35 Data Visualization Types to Master the Art of Data

Ready to unlock the power of your data? Brush up on data visualization types that will level-up the information you’re sharing!

Data visualization is all about figuring out how to present data in a way that’s not only visually appealing but also, and more importantly, gets a point across in the most effective way possible.

The problem with relying solely on raw data or basic tables is that they can be confusing, overwhelming, and lack context. Data without clear visualization can miscommunicate information and lead to poor decision-making.

It was business altering when I discovered the various tools and resources for effective data visualization. I especially appreciate how they help transform abstract numbers into tangible visuals, making it easier for everyone – from analysts to stakeholders – to understand complex datasets.

In this post, we’re going to look at the most popular yet effective data visualization types. We’re going to dive deep into each type, illustrating their uses, strengths, and limitations, and offering you a roadmap to transform your data into compelling stories.

So, grab your favorite drink (coffee, I’m thinking), and let’s dive into the many data visualization types!

Categorical Data Visualizations

Categorical data visualizations are an excellent tool for comparing different categories or segments within your dataset. These data visualization types are easy to understand, making them a popular choice for many data analysts.

Think of them as dealing with non-numerical or grouped data, where values fall into a specific category. These are often used to showcase comparisons, distributions, and relationships in a dataset, giving you the power to reveal patterns, trends, and insights that may be otherwise obscured in raw data.

If you’re the type of person who struggles to make sense of seemingly random data points, or if you’re a data enthusiast who loves uncovering hidden trends and insights, you may find this category of data visualization extremely beneficial.

#1: Bar Chart – Making Comparisons Effortless

The bar chart, often also known as a column chart, is a staple in the toolbox of data visualization types. Serving as a simple and effective tool, bar charts facilitate the comparison of data across categories. With one axis dedicated to numerical values and the other representing various categories or subjects under scrutiny, the bar chart brings your data to life.

horizontal bar chart for a number of complaints ranked from highest to lowest as a bar graph example

Whether you choose to orient your bars vertically or horizontally depends largely on the nature of your data. Vertical bar charts place numerical values on the y-axis, offering a quick glance at the size differences, while horizontal bar charts, with numerical values on the x-axis, provide more space for lengthy category labels.

With a bar chart, you can:

  • Transform complex datasets into easily understandable visuals.
  • Visualize comparisons between different categories.
  • Communicate detailed data trends effectively.

An essential tip for leveraging the power of bar charts is considering the complexity of your category labels. If your qualitative data features long or descriptive names, opt for a horizontal bar chart. For an extra layer of creativity, consider using color-coding systems, 3D bars, animated effects, or even photographic backgrounds. Alternatively, a stacked bar chart can illustrate part-to-whole relationships within your categories.

#2: Pie Chart – Showcasing Parts of a Whole

The pie chart ranks high among commonly used data visualizations types, given its simplicity and clarity when demonstrating parts of a whole. The entire circle represents the total, while each individual slice corresponds to a proportion of this total.

Pie charts are ideal for datasets with no more than five or six parts, as this keeps each slice visible and distinguishable. With more than this, slices may become too thin, and with similar values, discerning differences can become challenging. Successful pie charts use contrasting yet harmonious colors, ensuring each slice is visually distinct.

pie chart example

With a pie chart, you can:

  • Transform intricate data into easily digestible reports.
  • Create clear visualizations of proportional relationships.
  • Enhance communication through visual aids.

If your data contains more than six segments, a bar chart could be a more suitable alternative, maintaining the clarity and simplicity of a pie chart while accommodating larger datasets.

#3: Bullet Graph – Compact Data Storytelling

While not as widely recognized as bar or pie charts, bullet graphs pack a punch when it comes to presenting a wealth of information in a compact space. Bullet graphs excel in demonstrating performance against a goal or comparable metric, offering a rich, concise display of key metrics without overwhelming your audience.

bullet graph example to that depicts a complaint dataset for a utility organization with the bars representing the number of complaints and gantt marks to indicate those that had a refund
Bars = #of records | Black lines = # records with a refund

Bullet graphs can help you:

  • Present performance data relative to a set target or benchmark.
  • Highlight measures, drawing attention to whether they fall within an acceptable range.
  • Display multiple measures in a confined space, perfect for dashboard presentations.

