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33 Data Visualization Types: Choose the One You Need

Written by: Orana Velarde

Part of the strategy of visualizing data is choosing what type of data visualization to use. The trick to choosing the right visualization is selecting the one that best represents your data’s message and story.

There are many types of data visualization . The most common are scatter plots, line graphs, pie charts, bar charts, heat maps, area charts, choropleth maps and histograms.

In this guide, we’ve put together a list of 33 types of data visualizations. You’ll also find an overview of each one and guidelines for when to use them.

Before we get started, watch our video tutorial for creating data visualizations:

Here’s a short selection of 8 easy-to-edit data visualization templates you can edit, share and download with Visme. View more templates below:

Now, let’s get started.

33 Different Types of Data Visualization to Choose From

Type #1: bar chart, type #2: pie chart, type #3: donut chart, type #4: half donut chart, type #5: multi-layer pie chart, type #6: line chart, type #7: scatter plot.

• Type #8: Bubble Chart

Type #9: Cone Chart

Type #10: pyramid chart, type #11: funnel chart, type #12: radar triangle, type #13: radar polygon, type #14: polar graph, type #15: area chart, type #16: tree chart, type #17: flowchart, type #18: table.

• Type #19: Geographic Map

Type #20: Icon Array

Type #21: percentage bar, type #22: gauge, type #23: radial wheel, type #24: concentric circles, type #25: gantt chart.

• Type #26: Circuit Diagram

Type #27: Timeline

Type #28: venn diagram, type #29: histogram, type #30: mind map, type #31: dichotomous key.

• Type #32: Pert Chart

Type #33: Choropleth Map

The bar chart or bar graph is one of the most common data visualization examples on this list. They’re sometimes also referred to as column charts. Bar charts are used to compare data along two axes. One of the axes is numerical, while the other visualizes the categories or topics being measured.

You can use a bar chart with vertical bars or horizontal bars. On vertical bar graphs, numerical values are on the y axis (vertical axis) and can be aligned to the left, right or center. On horizontal bars, they are on the x-axis (horizontal axis.)

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To choose which style of bar graph to use, take a look at your data. If your qualitative data has long, descriptive names, then a horizontal bar chart would be the best choice. Another creative option is to use a visual axis system like in the template above. Instead of placing the category name next to its respective bar, use a color-coding system and a legend.

For a more creative approach, try using 3D bars, animated effects and photographic backgrounds. Alternatively, try creating a stacked bar chart — you’ll find the option to do so in Visme's graph settings.

The second most common data visualization on this list is the pie chart. The data in a pie chart represent parts of a whole. The entirety of the circle is the whole, and each wedge is a relevant section.

The best type of data for a pie chart has no more than five or six parts. Any more than this makes the wedges too thin at the center. If more than three values are similar to each other, it will be difficult to discern the difference. The best pie charts use contrasting colors that fit well together, making each wedge visually different from the one next to it.

If you have more than six sections to visualize, consider using a donut chart instead. You can animate your charts or make them interactive to engage your audience.

A donut chart is much like a pie chart but with the center area taken out. The difference between them is essentially visual. You can have more sections than a pie chart in a donut chart and it will still be readable.

The same rule about colors applies to donut charts; choose contrasting colors to separate the sections visually. To make them more attractive, add a 3D feature to the donut, which has more visual depth. If you’re working on a project to share online, consider adding an animation to the chart.

The half donut chart is exactly what its name implies, half of a donut chart. It’s a good choice of data visualization type when you need to showcase small data sets. Preferably, don’t use more than three wedges in a half donut chart.

Remember to use contrasting colors and use percentage values to make your half donut chart easier to read at a glance.

Use pie charts and donut charts in unison to create a multilayer pie chart. These visualizations work well for infographics and other visual representations of complex data.

You can see a multilayer pie chart in the infographic above depicting emotional nuances in marketing language. The outside donut chart is the top-level category, the emotions. On the second layer are the descriptive sections that fit inside each main category. In the center is a small layer separating all nuances into three connotations.

This data visualization type isn’t as easy to create as others; it does take some strategizing for all the categories and different charts to fit together and be easy to understand. In technical terms, this visualization is three pie charts layered over each other.

A line chart or line graph is a data visualization type that showcases changing data over time. Like a bar graph, the line chart has an x and y-axis. The difference is that both axes contain numerical values representative of the data.

To create a line chart, input the relevant time frame along the x-axis and the quantitative measurement on the y-axis. Plot the data in the graph by connecting the time value and the numeric value. After plotting all the dots, connect them with a line.

A line graph can have one line or several. In the case of a chart with several lines, each one represents a category. Every category has a color and the description is detailed in the legend.

For an effective line graph, use no more than four or five lines and make sure the colors are different enough to be differentiated visually. Connect your Google Sheets data for visualization purposes easily with our integration. Link a Visme account to a Google account and get instant access to all your sheets directly from Visme. Likewise, transfer live data from Google to Visme for easy updates on published line graphs.

A scatter plot is a data visualization type used to analyze the correlation between variables. The data is plotted on the chart as dots at the intersection of its two values.

Take a look at the scatter plot example here; the values are square footage and price. Each dot in the graph represents a house. If you were to add a scatter for apartments with the same values, you’d use dots in a different color. When there are dots outside of the expected range, these are called outliers and should be taken into consideration when analyzing the data.

Use scatter plots where your variables are related to each other regarding a group of test subjects. Some of these could be the relation between weight and height in children under 18 years old, temperature-dependent sales in an ice cream shop, diabetes, and obesity rates.

Stay away from plotting too many data points on a scatter plot or it will become impossible to read. Use no more than two different color dots and always use a legend if that’s the case.

If you want to read more on this subject, check out our complete guide to scatter plots .

Type # 8: Bubble Chart

A bubble chart (or bubble plot) is a variation of the scatter plot used to visualize relationships between three numeric variables and to identify patterns in data. Each bubble on the chart represents a data point and the size and position of the bubble correspond to a specific value.

Similar to a scatter plot, both the horizontal and vertical axes are value axes.  Besides the x values and y values, bubble charts have a third dimension — z (size) values.

This bubble chart visualizes data related to employee turnover in an organization. The bubbles represent the employees who left the company. But you can customize it to suit your unique needs using a variety of bubble chart templates .

For example, you can use color to represent different reasons for turnover, such as voluntary resignations, layoffs, or retirements.

The cone chart is another data visualization type that shows parts of a whole, similar to pie charts. The difference is that a cone chart also visualizes hierarchy. The data with the highest value sits highest on the cone with the widest area. Other values flow in descending order towards the bottom tip of the cone.

Use contrasting colors to visualize the different values, or select a monochromatic palette to add depth to the visual hierarchy. Don’t use more than seven or eight values, as using too many will make the cone chart difficult to understand. Include a color-coded legend for a more straightforward analysis.

Cone charts in Visme have a visual 3D effect to resemble a real cone. This visual effect differentiates them from the pyramid chart, which is similar but inverted.

Edit your cone charts on the go with Visme’s Android and iOS apps. Customize charts from your phone or tablet and see them reflected on your computer.

A pyramid is much like a cone chart but placed the other way around. The smallest data set is at the top, while the largest is at the bottom. Deciding whether you want to use a cone chart or a pyramid chart depends on how you want to present data; in ascending order or descending order.

Pyramid charts can also be created without numerical data. The sections are separated into equal parts to show a hierarchy of steps or components of a whole that are only visually hierarchical. Such is the case in the example below with the pyramid in violet tones.

RELATED: Top 10 Data Visualization Tools for 2024

A funnel chart is similar to a cone chart in shape but has a slightly different purpose. The main idea with a funnel chart is to visualize a sequential process from top to bottom. Generally, the data set at the top of the process is larger than the bottom as the process diminishes the quantity as it flows down.

The most common use for a funnel chart is visualizing an email nurture sequence or marketing strategy data. Another data set that fits this data visualization type is an admissions report or alcohol distillation process.

Just like cone charts and pyramid chats, choose the colors for your funnel chart wisely. It’s essential to create a visual difference between sections.

Radar charts are a data visualization type that helps analyze items or categories according to a specific number of characteristics. The radar chart layout is a circle with concentric circles where the data are plotted as dots. The dots are then connected to create a shape. Each item or category is a shape.

A radar triangle is a radar graph that compares items or categories based on three characteristics. Each dot is one corner of the triangle. The triangle can be composed only of lines or with a transparent color fill.

It’s important to remember that you can’t add too many layers to a radar graph or it will be impossible to analyze.

A Radar polygon is the same as the radar triangle, but the resulting shape is different. A radar triangle has three points for characteristic data, while a radar polygon has four or more. The maximum number of points is 9 or 10, the max layer of items is 4 or 5.

When choosing colors for each item, select ones that will layer well and not become a dirty mess where they all overlap — your best choice is to use a series of monochromatic tones with one base color. For example, shades of blue and purple or shades of red and orange.

A polar graph has the same circular base as a radar chart, but the data plots differently. Instead of connecting points to each other, wedges expand outwards from the center.

The difference is primarily visual. Choose a polar graph if the data values are very different to each other. Otherwise, it can be challenging to read at a glance.

The area chart is a variation of the line chart. The difference is that the area between the baseline and the values plotted on the line is colored in. The color fill is semi-transparent so that the overlapping regions are easy to read.

Even though you can switch any line chart into an area chart, it’s not always the best practice. An area chart can’t have more than four or five datasets simultaneously; the possibility of occlusion is too high. Area charts are sometimes stacked, separating the data into sections as part of whole relationships or as cumulative data.

A tree chart, or tree diagram is more of a visual data visualization than one for detailed numerical data. The main idea in a tree chart is to visualize data as parts of a whole inside a category. For a more complex tree chart, layout different categories next to each other.

Choose a tree chart when your visualization doesn’t depend on granular numerical data. Better yet, if the data is hierarchical, a tree chart does a good job.

A flowchart is a highly versatile data visualization technique . Use a flowchart to visually describe a process, hierarchical data of items or persons and even a mind map for brainstorming strategy.

The best part about flowcharts is that they are easy to customize for any project—for example, a training manual or strategy proposal. Inside a pitch deck or welcome kit, a flowchart can visualize the hierarchy of the company’s teams.

Visually, flowcharts start with one header shape that branches out to a series of shapes and lines that connect. Creating a flowchart with Visme is super easy; select from the pre-designed sections or start from scratch. Every shape has intuitive options for branching and you can customize all shapes for color and size.

Tables are like mini spreadsheets and show data in rows and columns. Use a table to display pricing for a service, comparative features of a product, school reports and more.

This data visualization type fits well inside visual documents like reports, proposals and training manuals. For a unique take on a table visualization, use dots or icons to represent yes or no data about a specific category.

Visme offers six visual types of tables that you can customize to fit the rest of your project.

Type #19: Geospatial Map

Maps are the ideal visualization for any data that has to do with geolocation. A data map has many uses, from country-by-country information to detailed regional analysis.

Visme's map maker works similarly to the graph maker. Input data in a CSV or via the Google Sheets integration. Use colors to color code the map to match your data and your project. Further adjust the map settings to your liking, such as turning the hover location tooltip on or off.

Alternatively, use the map graphics on their own and add data widgets for more complex visualization projects.

Icon array visualizations show two pieces of a whole, either as units or percentages. The most common use for an icon array is visualizing a population’s sector according to two factors. For example, male or female, remote workers or in-office workers, etc.

Each icon in the array can represent a unit or a specific amount like 10, 100, 1000. The icons are arranged according to your particular data.

In Visme, arrays are easily customizable in terms of colors and icon shapes. Select the icon that best matches your story and add the colors of your project. Use a legend to help viewers understand your values.

A progress or percentage bar is a simple data visualization type used to display a percentage value. These come in handy when creating an informational infographic or progress report. Since percentage bars are so small, they work well as a group.

Visme has several types of percentage bars in both vertical and horizontal layouts. For a balanced data visualization, use the same style in a group and use colors that go well together.

A gauge is another visualization type for percentages. The shape resembles a half donut and has a couple of uses. The simplest use is to show a percentage value with an arrow pointing to it. This is a great choice if you're dealing with a small amount of data.

Alternatively, use a gauge to demonstrate the status or goal of a project. Use a half donut chart with three of four equal values and color code for each section, such as Q1, Q2, Q3 and Q4.

Another data visualization type for percentage values is the radial wheel. This is a practical data widget for any type of visual project. Use a radial wheel for infographics, social media visuals, blogs, statistical reports and more.

Customize the radial wheel with the colors in your project and personalize the way the values are presented. Like percentage bars, radial wheel are great for group layouts.

A concentric circles data visualization is like a line chart on a circular axis. Each category or data item is a circle in the chart, and each circle has its own color and is plotted along the circular axis according to the data. Also, the circles are arranged concentrically.

For an easy-to-read chart, there should be no more than six concentric circles.

Gantt charts are based on horizontal bar graphs but are different in a big way. In a Gantt chart , it’s not about how the data changes over time but rather how long it takes to complete over a specific range of time.

Each item on the chart is represented by a rectangle that stretches from left to right. Each one has a different size, depending on how long each task takes to complete.

The best way to use a Gantt chart is with your team. Create one in Visme and share it with everyone via a link. If it needs to be adjusted, simply drag the corresponding rectangle to its new location on the chart.

Type #26: Network Diagram

A circuit diagram is a type of flowchart that visualizes concepts like technical circuits, network setups and other technical connections. These are generally simply designed diagrams without much fanfare. They need to be easy to follow at a glance.

Visme has several different network diagrams for different technical purposes like firewall setups , router setups and other basic network connections. These are great to include in employee handbooks and office documentation.

Timelines are visualizations that show events that have happened or will happen over a specific period. Use this data visualization type for informational reports about topics with a backstory or for visualizing a company’s growth story. Alternatively, use a timeline to explain a plan or objective for a project.

With Visme, you can create timelines in many different ways. The easiest is to use the flowchart tool, but you can also start from scratch and use lines and shapes. Timelines work great as infographics in vertical layouts and horizontally on one presentation slide or several consecutive ones.

A Venn diagram is a data visualization type that aims to compare two or more things by highlighting what they have in common. The most common style for a Venn diagram is two circles that overlap. Each circle represents a concept and the area that connects them is what the two have in common.

Venn diagrams can have up to four or five concept circles where the combined areas show what's in common between them. A Venn diagram with three circles has three areas with two combined concepts and one with three.

Using more than three or four circles or shapes in a Venn diagram gets very complicated. In those cases, circles aren’t always the best option — try ovals or blob shapes instead.

A histogram is similar to a bar graph but has a different plotting system. Histograms are the best data visualization type to analyze ranges of data according to a specific frequency. They’re like a simple bar graph but specifically to visualize frequency data over a specific time period.

Histograms can only be vertical, differently from how bar charts can be both vertical or horizontal.

A mind map is another data visualization type that helps brainstorm and organize ideas. Visually, a mind map is a web of shapes organized by concept and connected in order of hierarchy. A mind map can be small with only a few connected shapes or extremely large, with many shapes branching out from one or two main ideas.

To create a mind map, you’ll need the Visme mind map maker and an empty canvas. Start with one central shape and branch off in any direction. The intuitive builder offers four possible branches that can then branch out again into numerous other ones. If you need to move things around, select the shape and line and drag them to a new location.

Mind maps are great tools in education, business brainstorming and creativity. They’re like windows into your thoughts, making them easier to share with peers.

A dichotomous key is another type of flowchart visualization whose purpose is to help with decision making. As you answer question after question, you move along the flowchart towards the appropriate answer. There are usually two answers (yes or no), but there can be three or four depending on the key’s length and complexity.

Dichotomous keys are used widely in scientific education; they help classify organisms by answering questions about their characteristics. These data visualizations also work well as infographics or blog visuals. They‘re also used in work environments to help employees make decisions about a task or situation.

Type #32: PERT Chart

We have one more data visualization type based on the trusty flowchart. A PERT chart is a combination of a circuit diagram and a process chart. The idea behind a PERT chart is to follow each item as a process. The next connected shape is dependent on the one before it and can’t be done out of order unless stated in the chart.

Create a PERT chart with the Visme flowchart maker easily. Draft out your process on paper and then simply input your content into one of our templates or start from scratch. An effective PERT chart uses different shapes or colors to represent each step’s specific characteristics.

