graph presentation of data

Different Types of Charts: 8 Types of Graphs for Data Visualization

• By Judhajit Sen
• November 3, 2024

Graphs and statistical charts play a crucial role in organizing and presenting complex data, making it easier for people to understand. They are widely used in business settings and can help convey important information clearly and effectively. Understanding the different types of graphs and charts available is essential for selecting the most appropriate one for your project. 

Numerical data alone often fails to tell a compelling story. To make data meaningful, it must be communicated in a way that highlights relationships and insights. This is where different chart graph types come into play. Whether you’re dealing with stock market prices or sports statistics, visualizing data through charts helps transform raw numbers into insightful narratives. The good news is that you don’t need advanced statistical knowledge to create these visualizations.

When choosing a graph, it’s important to first consider what you want to convey and who your audience is. While options like pie charts have their place, it’s essential to match the type of visualization to the data and the viewers’ level of understanding. Think about the story behind the numbers and how best to present that information to ensure it resonates with your audience.

A well-crafted data story relies on selecting the right graph type. Companies can gain a competitive advantage by presenting data in a clear and captivating manner. Effective data storytelling involves choosing appropriate visualizations that communicate your message effectively, ensuring that your insights have a tangible impact.

There are numerous chart types, including line charts, bar graphs, stacked bar charts, pie charts, bubble charts, linear graphs, scatter plots, Pareto charts, radar charts, and histograms. Each serves a different purpose and is suited to different types of data. For instance, line charts are great for showing trends over time, while bar charts can effectively compare different groups. Choosing the right chart doesn’t have to be complicated, but it requires thoughtful consideration of your data and audience. 

Ultimately, the goal of using data visualization is to show large amounts of information into an easy-to-understand format. By presenting data visually, you can highlight key findings and insights for those who may not have access to the raw data, making it an essential skill in any field that involves data analysis.

What types of graphs are there? Let’s explore in detail.

Key Takeaways

• Choosing the Right Graph: Selecting the best graph type is essential for effectively communicating data and ensuring your message resonates with your audience.
• Purpose of Each Graph: Different kinds of graphs serve unique functions, like line graphs for trends, bar charts for comparisons, and pie charts for part-to-whole relationships.
• Simplifying Complex Data: Graphs transform large data sets into easy-to-understand visuals, helping audiences quickly grasp key insights.
• Audience Consideration: Tailor the graph to your audience’s level of understanding to present data clearly and engagingly, enhancing decision-making.

Different Types of Charts: Types of Graphs for Data Visualization

A line graph, also known as a line chart, is an example of a simple graph. It is an effective way to illustrate how data changes over time. In this type of data visualization , one axis typically represents a value, while the other axis displays a timeline. This setup allows viewers to easily see trends and patterns, such as fluctuations in temperature or changes in housing prices.

Line charts are versatile and can convey a lot of information at once. They connect distinct data points with straight lines, making it easy to notice trends and changes in variables. For example, you can use a line graph to show the growth of digital marketing interest over time by plotting the number of searches against specific dates.

To create a clear line chart, it’s essential to ensure that your data has a logical order. Use labels and annotations to provide context, and if you have a large dataset, consider using transparency or spacing to enhance visibility. Multiple lines can also be plotted in different colors, allowing for comparisons between various trends.

Line graphs are particularly useful when you want to display trends, make predictions based on historical data, or compare different variables over a specific period. By visually representing continuous changes, line charts help readers make informed projections about future outcomes.

Bar graphs are a straightforward way to compare different categories using rectangular bars. Each bar’s length represents a value, making it easy to see differences between groups. You can create bar charts either horizontally or vertically, depending on the data and your preferences.

One axis of the graph displays the categories being compared, while the other axis indicates the corresponding values. This setup allows viewers to quickly identify which category has the highest or lowest value. Bar charts are especially helpful for visualizing multiple data points, inventories, group sizes, ratings, and survey responses, making them popular in marketing and statistics.

When to use bar charts:

– If you have more than ten items or categories to compare.

– If category labels are long, as horizontal bars can accommodate them better.

Best practices for bar graphs include:

– Use one main color for the bars, with accent colors to highlight significant points.