Remember, the key to successfully using bullet graphs is to provide clear context. A bullet graph comparing current sales to a set target, with color-coded ranges indicating performance levels, can effectively convey a lot of information at a glance. However, they may not be suitable when your data demands a different context or when illustrating data over time.

Ready to get hands-on with these data visualization types? Check out our A to Z list of data visualization tools.

Hierarchical Data Visualizations Types: Revealing Order and Structure

Hierarchical data visualization techniques are invaluable when you’re dealing with data that’s organized into some sort of hierarchy, whether that be nested categories, familial relationships, organizational structures, or rankings. They help to bring out the order and structure inherent in the data, making it easier to understand and interpret. Here, we will delve into three common types: Tree Diagrams, Treemaps, and Sunburst Charts.

#4: Tree Diagrams – Simplifying Complex Structures

The tree diagram, also known as a hierarchical tree, is a visualization tool that clearly delineates hierarchical relationships within your data. This structure comprises ‘nodes’ and ‘edges’, with each node representing a data point and each edge representing the connection between these points.

tree diagram example

Using a tree diagram, you can:

  • Visualize intricate hierarchical data in a straightforward, logical manner.
  • Clarify relationships and connections between various data points.
  • Create an easy-to-follow map of data lineage or processes.

One crucial point to note when using tree diagrams is to maintain a logical and straightforward layout. Overcomplication can quickly lead to confusion. Remember that the main aim is to present a hierarchical relationship in the most comprehensible way.

#5: Treemaps – Depicting Hierarchies and Proportions

Treemaps take a slightly different approach to representing hierarchical data. Instead of focusing solely on the hierarchy, they simultaneously demonstrate proportions within the hierarchy through varying sizes of rectangles. Each rectangle represents a data point, with its size proportional to a particular dimension of the data.

tree map example

Treemaps allow you to:

  • Represent hierarchical relationships and proportions simultaneously.
  • Accommodate large amounts of data within a confined space.
  • Highlight significant data points through size and color variation.

While treemaps can be incredibly insightful, they may not be suitable if your data set involves too many small, similarly sized categories, which may make the map hard to read and interpret.

#6: Sunburst Charts – Circular Representation of Hierarchies

Sunburst charts, also known as radial treemaps, present hierarchical data in a circular format, making them particularly useful for displaying data that wraps around at the end-points (like hours in a day or months in a year). Each layer of the circle represents a level in the hierarchy, with the innermost layer being the top of the hierarchy.

Sunburst chart example

With a sunburst chart, you can:

  • Visualize complex hierarchical structures in a unique, engaging manner.
  • Demonstrate a full cycle of data effectively.
  • Highlight the proportion of different elements at each hierarchical level.

Keep in mind that while sunburst charts can provide a visually appealing way to present hierarchies, they might not be the best choice for data with many hierarchical levels, as the chart may become crowded and difficult to interpret.

Interested in strategies to enhance your data visualizations? We cover this and more in our in-depth guide on Data Visualization Basics.

Multidimensional Data Visualization Types

Sometimes, your data isn’t as simple as comparing two variables, or understanding hierarchical structures. You may be dealing with complex datasets where you need to understand relationships across multiple dimensions. For these situations, you can leverage multidimensional data visualizations such as Scatter Plots, Bubble Charts, and Radar/Spider Charts.

#7: Scatter Plots – Uncovering Correlations

A scatter plot, also known as a scatter chart or scattergram, is a type of visualization that uses dots to represent the values obtained for two different variables – one plotted along the x-axis and the other along the y-axis. This type of chart can be used to display and compare numeric values, such as scientific, statistical, and engineering data.

By using a scatter plot, you can:

  • Identify types of correlation between variables, if any.
  • Spot any unusual observations in your dataset.
  • Forecast trends by using lines of best fit.

While scatter plots can be effective at demonstrating relationships, it’s important to remember that correlation doesn’t always mean causation. Also, scatter plots may not be as effective when dealing with categorical data visualization types.