The last data visualization type on our list is the choropleth map. This visualization is based on a geographic map but has a specific purpose. A choropleth map is a geographical representation of statistical values according to region. For example, population density in a country, visualized by state.

Values are separated into equal parts and each assigned a color. The corresponding areas of the map are then color-coded to match their value. These visualizations are perfect for non-profit organizations, health-related companies or anyone who needs to visualize statistical values related to a geographical location.

A choropleth map is perfect for creating an interactive data visualization with large data sets. Each colored region can be assigned popup data labels with information about the data being used.

Ready to Take Your Data Visualization to the Next Level?

What a great long list of types of visualizations you just got through! Now you’re ready to create your own amazing data visualization.

Regardless if you’re a data scientist or a marketer working closely with data analysis, knowing the common types of data visualization is a great skill to have.

Use Visme to create all types of data visualization quickly and easily. Animate your charts and graphs, make them interactive, instantly export them as images or PDF files, add them to reports and presentations, and much more.

If you’re a Tableau or Excel user, you can still use Visme to present your data visualizations to your team with the help of embedding and import options. One example is Visme’s Excel Online integration, where you can select a data range or full sheet to import to Visme. 

Apart from the data visualization types listed here, you can also create Mekko charts , population pyramids, bullet graphs, waterfall charts, bubble charts and box plots.

Sign up for a free Visme account today and get started!

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About the Author

Orana is a multi-faceted creative. She is a content writer, artist, and designer. She travels the world with her family and is currently in Istanbul. Find out more about her work at oranavelarde.com

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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:

• 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.

• 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 .

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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4 years ago

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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13 of the Most Common Types of Data Visualization

In data analytics, data visualization is a vital technique for pattern-spotting and presenting findings. But what are the most common types of data visualization? Let’s find out.

Data visualization (or ‘data viz’) is one of the most important aspects of data analytics. Mapping raw data using graphical elements is great for aiding pattern-spotting and it’s useful for sharing findings in an easily digestible, eye-catching way. And while the priority should always be the integrity of your data, if done well, data visualization can also be a lot of fun.

Master the art of data viz and you’ll soon be spotting trends and correlations, all while flexing your creative muscle. But before we can unlock all these benefits, we first need to understand the basics, including the different types of data visualization and how they’re used . In this post, we’ll cover 13 of the most common ones, starting with…

1. Pivot tables

Source:  evolytics.com

You might not think of tables as a form of data visualization, but they are! When dealing with vast repositories of information—ones that are too large to easily comprehend—pivot tables help us summarize key statistics in a single view. The type of information collected in pivot tables might include sums, means, or other numerical summaries.

While pivot tables aren’t always the most visually inspiring form of data viz, they are useful in the right context. For instance, highlight tables, as shown in the image, use different shades or colors to easily flag the highest and lowest values in a dataset. Sometimes, this is all you need, making pivot tables a basic but effective form of data viz. They are also commonly used to underpin more complex forms of data visualization, hence making it on to our list. You can learn how to create a pivot table here .

2. Boxplots

Source: dimensionless.in

Another useful (if not particularly flashy) type of descriptive visualization is the boxplot (also known as a box-and-whisker plot ). Like pivot tables, boxplots are useful for visualizing a dataset’s key statistics. We can use them to represent minimum and maximum values, the median value, and the lower and upper quartiles (i.e. the median of the lower and upper halves of the data).

Boxplots are what is known as ‘non-parametric.’ This means they display variation in a data sample without making any assumptions about the data’s distribution. This makes them useful for exploratory and explanatory data analysis , i.e. getting to understand a dataset’s key features before drawing any broad conclusions about it.

3. Scatterplots

Source: displayr.com

A scatterplot (also known as a scattergraph, scattergram, or scatter chart) displays the relationship between two variables on an x- and y-axis. Each item of data is shown as a single point, creating the chart’s visual ‘scatter’ effect. When there are three interrelated data points (i.e. if there’s a z-axis) 3D scatterplots are also possible.

Scatterplots are best used for large datasets where time is not a significant factor. For instance, a simple scatterplot might measure people’s weight against height. This would help identify any correlation between the two measures. However, because other factors affect the data (e.g. people’s weights are also related to their diet) scatterplots are best for inferring relationships between variables rather than drawing firm conclusions. Nevertheless, they are an excellent tool for hypothesis creation.

A common variant of the scatterplot is the bubble chart . Displaying different-sized circles (rather than single points), bubble charts represent three dimensions of data, rather than the usual two.

Source: lucidchart.com

4. Line graphs

Source:  data-to-viz.com

Line graphs, or line charts, are a simple but effective staple for representing time-series data. They are visually similar to scatterplots but represent data points separated by time intervals with segments joined by a line. This allows for quick observation of features like acceleration (when the line goes up), deceleration (when the line goes down), and volatility (when the line moves up and down erratically).

While the simple line graph shown represents a single dataset, more complex line graphs may overlay several lines to represent different data. This is useful for spotting correlations or deviation. A common example of a line graph in action is the measure of stock market behavior or resource costs over time, e.g. the price of gold over several years.

Want to learn how to create data visualizations?  Follow this free introductory data visualization tutorial . You’ll learn, step by step, how to create bar charts, line graphs, and more in Google Sheets, for a real dataset!

5. Area charts

Source: anychart.com

Area charts, similar to line charts, are also used for tracking data over time. However, in an area chart, the space between the plotted line and the x-axis is shaded or colored for visibility. This is particularly useful for highlighting the difference between multiple variables, or for measuring overall volumes (rather than highlighting the difference between discrete data points).

For example, in the image provided—which is known as a stacked area chart—the most important factor to note is the volume of products sold in each country, which is represented by the shaded areas. A common variant on the area chart is the streamgraph, where data is plotted around a central axis to minimize so-called ‘wobble.’

Source: flowingdata.com

6. Bar charts

Source: datavizproject.com

Another common visualization—one you’ll no doubt be familiar with from school—is the bar chart. Bar charts are a simple but highly effective way of plotting categorical data against discrete values. The heights (or widths) of the bars are in direct proportion to the values they represent. This makes bar charts an excellent way of comparing discrete variables at a glance.

Some bar charts cluster bars into groups of two or three (or more) allowing you to compare numerous variables at different points in time. Another variation is the stacked bar chart, which divides each bar into separate sub-bars, one stacked on top of another. This allows for the introduction of additional variables.

Source: chartio.com

7. Histograms

Source: internetgeography.net

Although visually similar to bar charts, histograms are not the same thing. Bar charts measure categorical data, while histograms measure the distribution of numerical data , i.e. the frequency with which a discrete data point appears in a dataset.

In a histogram, each bar represents how often a data point falls within a given range. For example, each column might represent different age groups (20 to 29, 30 to 39, and so on). This makes histograms excellent for summarizing large amounts of continuous data without needing to inspect every single value.

If you struggle to distinguish between bar charts and histograms, look out for spacing—there should always be a space between bars on a bar chart (to signify that the categories are discrete) while there should be no gap between the bars on a histogram (signifying that the data are continuous). You’d be surprised how often people get this wrong though, so keep your eyes peeled!

You can learn how to create a histogram in Excel in this step-by-step guide .

8. Pie charts

Source: University of Texas at Austin

Another visualization you may remember from school is the pie chart. While pie charts are similar to bar charts in that they represent categorical data, this is where the similarities end. The main difference (besides how they look) is that bar charts represent numerous categories of data, while pie charts represent a single variable, broken down into percentages or proportions.

Each ‘slice of the pie’ in a pie chart is proportional to the quantity it contributes to the whole, i.e. the entire circle. For this reason, pie charts are best-suited to data that is split into about five or six categories…add more than that and it quickly becomes too complex to effectively represent the data.

9. Network graphs

Source: networkofthrones.wordpress.com

As sources of data grow more complex and interconnected, so must the visualizations we use to represent them. Enter network graphs, which are used to show how different elements of a network relate to one another. Each element in a network graph is represented by an individual node, interconnected to related nodes via lines. This approach is excellent for visualizing clusters within the larger whole—patterns that can otherwise be hard to spot.

The joy of this type of visualization is that you can represent networks with varying degrees of complexity without impacting the usefulness of the visualization. In fact, the more elements and connections a diagram includes, the more likely it is to help you spot the larger clusters hidden in the data.

10. Geographical maps

Source:  ubs.com

One of the most versatile types of data visualization is the geographical map, which can bring life to a whole range of different location-specific data. A common example is the distribution of vote share during an election, like that shown in the image.

Maps can be used in diverse ways. For example, geographical heat maps use color to show the variation of a particular element over a given area, offering visual clues about data distribution. A simple example is the social media company Snapchat, which uses heat maps to show where the highest density of snaps are being shared.

Other types of maps include dot distribution maps (which combine the idea of a scattergram with a map) and cartograms, where the size of geographical locations are distorted to match the proportion of a selected variable, e.g. world population.

Source: metrocosm.com

11. Radar charts

Source: Middlebury College, Vermont

Radar charts (also known as spider charts) are useful for representing multivariate data (i.e. data that incorporate more than one variable) in a two-dimensional format. They are commonly used to compare features between different observations. They are also helpful for identifying outliers or commonality between observations.

Radar charts usually work by overlaying two or more variables on the same axis, using different colored lines to distinguish between them. For example, you might use a radar chart to compare the features of three different products, including aspects like price, durability, cost of production, and so on. Radar charts are also commonly used in sport to compare athletic performance, as displayed in the image.

12. Treemaps

Source: Ali Zifan, CC BY-SA 4.0 , via Wikimedia Commons

Treemaps are a type of data visualization that are excellent for displaying hierarchical data, usually in the form of nested rectangles. This involves breaking each category down into smaller rectangles, which represent sub-categories.

Treemaps are commonly used to display things like products or distribution of disk space by location or file type. Because they make efficient use of space, they are excellent for displaying thousands of different categories in a limited amount of real estate. This ability to represent highly complex data makes them a popular visualization in data analytics and data science.

13. Venn diagrams

Last but not least: the classic Venn diagram. Venn diagrams use a series of overlapping shapes (usually circles, but sometimes ellipses or other abstract forms) to highlight common features between different groups of items. Each area created by the overlapping shapes represents features that groups share in common. Where circles don’t overlap, the groups do not share features in common.

Venn diagrams are useful for quickly visualizing the relationship between different groups of data. However, be aware that they can easily oversimplify these relationships. If you try to tackle this by adding more data, they can quickly become cumbersome. As a result, Venn diagrams are best used for descriptive purposes.

In this post, we’ve introduced a handful of core data visualizations. Despite their simplicity, these visualizations are highly versatile. Even just these few techniques can be used to make sense of complex datasets. After all, data visualization is all about simplicity and clarity. If you’re new to data viz, you’ll find a complete introduction to the topic here .

Once you’ve mastered the basics and explored a few visualizations of your own, you’ll be in a great position to start experimenting. Combine different graphics, play around with colors and shapes, and of course, try blending the different types of visualization. You can also play with interactivity by using some different data viz tools .

While the best visualizations are usually the simplest, that shouldn’t stop you trying new approaches and discovering novel ways of visually representing information. You’ll be surprised how many combinations and possibilities there are, and what insights you will uncover.

CareerFoundry’s  Data Visualizations with Python course is designed to ease you into this vital area of data analytics. You can take it as a standalone course as well as a specialization within our full Data Analytics Program, you’ll learn and apply the principles of data viz in a real-world project, as well as getting to grips with various data visualization libraries.

Next, to learn more about data analytics, why not try our free, 5-day data analytics short course ? You can also read more introductory data analytics topics:

• The Top 8 Free Data Viz Tools for 2022
• 9 Beautiful data visualization examples
• A step-by-step guide to the data analysis process

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Blog Graphic Design What is Data Visualization? (Definition, Examples, Best Practices)

What is Data Visualization? (Definition, Examples, Best Practices)

Written by: Midori Nediger Jun 05, 2020

Words don’t always paint the clearest picture. Raw data doesn’t always tell the most compelling story. 

The human mind is very receptive to visual information. That’s why data visualization is a powerful tool for communication.    

But if “data visualization” sounds tricky and technical don’t worry—it doesn’t have to be. 

This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.

Table of Contents

• What is data visualization?

What is data visualization used for?

Types of data visualizations.

• How to present data visually  (for businesses, marketers, nonprofits, and education)
• Data visualization examples

Data visualization is used everywhere. 

Businesses use data visualization for reporting, forecasting, and marketing. 

CREATE THIS REPORT TEMPLATE

Nonprofits use data visualizations to put stories and faces to numbers. 

Source:  Bill and Melinda Gates Foundation

Scholars and scientists use data visualization to illustrate concepts and reinforce their arguments.

CREATE THIS MIND MAP TEMPLATE

Reporters use data visualization to show trends and contextualize stories. 

While data visualizations can make your work more professional, they can also be a lot of fun.

What is data visualization? A simple definition of data visualization:

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart , infographic , diagram or map. 

The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. 

Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data. 

Data visualizations often use elements of visual storytelling to communicate a message supported by the data. 

There are many situations where you would want to present data visually. 

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Let’s look at some examples for each use case.

1. Make data digestible and easy to understand

Often, a large set of numbers can make us go cross-eyed. It can be difficult to find the significance behind rows of data. 

Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks. 

Related : How to Use Data Visualization in Your Infographics

For example, this infographic breaks down the concept of neuroplasticity in an approachable way:

Source: NICABM

The same goes for complex, specialized concepts. It can often be difficult to break down the information in a way that non-specialists will understand. But an infographic that organizes the information, with visuals, can demystify concepts for novice readers.

CREATE THIS INFOGRAPHIC TEMPLATE

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2. Identify trends and outliers

If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly. 

Even better, charts enable you to pick up on trends a lot quicker than you would sifting through numbers.

For example, here’s a simple chart generated by Google Search Console that shows the change in Google searches for “toilet paper”. As you can see, in March 2020 there was a huge increase in searches for toilet paper:

Source: How to Use SEO Data to Fuel Your Content Marketing Strategy in 2020

This chart shows an outlier in the general trend for toilet paper-related Google searches. The reason for the outlier? The outbreak of COVID-19 in North America. With a simple data visualization, we’ve been able to highlight an outlier and hint at a story behind the data. 

Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information. 

3. Tell a story within the data

Numbers on their own don’t tend to evoke an emotional response. But data visualization can tell a story that gives significance to the data. 

Designers use techniques like color theory , illustrations, design style and visual cues to appeal to the emotions of readers, put faces to numbers, and introduce a narrative to the data. 

Related : How to Tell a Story With Data (A Guide for Beginners)

For example, here’s an infographic created by World Vision. In the infographics, numbers are visualized using illustrations of cups. While comparing numbers might impress readers, reinforcing those numbers with illustrations helps to make an even greater impact. 

Source: World Vision

Meanwhile, this infographic uses data to draw attention to an often overlooked issue:

Read More:  The Coronavirus Pandemic and the Refugee Crisis

4. Reinforce an argument or opinion

When it comes to convincing people your opinion is right, they often have to see it to believe it. An effective infographic or chart can make your argument more robust and reinforce your creativity. 

For example, you can use a comparison infographic to compare sides of an argument, different theories, product/service options, pros and cons, and more. Especially if you’re blending data types.

5. Highlight an important point in a set of data

Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion. 

Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.

For example, in this data visualization, contrasting colors are used to emphasize the difference in the amount of waste sent to landfills versus recycled waste:

Here’s another example. This time, a red circle and an arrow are used to highlight points on the chart where the numbers show a drop: 

Highlighting specific data points helps your data visualization tell a compelling story.

6. Make books, blog posts, reports and videos more engaging

At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics. 

The added benefit of creating engaging visuals like infographics is that it has enabled our site to be featured in publications like The Wall Street Journal , Mashable , Business Insider , The Huffington Post and more. 

That’s because data visualizations are different from a lot of other types of content people consume on a daily basis. They make your brain work. They combine concrete facts and numbers with impactful visual elements. They make complex concepts easier to grasp. 

Here’s an example of an infographic we made that got a lot of media buzz:

Read the Blog Post: Every Betrayal Ever in Game of Thrones

We created this infographic because a bunch of people on our team are big Game of Thrones fans and we wanted to create a visual that would help other fans follow the show. Because we approached a topic that a lot of people cared about in an original way, the infographic got picked up by a bunch of media sites. 

