chart). This includes the context of the horizontal axis variable and can be helpful in displaying a direct comparison between the two sets of vertical values.
11. Bubble Chart: A scatter plot can also be modified to illustrate the relationship between three variables. Points that belong to different groups can be identified by distinct colours or shapes when the third variable is categorical. When the third variable is numeric in nature, that is where the bubble chart comes in.
12. Density Curve: An alternative to using a histogram to display data distributions is to use a density curve, also known as a kernel density estimate. Each data point contributes a small volume of data, which, when combined, forms the density curve, as opposed to gathering data points into frequency bins.
13. Violin Plot: The violin plot is an alternative to the box plot for comparing value distributions between groups. Each set of box and whiskers in a violin plot is swapped out for a density curve centred around a baseline.
14. Heatmap: A grid of values based on two relevant variables is displayed in the heatmap. The grid is produced by breaking each axis variable into ranges or levels, much like in a histogram or bar chart. The axis variables can be numerical or categorical.

Specialist Charts

For specific use cases, there are many more charts available that encode data in different ways. Yet, a few of these charts have enough common use cases to make them indispensable to be aware of.

15. Pie chart: The pie chart and its cousin, the doughnut plot, are excellent at informing the reader that the part-to-whole comparison should be the primary takeaway from the visualization. Although quite popular, this type of data visualization is regarded as specialised because it represents values as sliced areas in a circular form, which can often make it challenging to determine precise slice sizes.
16. Funnel chart: In business contexts where users or visitors need to be tracked in a pipeline flow, funnel charts are frequently used. The graph displays the number of users who pass through each stage of the monitored process based on the funnel’s width at each stage division.
17. Bullet chart: The bullet chart enhances a single bar by adding markings for contextualising its value. This usually consists of a perpendicular line displaying a target value, but it can also include background shading to provide additional performance benchmarks.
18. Map-based plots: With this technique, values are filled into regions on a map rather than being plotted in a grid as is the case with heat maps, which use colour to demonstrate values.

Knowing the various types of data visualization techniques can help you decide which one will best help you communicate your story through data. It’s critical to understand that data visualization has numerous advantages, including simplifying complex data sets and making it easier to identify trends and patterns.

Explore other benefits in our previous post, 5 Advantages of Data Visualization: With Best Practices to Implement It.

What are the 4 pillars of data visualization?

While there are many rules for data visualization, one that is quite intriguing is the 4 pillars of data visualization. Noah Illinsky, a former visualization expert at IBM, made the four pillars of data visualization popular. On his presentation, he explains the four pillars of data visualization:

• Purpose: This is the ‘why’ of the visualization. It involves understanding why we are creating this visualization and who it is for.
• Content: This is the ‘what’ of the visualization. It involves determining what data matters and what relationships matter.
• Structure: This is the ‘how’ of the visualization. It involves figuring out how to best reveal those data and relationships.
• Formatting: This involves the look & feel of the visualization and how it will be consumed.

Each pillar has a significant impact on your visualization, so it’s important to consider them in this order. They provide a framework for the design process and ensure the visualization effectively communicates the intended message.

In addition to the 4 pillars of data visualization, there are numerous other rules and principles that can assist you in creating an effective data visualization.

Read more in our previous post, Data Visualization Principles: Key Concepts for Effectively Communicating Insights.

Conclusion:

A wide variety of approaches are included in data visualization, each with specific advantages for drawing conclusions from the data. Organisations and individuals may fully utilise data visualization to support educated decision-making and accomplish their objectives by utilising the four pillars of purpose, content, structure, and format.

Ready to take your data visualization to the next level?

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Nexa Lab is a web and application developer that specialises in MSPs (Managed Service Providers) and IT departments. We were born and raised in Australia and have over 30 years of experience in the MSP and IT industries.