#8: Bubble Charts – Adding a Third Dimension

A bubble chart is a variation of a scatter plot. Like scatter plots, they display data across two axes, but they add a third dimension, represented by the size of the dots or ‘bubbles’. This third dimension allows you to incorporate even more data into your analysis.

bubble chart example

With a bubble chart, you can:

  • Display three dimensions of data effectively.
  • Show connections and differences in a dataset that would be difficult to express otherwise.
  • Highlight significant data points through size variation.

Remember that while bubble charts can be visually engaging and informative, too many bubbles or bubbles that are too similar in size can lead to confusion, so it might not make the best data visualization types. Be careful about the scale of your bubbles – disproportionate sizes can distort data interpretation.

#9: Radar/Spider Charts – Comparing Multivariate Data

Radar or spider charts are a unique way of showing multiple data points in a two-dimensional chart, making them useful for comparing multivariate data to and can really pique interest when thinking about data visualization types.

Each variable is given its own axis, all of which are radially distributed around a central point. Data points are plotted along these axes and connected to form a polygon.

spider chart example

Radar/Spider charts allow you to:

  • Compare multiple quantitative variables.
  • Understand the strengths and weaknesses of different variables.
  • Visualize multivariate data in a compact format.

However, these charts can become messy and hard to read when there are too many variables, or the values are too similar. Also, the area covered by the polygon can sometimes give a misleading impression if the values are not evenly distributed.

Ready to step up your data visualization game? Discover how to take your skills to the next level in our comprehensive guide on Data Visualization Basics.

Sequential Data Visualizations: Tracking Change Over Time

Data that is collected over time holds a unique place in data analysis. Time-series data, or sequential data, has its own set of visualization tools which are effective in showing trends, fluctuations, and patterns over a period.

Tracking metrics and KPIs over time is an excellent way to see trends.

It helps to be able to look at the same data from different perspectives at the same time and see how they fit together.

Stephen Few via Tableau Blog

#10: Line Graphs – Highlighting Trends and Fluctuations

A line graph, or line chart, is a powerful tool for showing continuous data, typically over time. It comprises points connected by line segments, with the x-axis often representing time and the y-axis the quantitative variable.

Line graphs enable you to:

  • Visualize trends and fluctuations in data over time.
  • Compare changes in the same variable across different groups.
  • Forecast future trends using historical data.

Line graphs are flexible and straightforward, but they can become cluttered if there are too many lines or time points. Also, they may not effectively represent data where values fluctuate drastically.

#11: Area Charts – Quantifying Changes Over Time

Area charts are similar to line graphs, but with the area below the line filled in. This can be beneficial when you want to demonstrate how a quantity has changed over time, particularly when you want to show the contribution of different components to a total.

With an area chart, you can:

  • Visualize the magnitude of trends over time.
  • Display the part-to-whole relationships.
  • Highlight the total across a trend.

Despite their advantages, area charts can be hard to read if there are too many categories or if the categories overlap significantly.

#12: Stream Graphs – Displaying Density Over Time

A stream graph, also known as a theme river, is a type of stacked area graph which is displaced around a central axis, resulting in a flowing, organic shape. Stream graphs are used to display high-volume datasets, showing the changes in data over time.

Stream graphs allow you to:

  • Visualize large sets of sequential data.
  • Display the density of data flow over time.
  • Highlight anomalies and major events within a dataset.

Stream graphs can be very visually appealing, but they might not be the best choice when precision is key, as it can be difficult to discern the exact values represented.

#13: Gantt Charts – Visualizing Project Timelines

Gantt Charts are an essential tool in project management and are used to illustrate a project schedule. It allows for the representation of the duration of tasks against the progression of time. A Gantt chart is a type of bar chart that shows both the scheduled and completed work over a period.

gantt drawn example

Using a Gantt chart, you can:

  • Plan and schedule projects of all sizes.
  • Set realistic timeframes for project completion.
  • Monitor progress and stay on track with your plan.

While Gantt Charts are excellent for planning and tracking progress, they can become overly complex for large projects with many tasks or dependencies. In such cases, it’s crucial to maintain and update the chart regularly to reflect the true status of the project.