Whether you’re a website looking to promote your content, a journalist looking for an original angle, or a creative building your portfolio, data visualizations can be an effective way to get people’s attention.

Data visualizations can come in many different forms. People are always coming up with new and creative ways to present data visually. 

Generally speaking, data visualizations usually fall under these main categories:

An infographic is a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic. 

While infographics can take many forms, they can typically be categorized by these infographic types:

• Statistical infographics
• Informational infographics
• Timeline infographics
• Process infographics
• Geographic infographics
• Comparison infographics
• Hierarchical infographics
• List infographics
• Resume infographics

Read More: What is an Infographic? Examples, Templates & Design Tips

Charts 

In the simplest terms, a chart is a graphical representation of data. Charts use visual symbols like line, bars, dots, slices, and icons to represent data points. 

Some of the most common types of charts are:

• Bar graphs /charts
• Line charts
• Bubble charts
• Stacked bar charts
• Word clouds
• Pictographs
• Area charts
• Scatter plot charts
• Multi-series charts

The question that inevitably follows is: what type of chart should I use to visualize my data? Does it matter?

Short answer: yes, it matters. Choosing a type of chart that doesn’t work with your data can end up misrepresenting and skewing your data. 

For example: if you’ve been in the data viz biz for a while, then you may have heard some of the controversy surrounding pie charts. A rookie mistake that people often make is using a pie chart when a bar chart would work better. 

Pie charts display portions of a whole. A pie chart works when you want to compare proportions that are substantially different. Like this:

CREATE THIS CHART TEMPLATE

But when your proportions are similar, a pie chart can make it difficult to tell which slice is bigger than the other. That’s why, in most other cases, a bar chart is a safer bet.

Here is a cheat sheet to help you pick the right type of chart for your data:

Want to make better charts? Make engaging charts with Venngage’s Chart Maker .

Related : How to Choose the Best Types of Charts For Your Data

Similar to a chart, a diagram is a visual representation of information. Diagrams can be both two-dimensional and three-dimensional. 

Some of the most common types of diagrams are:

• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Use case diagrams

Diagrams are used for mapping out processes, helping with decision making, identifying root causes, connecting ideas, and planning out projects.

CREATE THIS DIAGRAM TEMPLATE

Want to make a diagram ? Create a Venn diagram and other visuals using our free Venn Diagram Maker .

A map is a visual representation of an area of land. Maps show physical features of land like regions, landscapes, cities, roads, and bodies of water. 

Source: National Geographic

A common type of map you have probably come across in your travels is a choropleth map . Choropleth maps use different shades and colors to indicate average quantities. 

For example, a population density map uses varying shades to show the difference in population numbers from region to region:

Create your own map for free with Venngage’s Map Maker .

How to present data visually (data visualization best practices)

While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively. 

Not a designer? No problem. Venngage’s Graph Maker  will help you create better graphs in minutes.

1. Avoid distorting the data

This may be the most important point in this whole blog post. While data visualizations are an opportunity to show off your creative design chops, function should never be sacrificed for fashion. 

The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely. 

There are a number of different ways data can be distorted in a chart. Some common ways data can be distorted are:

• Making the baselines something other than 0 to make numbers seem bigger or smaller than they are – this is called “truncating” a graph
• Compressing or expanding the scale of the Y-axis to make a line or bar seem bigger or smaller than it should be
• Cherry picking data so that only the data points you want to include are on a graph (i.e. only telling part of the story)
• Using the wrong type of chart, graph or diagram for your data
• Going against standard, expected data visualization conventions

Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.

Read More: 5 Ways Writers Use Graphs To Mislead You

Want to create an engaging line graph? Use Venngage’s Line Graph Maker to create your own in minutes.

2. Avoid cluttering up your design with “chartjunk”

When it comes to best practices for data visualization, we should turn to one of the grandfather’s of data visualization: Edward Tufte. He coined the term “ chartjunk ”, which refers to the use of unnecessary or confusing design elements that skews or obscures the data in a chart. 

Here’s an example of a data visualization that suffers from chartjunk:

Source: ExcelUser

In this example, the image of the coin is distracting for readers trying to interpret the data. Note how the fonts are tiny – almost unreadable. Mistakes like this are common when a designers tries to put style before function. 

Read More : The Worst Infographics of 2020 (With Lessons for 2021)

3. Tell a story with your data

Data visualizations like infographics give you the space to combine data and narrative structure in one page. Visuals like icons and bold fonts let you highlight important statistics and facts.

For example, you could customize this data visualization infographic template to show the benefit of using your product or service (and post it on social media):

USE THIS TEMPLATE

  This data visualization relies heavily on text and icons to tell the story of its data:

This type of infographic is perfect for those who aren’t as comfortable with charts and graphs. It’s also a great way to showcase original research, get social shares and build brand awareness.

4. Combine different types of data visualizations

While you may choose to keep your data visualization simple, combining multiple types of charts and diagrams can help tell a more rounded story.

Don’t be afraid to combine charts, pictograms and diagrams into one infographic. The result will be a data visualization infographic that is engaging and rich in visual data.

Design Tip: This data visualization infographic would be perfect for nonprofits to customize and include in an email newsletter to increase awareness (and donations).

Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:

Design Tip: This infographic could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more.

Make your own bar graph in minutes with our free Bar Graph Maker .

5. Use icons to emphasize important points

Icons are perfect for attracting the eye when scanning a page. (Remember: use visual cues!)

If there are specific data points that you want readers to pay attention to, placing an icon beside it will make it more noticeable:

Design Tip: This infographic template would work well on social media to encourage shares and brand awareness.

You can also pair icons with headers to indicate the beginning of a new section.

Meanwhile, this infographic uses icons like bullet points to emphasize and illustrate important points. 

Design Tip: This infographic would make a great sales piece to promote your course or other service.  

6. Use bold fonts to make text information engaging

A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text. 

Even if you have a lot of text information, you can still create present data visually. Use bold, interesting fonts to make your data exciting. Just make sure that, above all else, your text is still easy to read.

This data visualization uses different fonts for the headers and body text that are bold but clear. This helps integrate the text into the design and emphasizes particular points:

Design Tip: Nonprofits could use this data visualization infographic in a newsletter or on social media to build awareness, but any business could use it to explain the need for their product or service. 

As a general rule of thumb, stick to no more than three different font types in one infographic.

This infographic uses one font for headers, another font for body text, and a third font for accent text. 

Read More: How to Choose Fonts For Your Designs (With Examples)

Design Tip: Venngage has a library of fonts to choose from. If you can’t find the icon you’re looking for , you can always request they be added. Our online editor has a chat box with 24/7 customer support.

7. Use colors strategically in your design

In design, colors are as functional as they are fashionable. You can use colors to emphasize points, categorize information, show movement or progression, and more. 

For example, this chart uses color to categorize data:

Design Tip : This pie chart can actually be customized in many ways. Human resources could provide a monthly update of people hired by department, nonprofits could show a breakdown of how they spent donations and real estate agents could show the average price of homes sold by neighbourhood.

You can also use light colored text and icons on dark backgrounds to make them stand out. Consider the mood that you want to convey with your infographic and pick colors that will reflect that mood. You can also use contrasting colors from your brand color palette.

This infographic template uses a bold combination of pinks and purples to give the data impact:

Read More: How to Pick Colors to Captivate Readers and Communicate Effectively

8. Show how parts make up a whole

It can be difficult to break a big topic down into smaller parts. Data visualization can make it a lot easier for people to conceptualize how parts make up a whole.

Using one focus visual, diagram or chart can convey parts of a whole more effectively than a text list can. Look at how this infographic neatly visualizes how marketers use blogging as part of their strategy:

Design Tip: Human resources could use this graphic to show the results of a company survey. Or consultants could promote their services by showing their success rates.

Or look at how this infographic template uses one focus visual to illustrate the nutritional makeup of a banana:

CREATE THIS FLYER TEMPLATE

9. Focus on one amazing statistic

If you are preparing a presentation, it’s best not to try and cram too many visuals into one slide. Instead, focus on one awe-inspiring statistic and make that the focus of your slide.

Use one focus visual to give the statistic even more impact. Smaller visuals like this are ideal for sharing on social media, like in this example:

Design Tip: You can easily swap out the icon above (of Ontario, Canada) using Venngage’s drag-and-drop online editor and its in-editor library of icons. Click on the template above to get started.

This template also focuses on one key statistic and offers some supporting information in the bar on the side:

10. Optimize your data visualization for mobile

Complex, information-packed infographics are great for spicing up reports, blog posts, handouts, and more. But they’re not always the best for mobile viewing. 

To optimize your data visualization for mobile viewing, use one focus chart or icon and big, legible font. You can create a series of mobile-optimized infographics to share multiple data points in a super original and attention-grabbing way.

For example, this infographic uses concise text and one chart to cut to the core message behind the data:

CREATE THIS SOCIAL MEDIA TEMPLATE

Some amazing data visualization examples

Here are some of the best data visualization examples I’ve come across in my years writing about data viz. 

Evolution of Marketing Infographic

Graphic Design Trends Infographic

Stop Shark Finning Nonprofit Infographic

Source: Ripetungi

Coronavirus Impact on Environment Data Visualization

What Disney Characters Tell Us About Color Theory

World’s Deadliest Animal Infographic

Source: Bill and Melinda Gates Foundation

The Secret Recipe For a Viral Creepypasta

Read More: Creepypasta Study: The Secret Recipe For a Viral Horror Story

The Hero’s Journey Infographic

Read More: What Your 6 Favorite Movies Have in Common

Emotional Self Care Guide Infographic

Source: Carley Schweet

Want to look at more amazing data visualization? Read More: 50+ Infographic Ideas, Examples & Templates for 2020 (For Marketers, Nonprofits, Schools, Healthcare Workers, and more)

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Types of Data Visualization and Their Uses

In today’s data-first business environment, the ability to convey complex information in an understandable and visually appealing manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as […]

In today’s data-first business environment, the ability to convey complex information in an understandable and  visually appealing  manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as 13 milliseconds.

In essence, data visualization is indispensable for distilling complex information into digestible formats that support both  quick comprehension  and informed decision-making. Its role in analysis and reporting underscores its value as a critical tool in any data-centric activity. 

Types of Data Visualization: Charts, Graphs, Infographics, and Dashboards

The diverse landscape of data visualization begins with simple charts and graphs but moves beyond infographics and animated dashboards.  Charts , in their various forms – be it bar charts for comparing quantities across categories or line charts depicting trends over time – serve as efficient tools for data representation. Graphs extend this utility further: Scatter plots reveal correlations between variables, while pie graphs offer a visual slice of proportional relationships within a dataset. 

Venturing beyond these traditional forms,  infographics  emerge as powerful storytelling tools, combining graphical elements with narrative to enlighten audiences on complex subjects. Unlike standard charts or graphs that focus on numerical data representation, infographics can incorporate timelines, flowcharts, and comparative images to weave a more comprehensive story around the data. 

A dashboard, when  effectively designed , serves as an instrument for synthesizing complex data into accessible and actionable insights. Dashboards very often encapsulate a wide array of information, from real-time data streams to historical trends, and present it through an amalgamation of charts, graphs, and indicators. 

A dashboard’s efficacy lies in its ability to tailor the visual narrative to the specific needs and objectives of its audience. By  selectively  filtering and highlighting critical data points, dashboards facilitate a focused analysis that aligns with organizational goals or individual projects. 

The best type of data visualization to use depends on the data at hand and the purpose of its presentation. Whether aiming to highlight trends, compare values, or elucidate complex relationships, selecting the appropriate visual form is crucial for effectively communicating insights buried within datasets. Through thoughtful design and strategic selection among these varied types of visualizations, one can illuminate patterns and narratives hidden within numbers – transforming raw data into meaningful knowledge.   

Other Types of Data Visualization: Maps and Geospatial Visualization  

Utilizing maps and geospatial visualization serves as a powerful method for uncovering and displaying insightful patterns hidden within complex datasets. At the intersection of geography and data analysis, this technique transforms numerical and categorical data into visual formats that are easily interpretable, such as heat maps, choropleths, or symbolic representations on geographical layouts. This approach enables viewers  to quickly grasp spatial relationships, distributions, trends, and anomalies that might be overlooked in traditional tabular data presentations. 

For instance, in public health,  geospatial visualizations  can highlight regions with high incidences of certain diseases, guiding targeted interventions. In environmental studies, they can illustrate changes in land use or the impact of climate change across different areas over time. By embedding data within its geographical context, these visualizations foster a deeper understanding of how location influences the phenomena being studied. 

Furthermore, the advent of interactive web-based mapping tools has enhanced the accessibility and utility of geospatial visualizations. Users can now engage with the data more directly – zooming in on areas of interest, filtering layers to refine their focus, or even contributing their own data points – making these visualizations an indispensable tool for researchers and decision-makers alike who are looking to extract meaningful patterns from spatially oriented datasets. 

Additionally,  scatter plots  excel in revealing correlations between two variables. By plotting data points on a two-dimensional graph, they allow analysts to discern potential relationships or trends that might not be evident from raw data alone. This makes scatter plots a staple in statistical analysis and scientific research where establishing cause-and-effect relationships is crucial. 

Bubble charts take the concept of scatter plots further by introducing a third dimension – typically represented by the size of the bubbles – thereby enabling an even more layered understanding of data relationships. Whether it’s comparing economic indicators across countries or visualizing population demographics, bubble charts provide a dynamic means to encapsulate complex interrelations within datasets, making them an indispensable tool for advanced data visualization. 

Innovative Data Visualization Techniques: Word Clouds and Network Diagrams 

Some innovative techniques have emerged in the realm of data visualization that not only simplify complex datasets but also enhance engagement and understanding. Among these, word clouds and network diagrams stand out for their  unique approaches  to presenting information. 

Word clouds represent textual data with size variations to emphasize the frequency or importance of words within a dataset. This technique transforms qualitative data into a visually appealing format, making it easier to identify dominant themes or sentiments in large text segments.

Network diagrams introduce an entirely different dimension by illustrating relationships between entities. Through nodes and connecting lines, they depict how individual components interact within a system – be it social networks, organizational structures, or technological infrastructures. This visualization method excels in uncovering patterns of connectivity and influence that might remain hidden in traditional charts or tables. 

Purpose and Uses of Each Type of Data Visualization 

The various types of data visualization – from bar graphs and line charts to heat maps and scatter plots – cater to different analytical needs and objectives. Each type is meticulously designed to highlight specific aspects of the data, making it imperative to understand their unique applications and strengths. This foundational knowledge empowers users to select the most effective visualization technique for their specific dataset and analysis goals.

Line Charts: Tracking Changes Over Time  Line charts are quintessential in the realm of data visualization for their simplicity and effectiveness in showcasing trends and changes over time. By connecting individual data points with straight lines, they offer a clear depiction of how values rise and fall across a chronological axis. This makes line charts particularly useful for tracking the evolution of quantities – be it the fluctuating stock prices in financial markets, the ebb and flow of temperatures across seasons, or the gradual growth of a company’s revenue over successive quarters. The visual narrative that line charts provide helps analysts, researchers, and casual observers alike to discern patterns within the data, such as cycles or anomalies.    

Bar Charts and Histograms: Comparing Categories and   Distributions  Bar charts  are highly suitable for representing comparative data. By plotting each category of comparison with a bar whose height or length reflects its value, bar charts make it easy to visualize relative values at a glance.

Histograms  show the distribution of groups of data in a dataset. This is particularly useful for understanding the shape of data distributions – whether they are skewed, normal, or have any outliers. Histograms provide insight into the underlying structure of data, revealing patterns that might not be apparent.  

Pie Charts: Visualizing Proportional Data   Pie charts  serve as a compelling visualization tool for representing proportional data, offering a clear snapshot of how different parts contribute to a whole. By dividing a circle into slices whose sizes are proportional to their quantity, pie charts provide an immediate visual comparison among various categories. This makes them especially useful in illustrating market shares, budget allocations, or the distribution of population segments.

The simplicity of pie charts allows for quick interpretation, making it easier for viewers to grasp complex data at a glance. However, when dealing with numerous categories or when precise comparisons are necessary, the effectiveness of pie charts may diminish. Despite this limitation, their ability to succinctly convey the relative significance of parts within a whole ensures their enduring popularity in data visualization across diverse fields. 

Scatter Plots: Identifying Relationship and Correlations Between Variables Scatter plots  are primarily used for spotting relationships and correlations between variables. These plots show data points related to one variable on one axis and a different variable on another axis. This visual arrangement allows viewers to determine patterns or trends that might indicate a correlation or relationship between the variables in question. 

For instance, if an increase in one variable consistently causes an increase (or decrease) in the other, this suggests a potential correlation. Scatter plots are particularly valuable for preliminary analyses where researchers seek to identify variables that warrant further investigation. Their straightforward yet powerful nature makes them indispensable for exploring complex datasets, providing clear insights into the dynamics between different factors at play. 

Heat Maps: Representing Complex Data Matrices through Color Gradients Heat maps  serve as a powerful tool in representing complex data matrices, using color gradients to convey information that might otherwise be challenging to digest. At their core, heat maps transform numerical values into a visual spectrum of colors, enabling viewers to quickly grasp patterns, outliers, and trends within the data. This method becomes more effective when the complex relationships between multiple variables need to be reviewed.  

For instance, in fields like genomics or meteorology, heat maps can illustrate gene expression levels or temperature fluctuations across different regions and times. By assigning warmer colors to higher values and cooler colors to lower ones, heat maps facilitate an intuitive understanding of data distribution and concentration areas, making them indispensable for exploratory data analysis and decision-making processes.

Dashboards and Infographics: Integrating Multiple Data Visualizations  Dashboards and infographics represent a synergistic approach in data visualization, blending various graphical elements to offer a holistic view of complex datasets.  Dashboards,  with their capacity to integrate multiple data visualizations such as charts, graphs, and maps onto a single interface, are instrumental in monitoring real-time data and tracking performance metrics across different parameters. They serve as an essential tool for decision-makers who require a comprehensive overview to identify trends and anomalies swiftly. 

Infographics, on the other hand, transform intricate data sets into engaging, easily digestible visual stories. By illustrating strong narratives with striking visuals and solid statistics, infographics make complex information easily digestible to any type of audience. 

Together, dashboards and infographics convey multifaceted data insights in an integrated manner – facilitating informed decisions through comprehensive yet clear snapshots of data landscapes.     

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends.  Harvard Business Review  (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We’ll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or  Design Thinking  sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate  workflows .  Data modeling  also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn’t limited to performance dashboards. For example, while  text mining  an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

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The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published  The Visual Display of Quantitative Information  (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

• Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
• Pie charts and stacked bar charts:  These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
• Line charts and area charts:  These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
• Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.
• Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
• Heat maps:  These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.
• Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

• D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers.  D3.js  (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.
• ECharts:  A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc.  Echarts  (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.
• Vega:   Vega  (link resides outside ibm.com) defines itself as “visualization grammar,” providing support to customize visualizations across large datasets which are accessible from the web.
• deck.gl: It is part of Uber's open source visualization framework suite.  deck.gl  (link resides outside ibm.com) is a framework, which is used for  exploratory data analysis  on big data. It helps build high-performance GPU-powered visualization on the web.

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You’ll want the data that you provide to motivate people to act within their scope of their role. If you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual:  Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple:  Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

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Get to know the types of data visualization charts and graphs

Uncover the diverse world of data visualization types, from basics like columns to advanced models. Elevate your data storytelling!

Data visualization is a powerful tool that transforms complex information into easily understandable visuals, helping in insightful decision-making or effective storytelling.

Among the many data visualization options available, understanding the basics is crucial for choosing the right representation for your data. In this exploration of data visualization models, we delve into fundamental types, such as column charts or bar charts, and move to specialized charts like Histogram, Waterfall or Marimekko. 

Whether you are comparing quantities, tracking trends over time, or revealing relationships between variables, this guide provides insights into which data visualization to use for your needs.

Basic types of data visualization charts and graphs

In this section, we'll delve into the basic charts and graphs that are the most commonly used ones. If you are hungry for more advanced and specialized visualization types, jump straight to the next section.

Column Chart

A column chart visually represents numerical values using vertical columns. Each column's height corresponds to the value it represents, making it an effective tool for comparing quantities across different categories or tracking changes over time. The key distinction from a bar chart is the orientation of the columns—vertical instead of horizontal.

Column charts are best utilized when showcasing comparisons between individual items, tracking changes over distinct categories, or emphasizing the magnitude of values. They offer a clear and straightforward way to illustrate data, making them widely applicable in various scenarios.

Similar to a column chart, a bar chart visually represents numerical values using rectangular bars. Each bar's length corresponds to the value it represents, making it effective for comparing quantities across different categories or tracking changes over time. Bar charts are particularly useful when dealing with discrete categories, and they offer a clear and straightforward way to illustrate comparisons.

This type of chart is best employed when showcasing comparisons between individual items, tracking changes over distinct categories, or emphasizing the magnitude of values. It is widely used in various scenarios, such as comparing sales figures for different products, displaying the performance of teams or departments, or visualizing survey results with distinct options.

Stacked Bar Chart

Derivative of a bar chart, a stacked bar chart is a type of data visualization that displays multiple datasets as bars, where each bar is divided into segments representing different subcategories or components. The total height of the bar represents the combined value of all segments, and each segment's length corresponds to its specific value within the category.

This type of chart is best used when you want to illustrate the total magnitude of a category while showing the composition of subcategories. Stacked bar charts are effective for comparing the contribution of each subcategory to the overall total. They are suitable for situations where you want to emphasize both the individual components and the overall pattern or trend.

A line chart, also known as a line graph, is a visual representation of data points connected by straight lines. One of the most popular data visualization graph types. This chart type is particularly useful for displaying trends and patterns over time, making it an effective choice for time-series data analysis. The x-axis typically represents time or another continuous variable, while the y-axis represents the values being measured.

Line charts excel in illustrating the overall direction and trajectory of data, emphasizing changes, fluctuations, or trends. They are ideal for showcasing continuous data sets and revealing relationships between variables.

Scatter Plot

A scatter plot is a data visualization technique that represents individual data points on a two-dimensional graph. Each point on the graph corresponds to a pair of values, with one value plotted on the x-axis and the other on the y-axis. Scatter plots are particularly useful for identifying relationships, correlations, or patterns between two variables.

This type of chart is best used when analyzing the correlation between two quantitative variables, allowing for the observation of trends and the identification of outliers. Scatter plots are beneficial for visualizing the distribution and clustering of data points, providing insights into the nature of the relationship between the variables.

Area Chart/Map

An area chart represents data points connected with lines, and an area map is a geospatial visualization displaying values over a map. The space between the line and the x-axis is filled, creating a shaded area that represents the quantity being measured.

This type of chart is best used for showing trends over time or comparing quantities in different categories, particularly suitable for time-series data, geographical data, and data with clear patterns over a continuous range.

Specialized types of data visualization charts and graphs

Moving beyond the foundational charts, we now delve into specialized types of data visualization that can serve distinct analytical needs. These visualizations offer nuanced insights and are tailored for specific scenarios, from illustrating hierarchical structures with Marimekko charts to showcasing relationships in a radial manner with radar charts.

A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, and the size of each slice corresponds to the quantity it represents relative to the total.

This type of chart is best used when you want to show the distribution of parts within a whole and emphasize the percentage contribution of each part. Pie charts are effective for illustrating simple relationships and conveying the share of each category in relation to the entire dataset.

Histograms are data visualization graphs representing the distribution of a dataset. It consists of a series of contiguous bars, where each bar represents the frequency (or count) of data falling within a specific range or "bin." The bars are usually adjacent and have no gaps between them, emphasizing the continuity of the data.

This type of chart is best used when you want to visualize the underlying frequency distribution of a continuous dataset and understand the pattern or shape of the data. Histograms are particularly useful for identifying central tendency, spread, and skewness in the distribution.

Box-and-whisker Plot

A box-and-whisker plot, also known as a boxplot, is a graphical representation of the distribution of a dataset, providing a summary of its central tendency, spread, and identification of outliers. The plot consists of a rectangular "box" and two "whiskers" extending from the box.

The box represents the interquartile range (IQR), with the central line inside indicating the median. The whiskers extend to the minimum and maximum values within a defined range or as determined by a statistical criterion. Any data points beyond the whiskers are considered outliers.

A treemap is a hierarchical data visualization that uses nested rectangles to represent the hierarchical structure of the data. The size and color of each rectangle convey information about the quantity or value of the data it represents. Treemaps are often used to visualize hierarchical data structures, where each branch of the hierarchy is represented by a nested rectangle.

This type of chart is best used when you want to display the hierarchical structure of a dataset and emphasize the relative proportions of each branch within the hierarchy. Treemaps are effective for visualizing large and complex datasets with multiple levels of categorization.

Bubble Chart

A bubble chart is a data visualization that displays three-dimensional data points using circles or bubbles. Each bubble represents a data point, and its position on the chart is determined by its x and y coordinates. Additionally, the size of the bubble represents a third numerical dimension, usually indicating the magnitude or value associated with the data point.

This type of chart is best used when you want to visualize relationships between three variables and emphasize the magnitude of each data point. Bubble charts are effective for showing patterns, trends, and correlations within datasets with multiple dimensions.

Radar Chart

A radar chart, also known as a radar polygon or radar triangle, is a data visualization that displays multivariate data in a radial manner. The chart consists of a series of spokes, each representing a different variable or category, and data points are plotted along these spokes to create a polygon or triangle shape. The area enclosed by the shape reflects the overall pattern or performance across the variables.

This type of chart is best used when you want to compare the values of multiple variables for a single data point. Radar charts are effective for highlighting patterns, strengths, and weaknesses across different categories, making them suitable for performance analysis, feature comparison, and showcasing profiles with multiple dimensions.

A heat map is a data visualization technique that uses color gradients to represent the values of a matrix or a two-dimensional dataset. In a heat map, each cell's color is determined by the data it represents, with variations in color intensity indicating different levels of values. Heat maps are often used to reveal patterns, trends, and variations in large datasets, making complex information more accessible.

This type of chart is best used when you want to visualize the distribution and relationships between two categorical variables or the intensity of a numerical variable across two dimensions. Heat maps are particularly effective for identifying concentrations, clusters, or trends within data and are widely utilized in various fields, including finance, biology, and user experience analysis.

Dual-Axis Chart

A dual-axis chart is a technique that combines two different types of data visualization graphs or charts within the same plot area, utilizing two separate y-axes that share a common x-axis. This approach enables the simultaneous representation of two distinct datasets with different units or scales, providing a comprehensive view of their relationships and trends.

This type of chart is best used when you want to compare two sets of data that have different units of measurement but share a common independent variable. The dual-axis chart allows for the visual exploration of correlations, patterns, or divergences between the two datasets. It is particularly effective when there is a potential cause-and-effect relationship or when changes in one variable may influence the other.

Network Graphs

Network graphs, also known as network diagrams, are a type of data visualization that represents relationships and connections between entities. In a network graph, nodes (representing entities) are connected by edges (representing relationships or interactions). This visualization method is particularly useful for illustrating complex relationships, dependencies, and interactions within a system or dataset.

This type of chart is best used when you want to explore and communicate relationships between various elements in a network. Network graphs are commonly employed in diverse fields such as social network analysis, biology (e.g., depicting protein-protein interactions), transportation systems, and organizational structures.

A Choropleth is a type of data visualization map that represents statistical data through various shading patterns or colors on predefined geographic areas such as countries, states, or regions. The intensity of the color or shading in each area corresponds to the value of the data being represented. 

Choropleth maps are particularly useful for visualizing spatial patterns, distributions, or variations of a specific variable across different geographical regions. They are commonly employed in fields like demographics, economics, and epidemiology to illustrate regional disparities, concentrations, or trends within a dataset. 

Waterfall Chart

A waterfall chart is a data visualization tool used to illustrate the cumulative effect of sequentially introduced positive or negative values. It displays how an initial value changes over a series of intermediate values, leading to a final cumulative total. The chart visually resembles cascading waterfalls, with each step representing a different part of the overall change.

This type of chart is best used when you want to depict the contributions of individual components to a total value, especially in financial or project management contexts. Waterfall charts are valuable for showcasing the incremental impact of various factors on the overall outcome.

Funnel Chart

A funnel chart is a visual representation of a process that narrows down progressively, highlighting the reduction in the number of elements at each stage. It resembles an inverted pyramid, where the top represents the initial stage, and subsequent sections illustrate the decreasing quantities as the process unfolds.

This type of chart is best used to depict the stages of a sequential process, emphasizing the diminishing volume or value at each step. Funnel charts are particularly popular in marketing and sales analytics to illustrate the conversion rates at different stages of a sales or marketing funnel.

Gantt Chart

A Gantt chart is a horizontal bar chart that visually represents the schedule and progress of tasks or activities over time. It provides a timeline view of project activities, allowing project managers and teams to plan, coordinate, and track the execution of tasks throughout the project lifecycle.

This type of chart is best used for project management to illustrate the start and end dates of individual tasks, as well as their dependencies and overall project timeline. Gantt charts are particularly effective in displaying the sequential order of tasks and the duration each task is expected to take.

Bullet Graph

A bullet graph is a specialized type of bar chart designed to display the progress or performance of a metric against pre-defined benchmarks or goals. It provides a concise and clear representation of how well a particular metric is performing in relation to the expected target or range.

This type of data visualization graphs are best used when there is a need to communicate performance metrics effectively, such as key performance indicators (KPIs) or sales targets. Bullet graphs are particularly suitable for scenarios where a single metric needs to be assessed against various benchmarks or comparative measures.

Polar Graph

A polar graph, also known as a radial chart, is a two-dimensional graph in which data points are plotted using polar coordinates. Unlike Cartesian coordinates, where points are defined by x and y values, polar coordinates use a radial distance and an angular direction to represent data. The graph is centered around a point, and data is plotted based on angles and distances from that center.

Polar graphs are particularly suitable for visualizing data that has a circular or cyclical nature, making them effective for displaying periodic patterns, trends, or relationships. The circular arrangement of data points is ideal for representing information that is distributed around a central point in a way that emphasizes the angular aspect of the data.

Marimekko Chart

A Marimekko chart, also referred to as a mosaic plot or matrix chart, is a two-dimensional stacked chart used to visualize categorical data. In this chart, rectangles represent the proportion of each category within different segments. The width of each rectangle signifies the proportion of a specific category, while the height represents the proportion of that category within a segment. Segments are usually organized along one axis, and rectangles within each segment are stacked to illustrate cumulative contributions.

Effective Marimekko chart design involves careful labeling, color differentiation for clarity, and organizing segments in a meaningful order. The visual representation provided by Marimekko charts aids in quickly identifying patterns, trends, and relative proportions within complex datasets, making it a valuable tool for decision-makers and analysts.

Radial Wheel

Radial wheel charts, also known as radial bar charts or radial graphs, are circular data visualization graphs that display data using spokes or bars extending from the center outward. Each spoke or bar represents a category, and the length or position of the spoke indicates the magnitude or value of the corresponding data.

This type of chart is best used for presenting data with distinct categories that radiate from a central point. It is effective for displaying proportions, comparisons, or distributions within a circular context. Radial wheel charts are commonly employed in scenarios where a clear visual representation of relative values around a central theme is beneficial.

Pyramid Chart

A pyramid chart is a graphical representation that resembles a pyramid, with layers of varying widths, representing different hierarchical levels or data categories. The width of each layer corresponds to the quantity or proportion it represents within the overall structure.

This type of chart is best used to illustrate hierarchical relationships, distribution of values, or the progression of data from a broad base to a narrower top. Pyramid charts are commonly employed in business scenarios to depict organizational structures, population distributions, or any hierarchical data with diminishing proportions. They provide a visually engaging way to showcase the diminishing significance of each layer as it ascends toward the pinnacle of the pyramid.

Multi-Layer Pie Chart

A multi-layer pie chart is a variant of the traditional pie chart that consists of multiple rings or layers, each representing a different set of data. Each layer is divided into segments, and the size of each segment corresponds to the proportion of the total within that layer.

This type of chart is best used when there is a need to display hierarchical or nested data with multiple levels of categorization. It is effective in illustrating the composition of each category within a broader context. Multi-layer pie charts provide a visually appealing way to convey complex relationships or the distribution of data across multiple dimensions, making them suitable for presenting categorical data with varying levels of granularity.