Geospatial Data Visualizations: Mapping Your Data

When your data is tied to specific geographical locations, traditional graphs and charts may not suffice. This is where geospatial visualizations come in. These data visualization types, such as Maps, Choropleth Maps, and Cartograms, allow you to represent data in relation to real-world locations.

Plus. Who doesn’t love a good map for context?

#14: Maps – Plotting Geographical Data

Maps are one of the most traditional forms of data visualization, providing a straightforward method of representing geographical data. This could be as simple as plotting the location of specific events or as complex as showing data variations across different regions.

Map of Texas with zip codes colored in based on number of complaints

With a map, you can:

  • Display the geographic distribution of data.
  • Identify regional patterns and trends.
  • Highlight areas of interest or concern.

While maps are a powerful tool for geospatial data visualization, they may not be as effective when comparing quantities across regions, due to size and proximity variations.

#15: Choropleth Maps – Showing Regional Variations

A Choropleth map uses differing shades or colors to represent statistical data on a predefined geographic area, such as countries, states, or counties. The color intensity represents the quantity of the variable of interest, helping to visualize how this variable changes across the map.

Choropleth maps allow you to:

  • Display divided geographic areas that are colored or patterned in relation to a data variable.
  • Visualize how a measurement varies across a geographic area.
  • Identify regional patterns and variations.

Keep in mind that choropleth maps can sometimes be misleading, as they give equal visual weight to each region, regardless of their size or the number of data points in each region.

#16: Cartograms – Distorting Reality for Clarity

Cartograms are a type of map in which some variable (like population or GDP) is substituted for land area or distance. The geometry or space of the map is distorted to convey the information of this alternate variable.

Cartograms help you to:

  • Represent a specific variable more effectively by sizing regions accordingly.
  • Compare variables independently from the geographical size of regions.
  • Highlight discrepancies in data relative to geographic size.

Remember, though cartograms can provide a powerful representation of data, they can also distort the perception of geographical space, potentially causing confusion.

#17: Heat Maps – Showcasing Density and Intensity

Heat Maps is one of the powerful data visualization types used to represent complex data sets through color gradations. They’re often used to display how a particular quantity or frequency varies across different areas of the map.

For instance, a heat map can show the concentration of population in a region or the intensity of traffic at different times of the day.

With a heat map, you can:

  • Represent complex data in an understandable way.
  • Identify hotspots or areas with high concentration or activity.
  • Spot correlations and patterns in large data sets.

However, heat maps may not be effective when used with data sets with few variations or when individual data points need to be distinct.

#18: Dot Distribution Maps – Representing Location and Frequency

Dot Distribution Maps, also known as dot density maps, are a type of thematic map that uses a dot symbol to show the presence of a feature or phenomenon. They’re used to visualize the geographical distribution of a particular attribute, such as population density in different regions.

Using a dot distribution map, you can:

  • Depict spatial patterns or the geographical distribution of a particular phenomenon.
  • Indicate the presence or frequency of an occurrence.
  • Provide a visual representation of raw data.

Remember, the interpretation of dot distribution maps can be somewhat subjective, and they may not provide a clear picture of the data if the dots are too close together, overlapping, or too spread out.

#19: Parallel Coordinates – Multidimensional Analysis

Parallel Coordinates are an exceptional type of visualization used to plot individual data elements across multiple dimensions. Each data attribute has its parallel vertical axis, and values are plotted as points on each axis, connected by line segments. This visualization type is particularly useful when dealing with multivariate data.

When you use parallel coordinates, you can:

  • Explore and analyze multidimensional numerical data.
  • Detect correlations, outliers, and trends across multiple dimensions.
  • Compare multiple variables without losing sight of individual data points.

However, parallel coordinates may not be as effective when dealing with large data sets due to overplotting. They also require a bit of learning to interpret accurately.

#20: Matrix Plots – Complex Comparisons Simplified

Matrix Plots or Matrix Charts provide a grid-like visual representation of data. Each cell in the grid represents a specific value, often using color to denote this value. It’s a great way to visualize large amounts of data and understand the correlation between different variables.

With a matrix plot, you can:

  • Represent complex and large data in a simplified and concise manner.
  • Compare multiple variables at once.
  • Spot patterns and correlations quickly.

Keep in mind that matrix plots can be less intuitive to understand at first glance and may not be suitable when you want to emphasize individual data points.

#21: Radar Charts – Multivariate Observations

Radar Charts, also known as Spider Charts, use a circular display with several different quantitative axes starting from the same point for a detailed view of data. Each variable has its axis, and the data points are connected, forming a polygon. Radar charts are best used when you want to observe which variables have similar values or if there are any outliers amongst them.

Using radar charts, you can:

  • Compare multiple quantitative variables.
  • Understand the pattern of each individual data series.
  • Highlight similarities or differences between different groups.

Remember, radar charts can become cluttered and hard to read when used with many variables or categories. Additionally, they can distort data perception when the axes aren’t uniformly scaled.

#22: Word Clouds – Textual Emphasis

Word Clouds, also known as tag clouds, depict textual data where the size of each word represents its frequency or importance in a body of text. They are a fun and visually appealing way to highlight popular or high-impact words, with larger-sized words indicating higher frequency or importance.

With Word Clouds, you can:

  • Visualize textual data, emphasizing popular or recurring themes.
  • Analyze and present customer feedback, social media sentiment, or keyword research.
  • Create visually engaging presentations of textual content.

However, keep in mind that Word Clouds are best used for illustrative purposes rather than deep analysis, as they lack precise quantitative values.

#23: Highlight Tables – Focus on Categories

Highlight Tables take data tables a step further by adding color to represent values, helping you focus on specific categories. The color intensity reflects the value in the cell, offering an at-a-glance overview of the data.

Using Highlight Tables, you can:

  • Add an extra layer of detail to a basic table.
  • Bring focus to high or low values in a large dataset.
  • Easily compare categorical data.

Remember that while highlight tables are useful for bringing attention to specific data points, they can become overwhelming and difficult to interpret if they’re too complex or have too many categories.

#24: Bubble Clouds – Multidimensional Textual Visualization

Bubble Clouds, sometimes called Circle Packing or Bubble Charts, visualize data hierarchically as a cluster of circles. The size and color of each circle can represent additional variables. Bubble Clouds can present numeric, categorical, or textual data and are helpful when the data has many layers of categorization.

With Bubble Clouds, you can:

  • Represent multilayered or hierarchical data.
  • Compare and contrast different categories and subcategories.
  • Add visual interest to complex datasets.

Keep in mind, however, that like with many other visually intense plots, Bubble Clouds can be challenging to understand and interpret if overused or if they include too many categories or subcategories.

Unique and Complex Data Visualizations

These data visualization types are less common but can provide unique insights when used correctly. They often display more complex data structures or more specific types of data and are best used when simpler visualizations fall short.

#25: Streamgraphs – Show Volume Over Time

Streamgraphs are stacked area charts with smooth, flowing shapes, used to visualize changes in data over time. The aesthetic appeal of streamgraphs often makes them a popular choice for public data visualizations.

With Streamgraphs, you can:

  • Display high-volume data over time in a visually engaging way.
  • Showcase patterns and trends in large datasets.
  • Highlight the magnitude of change between different categories over time.

However, Streamgraphs can be harder to read and interpret than basic line or bar charts due to their flowing shapes, so it’s essential to consider your audience’s data literacy.

#26: Waterfall Charts – Bridge the Gap

Waterfall charts are a form of data visualization that helps demonstrate how an initial value is affected by subsequent positive and negative values. It effectively showcases the cumulative effect of sequential data, providing a ‘bridge’ from one data point to the next, hence the name “waterfall.”

With Waterfall Charts, you can:

  • Visualize the cumulative effect of sequential positive and negative values.
  • Show how an initial value is adjusted to a final value.
  • Depict the incremental changes in a metric over time or between categories.

Keep in mind that Waterfall Charts can become complex and hard to interpret if they contain too many categories or steps.