A PERT (Program Evaluation and Review Technique) chart is a project management tool that visualizes the tasks involved in completing a project and the dependencies between them. It uses a network diagram to represent the sequence and relationships among different project activities.

This type of chart is best used in project planning and scheduling to identify the critical path, understand task dependencies, and estimate the time required for project completion. PERT charts are particularly useful for complex projects with interdependent activities, as they help project managers allocate resources efficiently and manage the workflow effectively.

If you found the advanced types of visualization interesting why not check out our Data Visualization Tips ?

Miscellaneous types of data visualization charts and graphs

A table is a structured arrangement of data in rows and columns, providing a clear and organized way to present information. Tables are commonly used in various contexts, including data analysis, statistics, and database management. Each row in a table typically represents a record or observation, while each column represents a specific attribute or variable.

Tables are highly versatile and applicable across different domains. They are particularly useful for displaying numerical data, making comparisons, and organizing information systematically. In business reports, academic research, and scientific presentations, tables are often employed to present data in a tabular format, making it easier for readers to interpret and analyze. The simplicity and clarity of tables make them effective tools for conveying structured information, and their use extends to areas such as spreadsheets, databases, and document preparation.

Pivot Tables

A bit more advanced type of a table,  pivot table is a data processing tool used in spreadsheet programs like Microsoft Excel or Google Sheets. It allows users to summarize, analyze, and interpret large datasets by transforming and reorganizing the information. Pivot tables are particularly effective for creating insightful reports and gaining valuable insights from complex data.

The primary function of a pivot table is to enable users to rearrange and analyze data dynamically. Users can drag and drop fields within the table to organize information based on different criteria, such as categories, time periods, or numerical values. The table then automatically performs calculations, such as sums, averages, counts, or percentages, depending on the user's preferences.

Highlight Table

Another specific type of a table, highlight table is a type of data visualization that uses color to emphasize and categorize values within a table. Each cell in the table is colored based on its data, providing a quick visual summary of the information. The color variations help highlight patterns, trends, or specific data points, making it easier for the audience to interpret the data.

This type of visualization is best used when there is a need to quickly identify and compare values within a large dataset. It is particularly effective for presenting data with clear patterns or significant variances, allowing stakeholders to focus on key insights. Highlight tables are commonly employed in data analysis, business intelligence, and reporting to enhance the visibility of important information.

A flowchart is a graphical representation of a process, displaying the steps and decisions involved in a system or workflow. It uses various shapes, symbols, and arrows to illustrate the sequence of actions and the flow of information within a process.

Flowcharts are versatile tools that can be applied in various fields, such as software development, business processes, project management, and decision-making. They are especially useful for visualizing complex procedures, identifying bottlenecks, and improving the efficiency of a process. Flowcharts facilitate communication and understanding among team members, stakeholders, and decision-makers by providing a clear and structured overview of how a process unfolds. Whether used to design new processes or analyze existing ones, flowcharts are instrumental in streamlining workflows and fostering better organizational comprehension.

A timeline is a graphical representation that displays a chronological sequence of events over a specific period. It presents a visual overview of historical, project-related, or sequential data, allowing viewers to understand the temporal progression of activities. Timelines typically use a horizontal axis to represent time, and events or milestones are marked along this axis.

Timelines are versatile tools used in various contexts, such as history, project management, and personal planning. In historical contexts, timelines illustrate the order of significant events, helping individuals comprehend historical narratives. In project management, timelines map out tasks, deadlines, and dependencies, aiding in project planning and tracking. Personal timelines can be used for planning life events, educational milestones, or career progressions.

Venn Diagram

A Venn diagram is a visual representation that illustrates the relationships between different sets or groups. It consists of overlapping circles, each representing a set, with the overlapping areas indicating common elements shared between the sets. Venn diagrams are valuable for displaying the intersections and differences among various data categories or concepts.

These diagrams are commonly used to depict logical relationships, highlighting the similarities and distinctions between different entities. Venn diagrams are particularly useful when showcasing the correlation between groups or when analyzing data with multiple attributes. They provide a clear visual structure that helps viewers comprehend the shared and exclusive characteristics of each set.

In various fields such as mathematics, statistics, and problem-solving, Venn diagrams are employed to simplify complex relationships and aid in logical reasoning. They are also prevalent in business presentations, educational materials, and scientific research to convey overlapping concepts or categories effectively.

A tree chart, also known as a hierarchical chart or tree diagram, is a visual representation of hierarchical structures or relationships among various entities. It resembles an inverted tree with branches and nodes, where each level represents a different set of categories or classifications. Tree charts are widely used to illustrate parent-child relationships, organizational structures, or any hierarchical information.

These charts are best utilized when showcasing the hierarchical relationships within a system, organization, or classification. They are often employed in organizational charts, family trees, project hierarchies, and classification systems.

For example, in project management, a tree chart can represent the breakdown of tasks and subtasks, showing the hierarchy of project components. In genealogy, a family tree chart displays the relationships between generations. 

A mind map is a visual representation of ideas, concepts, or information arranged around a central theme or topic. It is a graphical tool that uses branching and connections to illustrate relationships between different elements. Typically, the central idea is placed in the center, and related concepts radiate outward in a non-linear, organic structure.

Mind maps are effective for brainstorming, organizing thoughts, and representing the interconnectedness of various concepts. They provide a holistic view of a subject, allowing for creative exploration and capturing associations between different ideas.

Concentric Circles

Concentric circles are a visual representation where multiple circles share the same center but have different radii. This type of chart is characterized by the arrangement of circles within one another, creating a series of nested rings. The size of each circle and its position relative to others can convey different dimensions of information.

Concentric circles are often used for data visualization where the magnitude or proportion of values is represented by the size or area of the circles. Each ring can symbolize a distinct category, and the size differences between circles help illustrate variations in the data.

A gauge chart, also known as a dial chart or speedometer chart, is a visual representation designed to display a single metric or value within a specific range. It resembles a speedometer with a needle pointing to a value on a circular scale. The needle position indicates where the current value falls within the defined range.

Gauge charts are effective for presenting a single data point in comparison to a predetermined set of benchmarks, thresholds, or goals. The visual appeal of a gauge chart lies in its simplicity and ease of interpretation. Users can quickly assess whether the metric is within an acceptable range, below, or above expectations.

Half Donut Chart

A half donut chart is a variation of a traditional donut chart, displaying data in a half-circle or semicircle instead of a full circle. Like a standard donut chart, it conveys information using sectors and is useful for representing parts of a whole. The circular nature allows for easy visualization of proportions and comparisons.

This type of chart is best used for showing the percentage distribution of different categories within a total, making it effective for scenarios where you want to emphasize proportions or parts of a whole. It is particularly suitable for situations where you want to highlight specific data points or segments in a visually appealing and concise manner.

An icon array is a data visualization technique that represents numerical information using a grid of icons or symbols. Each icon in the array typically corresponds to a specific quantity or data point. The size, shape, or color of the icons may be used to convey additional information, such as the magnitude or category of the data.

Icon arrays are best used when visualizing categorical or discrete data where individual items or counts are significant. They provide a straightforward and intuitive way to communicate quantities, making them suitable for presentations, infographics, or reports where a visual representation can enhance understanding. Icon arrays are particularly effective in conveying proportions, percentages, or relative frequencies across different categories or groups.

A cone chart is a three-dimensional data visualization that uses cone-shaped elements to represent numerical values. Each cone in the chart typically has a base size or height proportional to a specific data point. These charts are often used to illustrate hierarchical structures, relationships, or distributions within the data.

Cone charts are best employed when you need to display hierarchical relationships or when comparing the magnitude of values across different categories. They can be effective in scenarios where the data has a natural hierarchical structure, and you want to emphasize the proportions or levels within that hierarchy. Cone charts add a visual dimension to the data, making them suitable for presentations, reports, or dashboards where a more engaging representation is desired.

Ready to rock the data visualization types you have just learned?

Congratulations! You've now gained a comprehensive understanding of various data visualization types and their applications. Armed with this knowledge, you're well-equipped to choose the right charts and graphs for your data, depending on your specific needs and the story you want to tell.

Remember, effective data visualization is not just about creating aesthetically pleasing charts; it's about conveying information in a way that is clear, insightful, and impactful. As you embark on your data visualization journey, keep these key takeaways in mind:

Know Your Data - Understand the nature of your data, whether it's categorical, numerical, or temporal. Different types of data call for different visualization techniques.

Choose the Right Chart - Select the visualization type that best suits your data and your communication goals. Whether it's a bar chart for comparisons, a line chart for trends, or a pie chart for proportions, each type serves a specific purpose.

Tell a Story - Whether you're presenting business metrics, analyzing trends, or conveying research findings, the ability to create meaningful and engaging visualizations is a powerful skill. As you integrate these diverse visualization types into your reports, you'll not only enhance your data storytelling but also empower others to gain valuable insights from the information you present.

So go ahead, rock those charts, and make your data shine with Vizzu!

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18 Best Types of Charts and Graphs for Data Visualization [+ Guide]

Published: May 22, 2024

As a writer for the marketing blog, I frequently use various types of charts and graphs to help readers visualize the data I collect and better understand their significance. And trust me, there's a lot of data to present.

In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

• Motivate your team to take action.
• Impress stakeholders with goal progress.
• Show your audience what you value as a business.

Data visualization builds trust and can organize diverse teams around new initiatives. So, I'm going to talk about the types of graphs and charts that you can use to grow your business.

And, if you still need a little more guidance by the end of this post, check out our data visualization guide for more information on how to design visually stunning and engaging charts and graphs.  

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Charts vs Graphs: What's the Difference?

A lot of people think charts and graphs are synonymous (I know I did), but they're actually two different things.

Charts visually represent current data in the form of tables and diagrams, but graphs are more numerical in data and show how one variable affects another.

For example, in one of my favorite sitcoms, How I Met Your Mother, Marshall creates a bunch of charts and graphs representing his life. One of these charts is a Venn diagram referencing the song "Cecilia" by Simon and Garfunkle. 

Marshall says, "This circle represents people who are breaking my heart, and this circle represents people who are shaking my confidence daily. Where they overlap? Cecilia."

The diagram is a chart and not a graph because it doesn't track how these people make him feel over time or how these variables are influenced by each other.

It may show where the two types of people intersect but not how they influence one another.

Later, Marshall makes a line graph showing how his friends' feelings about his charts have changed in the time since presenting his "Cecilia diagram.

Note: He calls the line graph a chart on the show, but it's acceptable because the nature of line graphs and charts makes the terms interchangeable. I'll explain later, I promise.

The line graph shows how the time since showing his Cecilia chart has influenced his friends' tolerance for his various graphs and charts. 

Image source

I can't even begin to tell you all how happy I am to reference my favorite HIMYM joke in this post.

Now, let's dive into the various types of graphs and charts. 

Different Types of Graphs for Data Visualization

1. bar graph.

I strongly suggest using a bar graph to avoid clutter when one data label is long or if you have more than 10 items to compare. Also, fun fact: If the example below was vertical it would be a column graph.

Best Use Cases for These Types of Graphs

Bar graphs can help track changes over time. I've found that bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

• Product comparisons.
• Product usage.
• Category comparisons.
• Marketing traffic by month or year.
• Marketing conversions.

Design Best Practices for Bar Graphs

• Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.

You should also use horizontal labels to improve its readability, and start the y-axis at 0 to appropriately reflect the values in your graph.

2. Line Graph

A line graph reveals trends or progress over time, and you can use it to show many different categories of data. You should use it when you track a continuous data set.

This makes the terms line graphs and line charts interchangeable because the very nature of both is to track how variables impact each other, particularly how something changes over time. Yeah, it confused me, too.

Line graphs help users track changes over short and long periods. Because of this, I find these types of graphs are best for seeing small changes.

Line graphs help me compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.

A business might use this graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs

• Use solid lines only.
• Don't plot more than four lines to avoid visual distractions.
• Use the right height so the lines take up roughly 2/3 of the y-axis' height.

3. Bullet Graph

A bullet graph reveals progress towards a goal, compares this to another measure, and provides context in the form of a rating or performance.

In the example above, the bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, I could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

• Customer satisfaction.
• Average order size.
• New customers.

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. YBullet graphs can also visualize:

• Customer satisfaction scores.
• Customer shopping habits.
• Social media usage by platform.

Design Best Practices for Bullet Graphs

• Use contrasting colors to highlight how the data is progressing.
• Use one color in different shades to gauge progress.

4. Column + Line Graph

Column + line graphs are also called dual-axis charts. They consist of a column and line graph together, with both graphics on the X axis but occupying their own Y axis.

Download our FREE Excel Graph Templates for this graph and more!

Best Use Cases

These graphs are best for comparing two data sets with different measurement units, such as rate and time. 

As a marketer, you may want to track two trends at once.

Design Best Practices 

Use individual colors for the lines and colors to make the graph more visually appealing and to further differentiate the data. 

The Four Basic Types of Charts

Before we get into charts, I want to touch on the four basic chart types that I use the most. 

1. Bar Chart

Bar charts are pretty self-explanatory. I use them to indicate values by the length of bars, which can be displayed horizontally or vertically. Vertical bar charts, like the one below, are sometimes called column charts. 

2. Line Chart 

I use line charts to show changes in values across continuous measurements, such as across time, generations, or categories. For example, the chart below shows the changes in ice cream sales throughout the week.

3. Scatter Plot

A scatter plot uses dotted points to compare values against two different variables on separate axes. It's commonly used to show correlations between values and variables. 

4. Pie Chart

Pie charts are charts that represent data in a circular (pie-shaped) graphic, and each slice represents a percentage or portion of the whole. 

Notice the example below of a household budget. (Which reminds me that I need to set up my own.)

Notice that the percentage of income going to each expense is represented by a slice. 

Different Types of Charts for Data Visualization

To better understand chart types and how you can use them, here's an overview of each:

1. Column Chart

Use a column chart to show a comparison among different items or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Best Use Cases for This Type of Chart

I use both column charts to display changes in data, but I've noticed column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar charts  show data horizontally.

For example, warehouses often track the number of accidents on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

• Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
• Sales volume, like showing which services are the top sellers each month or the number of sales per week.
• Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts

• Use horizontal labels to improve readability.
• Start the y-axis at 0 to appropriately reflect the values in your chart .

2. Area Chart

Okay, an area chart is basically a line chart, but I swear there's a meaningful difference.

The space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year.

It helps me analyze both overall and individual trend information.

Best Use Cases for These Types of Charts

Area charts help show changes over time. They work best for big differences between data sets and help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These charts make the size of a group and how groups relate to each other more visually important than data changes over time.

Area charts  can help your business to:

• Visualize which product categories or products within a category are most popular.
• Show key performance indicator (KPI) goals vs. outcomes.
• Spot and analyze industry trends.

Design Best Practices for Area Charts

• Use transparent colors so information isn't obscured in the background.
• Don't display more than four categories to avoid clutter.
• Organize highly variable data at the top of the chart to make it easy to read.

3. Stacked Bar Chart

I suggest using this chart to compare many different items and show the composition of each item you’re comparing.

These charts  are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view — when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These charts  can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say but not a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

• Show the frequency of survey responses.
• Identify outliers in historical data.
• Compare a part of a strategy to its performance as a whole.

Design Best Practices for Stacked Bar Charts

• Best used to illustrate part-to-whole relationships.
• Use contrasting colors for greater clarity.
• Make the chart scale large enough to view group sizes in relation to one another.

4. Mekko Chart

Also known as a Marimekko chart, this type of chart  can compare values, measure each one's composition, and show data distribution across each one.

It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values — instead of time progression, like column charts often do. In the graphic below, the x-axis compares the cities to one another.

Image Source

I typically use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart clarifies which firms manage the most assets in different areas.

It's also easy to see which asset managers are the largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts, so it's best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

• Detailed profit and loss statements.
• Revenue by brand and region.
• Product profitability.
• Share of voice by industry or niche.

Design Best Practices for Mekko Charts

• Vary your bar heights if the portion size is an important point of comparison.
• Don't include too many composite values within each bar. Consider reevaluating your presentation if you have a lot of data.
• Order your bars from left to right in such a way that exposes a relevant trend or message.

5. Pie Chart

Remember, a pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

The image above shows another example of customers by role in the company.

The bar chart  example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

• Customer personas in relation to all customers.
• Revenue from your most popular products or product types in relation to all product sales.
• Percent of total profit from different store locations.

Design Best Practices for Pie Charts

• Don't illustrate too many categories to ensure differentiation between slices.
• Ensure that the slice values add up to 100%.
• Order slices according to their size.

6. Scatter Plot Chart

As I said earlier, a scatter plot or scattergram chart will show the relationship between two different variables or reveal distribution trends.

Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or understanding your data's distribution.

Scatter plots are helpful in situations where you have too much data to see a pattern quickly. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

This type of chart  makes it easy to compare two data sets. Use cases might include:

• Employment and manufacturing output.
• Retail sales and inflation.
• Visitor numbers and outdoor temperature.
• Sales growth and tax laws.

Try to choose two data sets that already have a positive or negative relationship. That said, this type of chart  can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots

• Include more variables, like different sizes, to incorporate more data.
• Start the y-axis at 0 to represent data accurately.
• If you use trend lines, only use a maximum of two to make your plot easy to understand.

7. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

In the example above, the number of hours spent online isn't just compared to the user's age, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you're trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see your new product's cost, risk, and value. This can help you focus your energies on a low-risk new product with a high potential return.

You can also use bubble charts for:

• Top sales by month and location.
• Customer satisfaction surveys.
• Store performance tracking.
• Marketing campaign reviews.

Design Best Practices for Bubble Charts

• Scale bubbles according to area, not diameter.
• Make sure labels are clear and visible.
• Use circular shapes only.

8. Waterfall Chart

I sometimes use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how different departments influence overall company revenue and lead to a specific profit number.

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

• Deal pipelines.
• Conversion and retention analysis.
• Bottlenecks in manufacturing and other multi-step processes.
• Marketing campaign performance.
• Website conversion tracking.

Design Best Practices for Funnel Charts

• Scale the size of each section to accurately reflect the size of the data set.
• Use contrasting colors or one color in graduated hues, from darkest to lightest, as the size of the funnel decreases.

10. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

Best Use Cases for Heat Maps

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design .

Another common use for heat map charts  is location assessment. If you're trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can't communicate.

Heat maps can also help with spotting patterns, so they're good for analyzing trends that change quickly, like ad conversions. They can also help with:

• Competitor research.
• Customer sentiment.
• Sales outreach.
• Campaign impact.
• Customer demographics.

Design Best Practices for Heat Map

• Use a basic and clear map outline to avoid distracting from the data.
• Use a single color in varying shades to show changes in data.
• Avoid using multiple patterns.

11. Gantt Chart

The Gantt chart is a horizontal chart that dates back to 1917. This chart maps the different tasks completed over a period of time.

Gantt charting is one of the most essential tools for project managers. It brings all the completed and uncompleted tasks into one place and tracks the progress of each.

While the left side of the chart displays all the tasks, the right side shows the progress and schedule for each of these tasks.

This chart type allows you to:

• Break projects into tasks.
• Track the start and end of the tasks.
• Set important events, meetings, and announcements.
• Assign tasks to the team and individuals.

I use donut charts for the same use cases as pie charts, but I tend to prefer the former because of the added benefit that the data is easier to read.

Another benefit to donut charts is that the empty center leaves room for extra layers of data, like in the examples above. 

Design Best Practices for Donut Charts 

Use varying colors to better differentiate the data being displayed, just make sure the colors are in the same palette so viewers aren't put off by clashing hues. 

14. Sankey Diagram

A Sankey Diagram visually represents the flow of data between categories, with the link width reflecting the amount of flow. It’s a powerful tool for uncovering the stories hidden in your data.

As data grows more complex, charts must evolve to handle these intricate relationships. Sankey Diagrams excel at this task.

With ChartExpo , you can create a Sankey Chart with up to eight levels, offering multiple perspectives for analyzing your data. Even the most complicated data sets become manageable and easy to interpret.

You can customize your Sankey charts and every component including nodes, links, stats, text, colors, and more. ChartExpo is an add-in in Microsoft Excel, Google Sheets, and Power BI, you can create beautiful Sankey diagrams while keeping your data safe in your favorite tools.

Sankey diagrams can be used to visualize all types of data which contain a flow of information. It beautifully connects the flows and presents the data in an optimum way.

Here are a few use cases:

• Sankey diagrams are widely used to visualize energy production, consumption, and distribution. They help in tracking how energy flows from one source (like oil or gas) to various uses (heating, electricity, transportation).
• Businesses use Sankey diagrams to trace customer interactions across different channels and touchpoints. It highlights the flow of users through a funnel or process, revealing drop-off points and success paths.
• I n supply chain management, these diagrams show how resources, products, or information flow between suppliers, manufacturers, and retailers, identifying bottlenecks and inefficiencies.

Design Best Practices for Sankey Diagrams 

When utilizing a Sankey diagram, it is essential to maintain simplicity while ensuring accuracy in proportions. Clear labeling and effective color usage are key factors to consider. Emphasizing the logical flow direction and highlighting significant flows will enhance the visualization.

How to Choose the Right Chart or Graph for Your Data

Channels like social media or blogs have multiple data sources, and managing these complex content assets can get overwhelming. What should you be tracking? What matters most?

How do you visualize and analyze the data so you can extract insights and actionable information?

1. Identify your goals for presenting the data.

Before creating any data-based graphics, I ask myself if I want to convince or clarify a point. Am I trying to visualize data that helped me solve a problem? Or am I trying to communicate a change that's happening?

A chart or graph can help compare different values, understand how different parts impact the whole, or analyze trends. Charts and graphs can also be useful for recognizing data that veers away from what you’re used to or help you see relationships between groups.

So, clarify your goals then use them to guide your chart selection.

2. Figure out what data you need to achieve your goal.

Different types of charts and graphs use different kinds of data. Graphs usually represent numerical data, while charts are visual representations of data that may or may not use numbers.

So, while all graphs are a type of chart, not all charts are graphs. If you don't already have the kind of data you need, you might need to spend some time putting your data together before building your chart.

3. Gather your data.

Most businesses collect numerical data regularly, but you may need to put in some extra time to collect the right data for your chart.

Besides quantitative data tools that measure traffic, revenue, and other user data, you might need some qualitative data.

These are some other ways you can gather data for your data visualization:

• Interviews 
• Quizzes and surveys
• Customer reviews
• Reviewing customer documents and records
• Community boards

Fill out the form to get your templates.

4. select the right type of graph or chart..

Choosing the wrong visual aid or defaulting to the most common type of data visualization could confuse your viewer or lead to mistaken data interpretation.

But a chart is only useful to you and your business if it communicates your point clearly and effectively.

Ask yourself the questions below to help find the right chart or graph type.

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. do you want to compare values.

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

• Scatter plot

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

• Stacked bar

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want more information about how a data set performed during a specific time, there are specific chart types that do extremely well.

You should choose one of the following:

• Dual-axis line

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

Featured Resource: The Marketer's Guide to Data Visualization

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What is: Data Representation

Understanding data representation.

Data representation refers to the methods and techniques used to visually or symbolically depict data. This can include various formats such as graphs, charts, tables, and diagrams. Effective data representation is crucial for data analysis and data science, as it allows for easier interpretation and communication of complex information. By transforming raw data into a more understandable format, stakeholders can make informed decisions based on insights derived from the data.

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Types of Data Representation

There are several types of data representation, each suited for different types of data and analysis. Common forms include numerical representation, categorical representation, and temporal representation. Numerical representation involves using numbers to convey information, while categorical representation uses categories or groups. Temporal representation focuses on data over time, often visualized through time series graphs. Understanding these types is essential for selecting the appropriate method for data visualization.

The Importance of Visual Representation

Visual representation of data plays a vital role in data analysis. It enhances the ability to identify trends, patterns, and outliers within datasets. By utilizing visual tools like bar charts, pie charts, and scatter plots, analysts can quickly convey complex information in a digestible format. This not only aids in analysis but also facilitates communication with non-technical stakeholders, ensuring that insights are accessible to a broader audience.

Common Tools for Data Representation

Several tools and software applications are widely used for data representation in the fields of statistics and data science. Popular tools include Tableau, Microsoft Power BI, and Google Data Studio. These platforms provide users with the ability to create interactive and dynamic visualizations, allowing for real-time data analysis and exploration. Additionally, programming languages like Python and R offer libraries such as Matplotlib and ggplot2, which enable custom visualizations tailored to specific analytical needs.

Best Practices in Data Representation

When creating data representations, adhering to best practices is essential for clarity and effectiveness. This includes choosing the right type of visualization for the data at hand, ensuring that visualizations are not cluttered, and using appropriate scales and labels. Additionally, color choices should enhance readability and accessibility, avoiding combinations that may confuse or mislead viewers. Following these guidelines helps ensure that the data representation communicates the intended message accurately.

Challenges in Data Representation

Despite its importance, data representation comes with challenges. One significant challenge is the risk of misrepresentation, where visualizations may distort the data or lead to incorrect conclusions. This can occur due to inappropriate scaling, selective data presentation, or biased visual choices. Analysts must be vigilant in ensuring that their representations are truthful and accurately reflect the underlying data, as misleading visuals can have serious implications for decision-making.

Data Representation in Machine Learning

In the realm of machine learning, data representation is critical for model performance. The way data is represented can significantly impact the effectiveness of algorithms. Feature engineering, which involves selecting and transforming variables into a suitable format for modeling, is a key aspect of this process. Proper data representation can enhance the model’s ability to learn from the data, leading to better predictions and insights.

Interactive Data Representation

Interactive data representation has gained popularity in recent years, allowing users to engage with data in real-time. Tools that support interactive visualizations enable users to filter, zoom, and manipulate data, providing a more immersive experience. This interactivity fosters deeper exploration and understanding of the data, making it easier for users to uncover insights that may not be immediately apparent in static representations.

Future Trends in Data Representation

As technology continues to evolve, so too does the field of data representation. Emerging trends include the use of augmented reality (AR) and virtual reality (VR) for data visualization, offering new dimensions for understanding complex datasets. Additionally, advancements in artificial intelligence are enabling automated data representation, where algorithms can generate visualizations based on data patterns without human intervention. These innovations promise to enhance the way data is represented and understood in the future.

The Top 10 Types of Data Visualization Made Simple

Research shows that we create 2.5 quintillion bytes of data every single day. What types of data visualization do you use to properly digest all of that data?

While this is a staggering figure, it’s only going up as the Internet of Things (IoT) evolves. In fact, 90% of the world’s data was generated in the past two years alone!

With so much information accessible at our fingertips, it’s important to understand how to organize it into analyzable, actionable insights. Yet, if you manage multiple content assets with multiple data sources, it can be difficult to determine how to shape your analytics strategy.

This is where it helps to know the best data visualization types to use.

Data visualization is the process of turning your data into graphical representations that communicate logical relationships and lead to more informed decision-making.

Today, we’re sharing a list of the various types of data organization and how you can implement this approach in your own organization.

Ready to learn more? Let’s get started.

What is Data Visualization?

In short, data visualization is the representation of data in a graphical or pictorial format.

It allows key decision-makers to see complex analytics in a visual layout, so they can identify new patterns or grasp challenging concepts.

From website metrics and sales team performance to marketing campaign results and product adoption rates, there is a range of data points your organization needs to track.

When you have your hands full juggling multiple projects at once, you need a quick and effective reporting method that allows you to get a clear point across. Do you know which types of data visualization method to use?

The 15 Most Common Types of Data Visualization Formats

Some of the most common types of data visualization chart and graph formats include:

• Column Chart
• Stacked Bar Graph
• Stacked Column Chart
• Dual Axis Chart
• Mekko Chart
• Waterfall Chart
• Bubble Chart
• Scatter Plot Chart
• Bullet Graph
• Funnel Chart

While all of them serve to expedite and improve data interpretation, not all are appropriate for the same job. Choosing the right visual aid is the key to preventing user confusion and making sure your analysis is accurate. Let’s dive into 10 of these 15 types of charts and graphs below.

10 Types of Data Visualization Explained

There are myriad different types of charts, graphs and other visualization techniques that can help analysts represent and relay important data. Let’s take a look at 10 of the most common ones:

1. Column Chart

This is one of the most common types of data visualization tools. There’s a reason we learn how to make column charts in elementary school. They’re a simple, time-honored way to show a comparison among different sets of data. You can also use a column chart to track data sets over time.

A column chart will include data labels along the horizontal (X) axis with measured metrics or values presented on the vertical (Y) axis, also known as the left side of the chart. The Y-axis will normally start at 0 and go as high as the largest measurement you’re tracking.

You can use column charts to track monthly sales figures, revenue per landing page, or similar measurements. Consistent colors help keep the focus on the data itself, though you can introduce accent colors to emphasize important data points or to track changes over time.

2. Bar Graph

You can often use a bar graph and column chart in the same way, though column charts limit your label and comparison space. It’s best to stick with a bar graph if you’re:

• Working with lengthier labels
• Displaying negative numbers
• Comparing 10 or more items

In this case, your data labels will go along the Y-axis while the measurements are along the X-axis.

3. Stacked Bar Graph

Are you comparing many different items? Do you want to track the individual growth of each data set itself, along with the group’s growth as a collective whole? To reveal this part-to-whole relationship, you’ll create a stacked bar graph.

If you removed the color from this chart, it would look similar to a standard bar chart. The “stacked” layout represents this chart’s contrasting color scheme. These colors map back to a legend that accompanies your map.

For example, you might want to track the performance of four different types of products across five different sales strategies. Strategy 1 through Strategy 5 will be at your X-axis, while sales numbers will be on the Y-axis.

Within each strategy category, however, you’ll have four different color blocks. Each represents one of the product types. This way, you can determine which strategy worked best for each product type as a whole, as well as which products did well within each strategy.

4. Line Graph

This is another one of those standard chart types that’s instantly recognizable. A line graph is designed to reveal trends, progress, or changes that occur over time. As such, it works best when your data set is continuous rather than full of starts and stops.

Like a column chart, data labels on a line graph are on the X-axis while measurements are on the Y-axis.

Make sure to use solid lines and avoid plotting more than four lines, as anything above this can be distracting. You should plan enough space that your lines are around 2/3 the height of the Y-axis.

5. Dual-Axis Chart

While most visualization charts use a single Y-axis and X-axis, a dual-axis chart incorporates a shared X-axis and two separate Y-axes. Most combine the features of a column chart and a line chart, though you can vary the graphing styles according to the data you’re using.

This layout allows you to show a relationship (or lack thereof) between different variables, and it works best when you’re working with three data sets as follows:

• One set of continuous data
• Two data sets grouped by category

As our brains are more inclined to read from left to right, it helps to make the left-side Y-axis the primary variable. It’s also important to use contrasting colors for the two charts to provide visual distinction.

6. Mekko Chart

This is one chart you might be less familiar with unless you’re in the data analyzation space . Standing for Marimekko chart, a Mekko chart has a similar layout to a stacked bar graph, with one major exception: Instead of tracking time progression, the X-axis measures another dimension of your data sets.

With this layout, you can compare values, measure the composition of each value, and analyze data distribution all at the same time.

7. Pie Chart

A pie chart represents one static number, divided into categories that constitute its individual portions. When you use one, you’ll represent numerical amounts in percentages. When you sum up all of the separate portions, they should add up to 100%.

These are especially helpful in digital marketing, as you can use them to show a breakdown of:

• Market shares
• Marketing expenditures
• Customer demographics
• Customer device usage (for UX testing)
• Online traffic sources

You want your pie chart to have plenty of differentiation between slices. As such, it’s best to limit the number of categories you illustrate.

8. Scatter Plot

This type of visualization is also called a scattergram, and it represents different variables plotted along two axes. Note that both the X-axis and the Y-axis are value axes as a scatter plot does not use a category axis.

These types of data visualization work best when you’re analyzing multiple data points and you’re looking for any similarities within the data set. As you do so, you can notice any outliers and also gain a clearer understanding of your overall data distribution.

Say, for instance, that you wanted to measure customer feedback scores that your organization receives. You also wanted to see if your service desk response times have any impact on those scores.

Feedback scores range from 0 to 10, so those would be your Y-axis measurements.