#27: Chord Diagrams – Visualizing Inter-Relationships

Chord Diagrams are circular charts used to display the inter-relationships between data in a matrix. The data points are arranged around a circle with the relationships depicted as arcs connecting the data points.

With Chord Diagrams, you can:

  • Represent complex inter-relationships between different data points.
  • Visualize network structures or flow data.
  • Present multidimensional data in a single plot.

Chord Diagrams are complex and require a higher degree of data literacy to interpret correctly. Therefore, it’s advisable to use them when your audience has a good understanding of the data and the relationships being represented.

#33: Heatmaps – Visualize Magnitude of Phenomena

Heatmaps are data visualizations that use color-coding to represent different values of data. Heatmaps are excellent tools for displaying large amounts of data and showing variance across multiple variables, helping to visualize complex data sets.

With Heatmaps, you can:

  • Visualize large amounts of data in a compact space.
  • Display variations across multiple variables.
  • Understand complex data sets intuitively through color differentiation.

However, Heatmaps can become hard to interpret when there are too many categories or if the color differentiation isn’t clear.

#34: Dot Distribution Maps – Geographical Representation of Data

Dot Distribution Maps are used to show the geographical distribution of phenomena. Each dot represents a specific quantity of the phenomena at a particular location. They are most effective when you want to show density or distribution over a geographic area.

With Dot Distribution Maps, you can:

  • Show geographic distribution of a single category or multiple categories.
  • Highlight density or concentration in specific areas.
  • Represent large datasets on a geographical layout.

Dot Distribution Maps can become confusing when there are too many dots or categories, so it’s essential to use them judiciously.

#35: Bubble Clouds – Multi-Dimensional Visualizations

Bubble clouds are similar to scatter plots but with an additional dimension represented by the size of the bubbles. The X and Y axes represent two dimensions, while the size (and sometimes color) of the bubbles represent additional dimensions.

With Bubble Clouds, you can:

  • Visualize multi-dimensional data in a single plot.
  • Show relationships and disparities between data points.
  • Highlight the significance of specific data points using the bubble size.

Bubble Clouds can become complex if there are too many bubbles or if the bubbles overlap, making it hard to interpret the data accurately.

Remember, while adding more types of visualizations to your list can make it comprehensive, the key is to help your reader understand when and how to use each type effectively.

The Art of Choosing the Right Visualization: Concluding Thoughts

Navigating the vast landscape of data visualizations can initially seem like a daunting task, but with the right understanding and tools, it transforms into an exciting journey. Remember, data visualizations are a powerful medium to convey complex information in an easily digestible and engaging way. However, the effectiveness of your visualization hinges on choosing the right type.

When deciding which visualization to use, here are some fundamental aspects to consider:

1. The Nature of Your Data: The type and structure of your data are key determinants in your choice of visualization. Numerical data might be best served by bar or line charts, while geographical data can be presented as a map. Categorical data, on the other hand, might warrant a pie chart or a treemap.

2. The Message You Want to Convey: What’s the story you want to tell with your data? Are you highlighting a trend, comparing items, or showing a relationship? The goal of your communication heavily influences your choice.

3. The Audience: Consider who will be interpreting your visualization. What’s their level of data literacy? Are they familiar with more complex visualizations or should you stick to the basics? Tailoring your visualization to your audience ensures your data story is received as intended.

4. Simplicity vs. Complexity: While some visualizations can depict complex, multi-dimensional data, simplicity often leads to better understanding. If a simpler visualization can tell the same story, it might be the better choice.

5. Trial and Experimentation: Don’t be afraid to experiment with different visualizations. Often, it’s not until you see your data in several visual forms that the most effective one becomes apparent.

In conclusion, the art of data visualization lies in striking the balance between aesthetic appeal and functional communication. The right visualization accentuates your data’s story, driving insight and aiding decision-making. Each type of data visualization has its strengths and appropriate uses, so choose wisely and let your data shine. And always remember, the ultimate aim of data visualization is not just to make data look pretty, but to make it meaningful and accessible for everyone.

If you’re intrigued by the possibilities of data visualization, learn about the key skills you need to master in our essential guide on Data Visualization Basics.