On your X-axis, you’d label from 0 until the longest response time allowed, such as one hour. Then, you’d plot the scores you’d received, noticing patterns and trends that can help inform your service efforts.

9. Bubble Chart

Like a scatter chart, a bubble chart can also show relationships or distribution.

In this variation, however, you’ll replace the data points with bubbles. You’ll also vary the sizes of the bubble to represent a third data set.

As with a scatter chart, a bubble chart does not use a category axis. Rather, you’ll plot the data sets as X-values, Y-values and now, Z-values (bubble size).

10. Bullet Graph

Is your team working toward a goal ? A bullet graph can help you visually track your progress. Similar in layout to a bar graph, these also incorporate other visual elements.

When using a bullet graph, you’ll begin with a one, main measure, and then compare that measure to another (or multiple) measure to find a deeper meaning and connection.

Five Essential Reasons to Implement Data Visualization Tools

Now that we’ve explored the different types of data visualization graphs, charts, and maps, let’s briefly discuss a few of the reasons why you might require data visualization in the first place.

If you’re on the fence about which type of visual will work best for your company, it helps to understand the top business functions that data visualization can serve. Here are the main five to consider.

1. Comparing Values

As data analysts, you see your fair share of data sets. When you want to compare the differences and similarities between these sets, charts are ideal. They easily reveal the high and low values of a particular set so you can note major differences, gaps, and other trends.

If you need to create a comparison chart, the following types of visualizations are appropriate:

• Scatter Plot

Any of these visualization techniques allow you to scan through huge amounts of data and still derive relevant and informative patterns from it.

2. Show Composition

You might also need to break your value sets apart, showing how individual units affect the greater picture. For instance, you may want to track overall mobile access on your website by device type or geographical location. Or, you might want to know which elements of your recent digital marketing campaign proved the most successful.

In this case, you can use any one of these types of data visualizations:

• Stacked Bar Graph

All of these representations allow users to measure individual performance levels to determine their effect on the overall data set.

3. Determine Distribution

Are you trying to understand the overarching distribution of your data? If so, a distribution chart will show all of the possible intervals or values of the value set as well as how often they occur.

From this visualization, you can identify the normal trends as well as any outliers that could disrupt them. You can also get a clear picture of how wide the range is between your information values.

You can reach for the following types of data visualizations when you need to determine distribution:

4. Researching Trends

Did you wrap up a recent television advertising campaign? What about a new product launch?

Once the dust settles and it’s time to get back to work, it’s your job to see if those efforts succeeded. When you want to determine how a particular data set performed during a set time frame, these types of visualizations work well:

• Dual-Axis Line Graph

5. Understanding Relationships in Different Types of Data Visualization

Sometimes, the best way to understand a given variable is to see how it relates to one or multiple other variables. For instance, one variable could have a positive or negative effect on another.

You can use these types of charts to visually depict the relationship between things:

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Are you ready to make sense of all of the data that your organization receives? If so, you can’t get there by relying on antiquated analytics or clunky spreadsheets. It’s time to look into various types of data visualization.

Instead, it’s time to partner with the best data visualization services around. We’re a full-stack data visualization and software products firm, ready to help you communicate complex data with ease to every member of your organization.

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Table of Contents

What is data visualization , how to select the appropriate graph or chart for your data, most common types of data visualization, how to choose the right type of chart: questions to ask, best data visualization tools, types of data visualization you should know.

Visualization has become an integral aspect of data analysis in today's data-driven environment. Businesses may obtain insights and make data-driven choices by using a variety of successful data visualization approaches. This article will examine the many types of data visualization.

Before learning about types of data visualization, let’s learn about what is data visualization first.

The art of presenting your data and information as graphs, charts, or maps is known as data visualization . Data visualization's purpose is to emphasize observations that would not otherwise jump out when looking at a linear list of values and numbers to enable people to quickly and easily grasp their data.

To successfully express your message and insights, selecting the appropriate chart or graph for your data is essential. The following factors need to be considered while choosing the optimal data visualization:

What are you trying to visualize? Are you attempting to demonstrate contrasts, patterns, or connections in your data?

Type of Data

What kind of data do you have? Is it a numerical or category list? Both continuous and discrete? This will aid in choosing the best types of data visualization charts.

What context does your data come from? Is it recent or historical? Local or worldwide? This will enable you to choose the proper scale and coverage for your visualization.

There are many types of data visualization. The most common types are:

1. Column Chart 

They are a straightforward, time-tested method of comparing several collections of data . A column chart may be used to track data sets across time.

2. Line Graph

A line graph is used to show trends, development, or changes through time. As a result, it functions best when your data collection is continuous as opposed to having many beginnings and ends.

3. Pie Chart

In a pie chart, a single, constant number is represented by the several categories that make up its parts. You will portray numerical quantities in percentages when you employ one. All of the various components should sum up to a hundred percent when totaled.

4. Bar Chart

To compare data along two axes, use bar charts. A visual representation of the categories or subjects being measured is shown on one of the axes, which is numerical.

5. Heat Maps

A data visualization method that uses colors to denote values; great for seeing trends in huge datasets.

6. Scatter Plot

The correlation between variables is examined using a scatter plot. At the point where the data's two values overlap, the data are represented on the graph as dots.

7. Bubble Chart

A variant of the scatter plot where the size and color of the bubbles, which represent the data points, provide extra information, are used to depict the data points as dots.

8. Funnel Chart

To illustrate a sequential process from top to bottom, a funnel chart's principal purpose is to represent it graphically. As the process flows down, the amount generally decreases, making the data set at the top of the process greater than the bottom.

9. Radar Chart

Radar charts are a sort of data visualization that aids in the analysis of objects or categories in light of a variety of attributes. The radar chart consists of a circle with concentric rings, and the data are shown as dots on the chart. The shape is then formed by connecting the dots. Each thing or group has a shape.

10. Tree Chart

An alternative to a table for precise numerical data is a tree chart , often known as a tree diagram. The basic goal of a tree chart is to represent data as pieces of a larger whole within a category.

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11. Flow Chart

One extremely adaptable method of data display is the flowchart. Use mind maps for brainstorming, flowcharts to depict a process graphically and hierarchical data of objects or people.

A gauge is a percentage visualization. There are a few uses for the half-doughnut-like form. To display a percentage figure with an arrow pointing to it is the simplest use. If you have a small quantity of data to work with, this is a fantastic option.

13. Gantt Chart

Horizontal bar graphs are the basis for the Gantt chart ; however, they differ significantly from them. A rectangle that extends from left to right stands for each item on the chart. Depending on how long each activity takes to accomplish, each one varies in size.

14. Venn Diagram

A Venn diagram is a data visualization that compares two or more objects by emphasizing their similarities. The most typical Venn diagram design consists of two overlapping circles.

15. Histogram

While a histogram and a bar graph are similar, they use distinct charting systems. The ideal sort of data visualization for frequency-based analysis of data ranges is a histogram.

16. Waterfall Chart

A style of bar graph that demonstrates how a sequence of positive and negative numbers affects an initial value.

17. Marimekko Chart

A graphic depiction called a Marimekko chart shows category data using stacked bar graphs of various widths. Mekko charts and mosaic plots are other names for the same type of diagram.

18. Choropleth Map

The technique of color mapping symbology is used to create choropleth maps, which are themed maps used to display statistical data. It shows geographically segmented sections or regions that are colored, shaded, or patterned according to a data variable, known as enumeration units.

19. PERT Chart

PERT is a technique for calculating the least amount of time needed to finish a project by analyzing the amount of time needed to complete each job and the dependencies related to it.

20. Dichotomous Key

An identification chart called a dichotomous key allows users to choose between questions and assertions offered in the chart to arrive at a conclusion that will assist them in identifying objects or anything else.

21. Mind Map

Using a radial layout to represent thoughts and ideas, mind maps are data visualization that helps organize and spark ideas while dealing with complicated material.

22. Timeline

They are visual depictions of a historical period with significant events labeled in chronological order. They may be more detailed visuals or rather straightforward linear representations.

23. Concentric Circles

A style of data visualization known as concentric circles makes use of circles inside circles to represent hierarchical connections or proportions, with the size of the circles signifying the amount of data being displayed.

24. Radial Wheel

With each spoke or segment denoting a separate category or value, radial wheels are a style of data visualization that uses a circular structure to highlight connections between data elements.

25. Percentage Bar

A type of data visualization known as percentage bars use a horizontal bar with proportional segments to show numbers as percentages of the total and the relative size of each category.

26. Donut Chart

Donut charts, often called doughnut charts, are variants on pie charts that include a hole in the center, giving them the appearance of doughnuts. This open space may be used to display further information.

27. Half-Donut Chart

The half-doughnut chart is precisely what its name suggests—it's a half-doughnut chart. When displaying modest amounts of data, this type of data visualization is a useful option. A half-donut chart should, ideally, not include more than three wedges.

28. Polar Graph

If the data values are substantially dissimilar from one another, choose a polar graph as the types of data visualization in data science. If not, it could be difficult to read at a glance.

29. Icon Array

Icon arrays are a type of data visualization that works well for displaying proportions and patterns because they employ icons or symbols to represent individual data points, such as circles or squares.

30. Cone Chart

Hierarchy is depicted with a cone chart. The greatest value data is located on the broadest section of the cone. The other values are distributed in descending order from top to bottom of the cone.

To ensure that you select the optimal visualization approach for your data, it is crucial to ask the right questions when choosing the style of chart or graph to employ. Consider the following questions:

• What sort of narrative am I attempting?
• How much data do I have?
• What audience am I presenting for, and how much complexity and depth do they need to comprehend my data?
• What kind of data do I have?
• How can I create a compelling and clear visualization?

Among the top types of data visualization tools are:

• Google Charts
• Datawrapper

In conclusion, choosing the appropriate form of data visualization can be crucial for effectively expressing the patterns and insights buried in complex data. Businesses and individuals may harness the potential of data visualization to improve decision-making and outcomes by knowing the advantages and disadvantages of various data visualization techniques. If you wish to master these techniques, you must enroll in our Post Graduate Program In Business Analysis today!

1. What main kinds of data visualization are there?

Bar charts, line charts, scatter plots, pie charts, and heat maps are a few of the prevalent types of data visualization.

2. What are the data visualization field's key objectives?

Data visualization's primary objectives are to convey insights, trends, patterns, and correlations in data in a simple and obvious manner.

3. What advantages can data visualization offer?

Data visualization can potentially improve the communication of detailed information and speed up data processing.

4. Why is data visualization effective?

Data analysis and comprehension are aided by data visualization because it uses the visual system to spot patterns and correlations swiftly.

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Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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Charts and Graphs for Data Visualization

As companies and groups deal with more and more data, it’s crucial to present it visually. Data is everywhere these days, and it can be overwhelming.

This article is your guide to Data Visualization , which is turning all that data into pictures and charts that are easy to understand. Whether you work in business, marketing, or anything else, these charts can help you explain ideas, track how things are going, and make smart choices.

What is Data Visualization?

Data visualization is taking a bunch of numbers and information and turning it into pictures or any kind of charts that are easier to understand. It takes a big pile of information and sorts it into pictures (like bar charts, line graphs, or pie charts) that make it easier to understand or see patterns and trends. Here are some of the things data visualization can help you see:

• How things are changing over time
• How things compare to each other
• Relationships between things

Different Types of Graphs for Data Visualization

Data can be a jumble of numbers and facts. Charts and graphs turn that jumble into pictures that make sense. 10 prime super useful chart types are:

Bar graphs are one of the most commonly used types of graphs for data visualization. They represent data using rectangular bars where the length of each bar corresponds to the value it represents. Bar graphs are effective for comparing data across different categories or groups.

Bar Graph Example

Advantages of Bar Graphs

• Highlighting Trends : Bar graphs are effective at highlighting trends and patterns in data, making it easy for viewers to identify relationships and comparisons between different categories or groups.
• Customizations : Bar graphs can be easily customized to suit specific visualization needs, such as adjusting colors, labels, and styles to enhance clarity and aesthetics.
• Space Efficiency : Bar graphs can efficiently represent large datasets in a compact space, allowing for the visualization of multiple variables or categories without overwhelming the viewer.

Disadvantages of Bar Graphs

• Limited Details : Bar graphs may not provide detailed information about individual data points within each category, limiting the depth of analysis compared to other visualization methods.
• Misleading Scaling : If the scale of the y-axis is manipulated or misrepresented, bar graphs can potentially distort the perception of data and lead to misinterpretation.
• Overcrowding : When too many categories or variables are included in a single bar graph, it can become overcrowded and difficult to read, reducing its effectiveness in conveying clear insights.

Line Graphs

Line graphs are used to display data over time or continuous intervals. They consist of points connected by lines, with each point representing a specific value at a particular time or interval. Line graphs are useful for showing trends and patterns in data. Perfect for showing trends over time, like tracking website traffic or how something changes.

Line Graph Example

Advantages of Line Graphs

• Clarity : Line graphs provide a clear representation of trends and patterns over time or across continuous intervals.
• Visual Appeal : The simplicity and elegance of line graphs make them visually appealing and easy to interpret.
• Comparison : Line graphs allow for easy comparison of multiple data series on the same graph, enabling quick insights into relationships and trends.

Disadvantages of Line Graphs

• Data Simplification: Line graphs may oversimplify complex data sets, potentially obscuring nuances or outliers.
• Limited Representation : Line graphs are most effective for representing continuous data over time or intervals and may not be suitable for all types of data, such as categorical or discrete data.

Different Types of Charts for Data Visualization

Pie charts are circular graphs divided into sectors, where each sector represents a proportion of the whole. The size of each sector corresponds to the percentage or proportion of the total data it represents. Pie charts are effective for showing the composition of a whole and comparing different categories as parts of a whole.

Pie Chart Example

Advantages of Pie Charts

• Easy to create: Pie charts can be quickly generated using various software tools or even by hand, making them accessible for visualizing data without specialized knowledge or skills.
• Visually appealing: The circular shape and vibrant colors of pie charts make them visually appealing, attracting the viewer’s attention and making the data more engaging.
• Simple and easy to understand: Pie charts present data in a straightforward manner, making it easy for viewers to grasp the relative proportions of different categories at a glance.

Disadvantages of Using a Pie Chart

• Limited trend analysis: Pie charts are not ideal for showing trends or changes over time since they represent static snapshots of data at a single point in time.
• Limited data slice: Pie charts become less effective when too many categories are included, as smaller slices can be difficult to distinguish and interpret accurately. They are best suited for representing a few categories with distinct differences in proportions.

Scatter Plots

Scatter plots are used to visualize the relationship between two variables. Each data point in a scatter plot represents a value for both variables, and the position of the point on the graph indicates the values of the variables. Scatter plots are useful for identifying patterns and relationships between variables, such as correlation or trends.

Scatter Chart Example

Advantages of Using Scatter Plots

• Revealing Trends and Relationships: Scatter plots are excellent for visually identifying patterns, trends, and relationships between two variables. They allow for the exploration of correlations and dependencies within the data.
• Easy to Understand: Scatter plots provide a straightforward visual representation of data points, making them easy for viewers to interpret and understand without requiring complex statistical knowledge.
• Highlight Outliers: Scatter plots make it easy to identify outliers or anomalous data points that deviate significantly from the overall pattern. This can be crucial for detecting unusual behavior or data errors within the dataset.

Disadvantages of Using Scatter Plot Charts

• Limited to Two Variables: Scatter plots are limited to visualizing relationships between two variables. While this simplicity can be advantageous for focused analysis, it also means they cannot represent interactions between more than two variables simultaneously.
• Not Ideal for Precise Comparisons: While scatter plots are excellent for identifying trends and relationships, they may not be ideal for making precise comparisons between data points. Other types of graphs, such as bar charts or box plots, may be better suited for comparing specific values or distributions within the data.

Area Charts

Area charts are similar to line graphs but with the area below the line filled in with color. They are used to represent cumulative totals or stacked data over time. Area charts are effective for showing changes in composition over time and comparing the contributions of different categories to the total.

Area Chart Example

Advantages of Using Area Charts

• Visually Appealing: Area charts are aesthetically pleasing and can effectively capture the audience’s attention due to their colorful and filled-in nature.
• Great for Trends: They are excellent for visualizing trends over time, as the filled area under the line emphasizes the magnitude of change, making it easy to identify patterns and fluctuations.
• Compares Well: Area charts allow for easy comparison between different categories or datasets, especially when multiple areas are displayed on the same chart. This comparative aspect aids in highlighting relative changes and proportions.

Disadvantages of Using Area Charts

• Limited Data Sets: Area charts may not be suitable for displaying large or complex datasets, as the filled areas can overlap and obscure details, making it challenging to interpret the data accurately.
• Not for Precise Values: Area charts are less effective for conveying precise numerical values, as the emphasis is on trends and proportions rather than exact measurements. This can be a limitation when precise data accuracy is crucial for analysis or decision-making.

Radar Charts

A radar chart , also known as a spider chart or a web chart, is a graphical method of displaying multivariate data in the form of a two-dimensional chart. It is particularly useful for visualizing the relative values of multiple quantitative variables across several categories. Radar charts compare things across many aspects, like how different employees perform in various skills.

Radar Chart Example

Advantages of Using Radar Chart

• Highlighting Strengths and Weaknesses: Radar charts allow for the clear visualization of strengths and weaknesses across multiple variables, making it easy to identify areas of excellence and areas for improvement.
• Easy Comparisons: The radial nature of radar charts facilitates easy comparison of different variables or categories, as each axis represents a different dimension of the data, enabling quick visual assessment.
• Handling Many Variables: Radar charts are particularly useful for handling datasets with many variables, as each variable can be represented by a separate axis, allowing for comprehensive visualization of multidimensional data.

Disadvantages of Using Radar Chart

• Scaling Issues: Radar charts can present scaling issues, especially when variables have different units or scales. Inaccurate scaling can distort the representation of data, leading to misinterpretation or misunderstanding.
• Misleading Comparisons: Due to the circular nature of radar charts, the area enclosed by each shape can be misleading when comparing variables. Small differences in values can result in disproportionately large visual differences, potentially leading to misinterpretation of data.

Histograms are similar to bar graphs but are used specifically to represent the distribution of continuous data. In histograms, the data is divided into intervals, or bins, and the height of each bar represents the frequency or count of data points within that interval.

Example of Histogram

Advantages of using Histogram

• Easy to understand: Histograms provide a visual representation of the distribution of data, making it easy for viewers to grasp the overall pattern.
• Identify Patterns: Histograms allow for the identification of patterns and trends within the data, such as skewness, peaks, or gaps.
• Compare Data Sets: Histograms enable comparisons between different datasets, helping to identify similarities or differences in their distributions.

Disadvantages of using Histogram

• Not for small datasets: Histograms may not be suitable for very small datasets as they require a sufficient amount of data to accurately represent the distribution.
• Limited details: Histograms provide a summary of the data distribution but may lack detailed information about individual data points, such as specific values or outliers.

Treemap Charts

Treemap charts are a type of data visualization that represent hierarchical data as a set of nested rectangles. Each rectangle, or “tile,” in the treemap represents a category or subcategory of the data, and the size of the rectangle corresponds to a quantitative value, such as the proportion or absolute value of that category within the dataset.

Treemap Charts >>

Advantages of using a Treemap Chart

• Identifying patterns and trends: Treemap charts help in visually identifying patterns and trends within hierarchical data structures by representing data in nested rectangles, making it easier to see how smaller components contribute to the whole.
• Highlighting Proportions: Treemaps effectively highlight proportions by using varying sizes and colors of rectangles to represent different values or categories, making it easy to understand the relative significance of each component.
• Efficient use of space: Treemap charts efficiently utilize space by packing rectangles within larger rectangles, allowing for the visualization of large datasets in a compact and organized manner.

Disadvantages of using a Treemap Chart

• Difficulty comparing exact values: Due to the varying sizes and shapes of the rectangles in a treemap, it can be challenging to accurately compare exact values between different categories or components, especially when the differences are subtle.
• Order dependence: The arrangement of rectangles within a treemap can significantly impact perception. Small changes in sorting or hierarchical structure can lead to different visual interpretations, making it important to carefully consider the ordering of data elements.

Pareto Charts

A Pareto chart is a specific type of chart that combines both bar and line graphs. It’s named after Vilfredo Pareto, an Italian economist who first noted the 80/20 principle, which states that roughly 80% of effects come from 20% of causes. Pareto charts are used to highlight the most significant factors among a set of many factors.

Pareto Chart Example

Advantages of using a Pareto Chart

• Simple to understand: Pareto charts present data in a straightforward manner, making it easy for viewers to grasp the most significant factors at a glance.
• Visually identify key factors: By arranging data in descending order of importance, Pareto charts allow users to quickly identify the most critical factors contributing to a problem or outcome.
• Focus resources effectively: With the ability to prioritize factors based on their impact, Pareto charts help organizations allocate resources efficiently by addressing the most significant issues first.

Disadvantages of Using a Pareto Chart

• Limited Data Exploration: Pareto charts primarily focus on identifying the most critical factors, which may lead to overlooking nuances or subtle trends present in the data.
• Assumes 80/20 rule applies: The Pareto principle, which suggests that roughly 80% of effects come from 20% of causes, is a foundational concept behind Pareto charts. However, this assumption may not always hold true in every situation, potentially leading to misinterpretation or oversimplification of complex data relationships.

Waterfall Charts

Waterfall charts are a type of data visualization tool that display the cumulative effect of sequentially introduced positive or negative values. They are particularly useful for understanding the cumulative impact of different factors contributing to a total or final value.

Waterfall Charts Example

Advantages of Using a Waterfall Chart

• Clear Breakdown of Changes: Waterfall charts provide a clear and visual breakdown of changes in data over a series of categories or stages, making it easy to understand the cumulative effect of each change.
• Easy to Identify the Impact: By displaying the incremental additions or subtractions of values, waterfall charts make it easy to identify the impact of each component on the overall total.
• Focus on the Journey: Waterfall charts emphasize the journey of data transformation, showing how values evolve from one stage to another, which can help in understanding the flow of data changes.

Disadvantages of Using a Waterfall Chart

• Complexity with Too Many Categories: Waterfall charts can become complex and cluttered when there are too many categories or stages involved, potentially leading to confusion and difficulty in interpreting the data.
• Not Ideal for Comparisons: While waterfall charts are effective for illustrating changes over a sequence of categories, they may not be suitable for direct comparisons between different datasets or groups, as they primarily focus on showing the cumulative effect of changes rather than individual values.

How to Choose Right Charts or Graphs for Data Visualization?

Choosing the right chart for your data visualization depends on what you want to communicate with your data. Here are some questions provided below to ask yourself before doing Data Visualization,

• How much Data do you have?
• What type of Data are you working with?
• What is the goal of your Visualization?

Also, you can check the general guidelines below to help you pick the right chart type for your reference,

• Distribution of Data
• Relationship between variables
• Comparisons between groups
• Trends over time
• Audience familiarity with different types of Charts and Graphs

The right choice of chart or graph depends on your specific data and the information you want to convey to others. Whether you’re motivating your team, impressing stakeholders, or showcasing your business values, thoughtful data visualization builds trust and drives informed decision-making.

Remember, the key to impactful data visualization lies in choosing the right tool to transform complex data into clear, understanding actionable insights for your audience.

FAQs- Best Types of Charts and Graphs For Data Visualization

Which type of graph is best for data visualization.

The best type of graph depends on the nature of the data. Line graphs are ideal for showing trends over time, bar graphs for comparisons, scatter plots for correlations, and pie charts for proportions.

What type of chart would be best for this visualization?

If you’re comparing categories or groups, a bar chart is often best. It offers a clear visual representation of comparisons between discrete data points.

What are the 4 types of graphs and charts?

Bar Graph Line Graph Pie Chart, and Area Chart

What are the 4 main visualization types?

Spatial, Temporal, Hierarchical, and Network are the 4 main types of visualizations.

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Are you overwhelmed by the volume of data your business generates? You're not alone. Every day, businesses worldwide produce around 402.74 million terabytes of data, and the number is only increasing. For project managers, business owners, and leaders, this flood of information can be both a blessing and a challenge. The key lies in not just collecting data but making sense of it—extracting actionable insights that drive informed decisions.

But how can you effectively navigate this ocean of data? The answer is data visualization—a powerful tool that turns raw data into clear, actionable insights. This article explores why data visualization is essential for modern businesses, especially in project management, and how integrating predictive intelligence elevates decision-making. By the end, you'll see why data visualization isn’t just a tool; it’s a strategic imperative.

The Data Dilemma - Why Data Visualization Matters

Imagine sifting through endless rows of numbers in a spreadsheet, trying to spot a trend. Now, picture that same data as a dynamic chart where patterns and outliers stand out instantly. This is the power of data visualization. It transforms complex, abstract data into visual stories that are easy to understand, enabling quicker, more accurate decisions

The Power of Visual Storytelling in Business

In a fast-paced business environment, the ability to interpret data swiftly and communicate insights is crucial. This is especially true in project management, where delays in decision-making can lead to missed deadlines, cost overruns, and project failure. Data visualization aids in identifying risks and opportunities enhance communication across teams and ensures everyone is aligned.

Beyond Numbers - The Strategic Role of Data Visualization

Data visualization is more than a tool for interpreting data; it’s a strategic asset. In project management, it serves as a lens through which you can view the health of your projects in real-time. It helps monitor progress, identify bottlenecks, and anticipate challenges before they escalate.

Without data visualization, project managers might miss subtle trends, indicating a project is veering off course. With it, those trends become immediately apparent, allowing quick, informed decisions that keep projects on track. This ability to visualize project performance and health is not just beneficial—it’s imperative.

Selecting the Right Data Visualization Tool - A Strategic Guide

Choosing the right data visualization tool is critical for maximizing the benefits discussed above. The right tool will not only meet your current needs but also scale with your business as it grows. Here are key factors to consider:

• Define Your Requirements - Before selecting a tool, clearly define what you need it to do. Consider the types of data you need to visualize, the complexity of your analysis, and the audience for your visualizations. This will help you narrow down your options to tools that align with your specific goals.
• Evaluate Ease of Use - The best tools are those that are easy to use, even for those with limited technical expertise. Look for tools with drag-and-drop functionality, pre-built templates, and intuitive interfaces. A user-friendly tool will streamline the visualization process, allowing users to focus on analysis rather than struggling with the software.
• Consider Scalability - As your business grows, so will your data. Choose a tool that can handle increasing data volumes and complexity without compromising performance. Scalability ensures that your data visualization solution remains effective as your business evolves.
• Assess Interactivity Features - Interactivity is key to engaging users and allowing deeper exploration of the data. Look for tools that offer features such as filtering, drill-down capabilities, and dynamic data updates. Interactive visualizations enable users to explore the data in real-time, uncovering insights that static reports might miss.
• Evaluate Integration Capabilities - Your data visualization tool should integrate seamlessly with your existing data sources and analytics platforms. This ensures that you can easily access and visualize data from multiple sources without the need for manual data preparation or duplication. Look for tools with robust integration capabilities, including APIs and connectors.
• Assess Customization Options - Every business has unique needs when it comes to data visualization. Choose a tool that offers extensive customization options, allowing you to tailor visualizations to your specific requirements. Customization capabilities help you align visualizations with your brand identity and analytical objectives.
• Consider Collaboration and Sharing Features - Collaboration is essential for data-driven decision-making. Choose a tool that offers features such as shared dashboards, commenting, and real-time collaboration. These features make it easier for teams to work together and for stakeholders to stay informed.
• Evaluate Support and Training Resources - Finally, consider the level of support and training offered by the tool’s vendor. Look for vendors that provide comprehensive documentation, tutorials, and training materials. Additionally, ensure that the vendor offers robust customer support options, such as email, phone, or live chat, to assist with any issues that may arise.

Selecting the right data visualization tool is an investment in your organization's future success. By carefully considering these factors, you can ensure that your chosen tool will not only meet your current needs but also adapt and grow alongside your business.

Benefits of Combining Data Visualization with Predictive Analytics

In today’s competitive business landscape, data-driven decision-making is crucial. The combination of predictive analytics and data visualization is revolutionizing project management by enabling more accurate forecasting, proactive risk management, and efficient resource allocation. Here’s how these tools are transforming the way projects are managed:

Realistic Timelines - Predictive models provide accurate project timelines, visualized through Gantt charts, allowing project managers to plan with precision and avoid delays.

Resource Planning - Forecast resource needs effectively to allocate them where they’re needed most, ensuring optimal utilization and preventing bottlenecks.

• Risk Management

Early Risk Detection - Use predictive tools to identify potential risks early, visualized through heat maps, which helps in taking preemptive actions to mitigate these risks.

Scenario Planning - Visualize different outcomes to choose the best course of action under uncertainty, making informed decisions that steer projects toward success.

• Real-Time Monitoring

Continuous Tracking - Monitor project performance continuously with predictive dashboards that highlight key metrics, enabling project managers to stay on top of developments and respond swiftly to changes.

KPI Tracking - Keep an eye on KPIs and anticipate future performance to make timely adjustments, ensuring that projects remain aligned with goals.

Cost Forecasting - Predict budget needs and visualize spending with cost-performance charts, helping to maintain financial discipline and avoid budget overruns.

Financial Tracking - Enhance Earned Value Management (EVM) with clear, visual financial progress indicators, allowing for better financial control and informed decision-making.

• Stakeholder Communication

Visual Reporting - Share data-driven, visual reports with stakeholders for clear and effective communication, fostering transparency and trust in project outcomes.

Expectation Setting - Use visualizations to set and communicate realistic expectations, ensuring that all stakeholders are aligned and informed throughout the project lifecycle.

• Agile Management

Adaptive Planning - Make real-time adjustments to project plans with visualized predictive insights, allowing teams to remain agile and responsive to changes.

Continuous Improvement - Drive ongoing process improvements through predictive analytics, leading to enhanced project outcomes and operational efficiency.

• Data Literacy and Team Empowerment

Democratizing Data Access - Data visualization democratizes access to information, allowing employees at all levels to engage with data directly. This accessibility empowers teams to make informed decisions without relying solely on technical experts.

Fostering a Data-Driven Culture - By promoting the use of data visualization, organizations cultivate a culture where decisions are based on evidence rather than intuition. This shift towards data-driven decision-making enhances overall business outcomes and drives continuous improvement .

The integration of predictive analytics and data visualization is not just a trend; it is a transformative approach that enhances every aspect of project management. By leveraging these tools, organizations can ensure that their projects are not only well-managed but are also positioned for long-term success.

The Future of Project Management 

The future of project management is increasingly driven by the power of data visualization. In a world overflowing with data, transforming raw information into clear, actionable insights is critical. Data visualization turns complex datasets into intuitive visuals, enabling project managers and business leaders to quickly identify trends, spot risks, and make informed decisions. It’s not just about seeing the data; it’s about understanding it at a glance, which is essential for staying agile and competitive .

As data visualization paves the way, its integration with predictive intelligence takes things a step further, enabling businesses to understand the present and anticipate the future.

This is where TrueProject excels. TrueProject is an advanced KPI-based predictive project management solution that integrates powerful data visualization with advanced predictive analytics. It provides real-time risk identification, optimizes resource allocation, and offers early warning signals to prevent issues before they escalate. With its user-friendly interface and seamless integration capabilities, TrueProject empowers your team to adopt a data-driven approach, ensuring continuous improvement and project success. If you’re looking to foresee project challenges and gain optimal insights through data visualization, TrueProject is the solution that will keep you ahead of the curve.

About the Author:

Nisha Antony is an accomplished senior marketing communications specialist at TrueProject and a leader in predictive intelligence. With over 16 years of experience, she has worked as a Senior Analyst at Xchanging, a UK consulting firm, and as an Internal Communications Manager on a major cloud project at TE Connectivity. She is an insightful storyteller who creates engaging content on AI, machine learning, analytics, governance, project management, cloud platforms, workforce optimization, and leadership.

  Endnotes:

• Josh Howarth. “5 Top Data Visualization Trends (2024-2026).” Expoding Topics: Jan 09, 2024.  https://explodingtopics.com/blog/data-visualization-trends
• Menahil Shahzad. “The Future of Data Visualization: Trends and Predictions For 2024.” Analytico: Jan 22, 2024.  https://www.analyticodigital.com/blog/the-future-of-data-visualization-trends-predictions

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