– Ensure bars are wider than the space between them for clarity.

– Label axes and bars clearly and write labels horizontally for easy reading.

– Order categories alphabetically or by value to maintain consistency.

Bar charts are among the most popular chart types because they are easy to understand. Viewers can quickly interpret the lengths of the bars without needing special skills in data visualization. If you want to present categorical data clearly, a bar chart is a reliable choice.

A pictograph, also known as a pictogram, is a chart that uses pictures or symbols to show data. Unlike traditional graphs that rely on bars or lines, pictographs use icons to display information visually. Each icon corresponds to a specific number of items or data sets, making complex information easier to understand at a glance.

Pictographs are particularly effective when your audience prefers visuals over numbers. They work well for showing the progress of goals or projects, highlighting ratings for comparisons, and sharing survey results. For example, using an image of a book can effectively illustrate how many books were sold over a few months.

When creating pictographs, keep the icons simple to avoid distracting viewers. It’s best to use shades of one color rather than contrasting colors to maintain clarity. Limiting the number of rows to five or ten also improves readability.

Pictographs are valuable tools for overcoming language barriers, making data more accessible and memorable. They can evoke emotional responses, especially in sensitive topics like health data, where an image can communicate more powerfully than numbers alone. By making data engaging and easy to interpret, pictographs are widely used in educational settings and infographics.

A histogram is one type of bar graph that displays the distribution of numeric data across different categories. Unlike traditional bar charts, which represent distinct categories, histograms visualize continuous data. Each bar, or rectangle, in a histogram is connected, with no gaps in between, showing how many data points fall within specific ranges or intervals.

The height of each bar displays the frequency of the data in that range. For example, a histogram might show how many people fall into different age groups in a population, helping to illustrate trends and patterns within the data. This makes histograms useful for identifying the shape of a data set, spotting outliers, and quickly communicating the overall distribution of values.

To create an effective histogram, it’s essential to choose an appropriate number of bins and maintain consistent intervals for accurate data representation. Having enough data points is also important, as histograms are less effective with smaller datasets. Histograms are a powerful tool for summarizing and visualizing large sets of continuous data.

An area graph is a type of chart that shows changes in one or more quantities over time. It is similar to a line chart, using dots connected by lines to display data points. However, in an area graph, the space between the horizontal axis and the line is colored, making it visually striking. This method is particularly useful for illustrating trends and patterns in data, as it emphasizes the magnitude of changes over time.

Area graphs can represent multiple values, allowing users to see how different quantities contribute to a total. For instance, a retailer might use an area graph to display the profits from several stores over a specified period. Each store’s profits can be shown with a different color, helping to visualize how these values stack up against each other.

When using area graphs, it’s essential to keep a few best practices in mind. First, limit the number of categories displayed to four or fewer to prevent overcrowding. Use transparent colors to ensure that data from the background is not obscured. Additionally, consider grouping smaller values together to simplify the graph. Including annotations and explanations can also help viewers better understand the data being presented.

Area charts are ideal for showing trends rather than specific values. They allow for simple comparisons of different datasets over time and can effectively illustrate changes in volume, making them a valuable tool for data visualization.

Scatter Plot

A scatter plot is a graph type that uses dots to show the relationship between two different variables. Each dot represents a pair of values plotted on an x-y coordinate system, where one variable is displayed along the horizontal axis and the other along the vertical axis. For instance, a scatter plot could illustrate how a person’s height relates to their weight.

These graphs are especially useful for identifying patterns, trends, or correlations between numeric variables. When there is a strong correlation, the dots tend to cluster closely together, often forming a line. If there is no correlation, the dots will appear scattered randomly across the plot. This allows for easy visualization of how one variable may affect another.

To enhance clarity, it’s important to follow best practices when creating scatter plots. Starting the y-axis at zero ensures accurate representation of data. Additionally, you can use different dot sizes or colors to represent additional data points or to highlight overlapping points. Including a trend line can also help to visualize the connection between the two variables more clearly.

Scatter plots are versatile tools in data visualization, providing insights into how two sets of data are related and helping to uncover trends that may not be immediately obvious.

A pie chart is a common type of circular graph that visually represents data as slices of a pie. Each slice shows how much a particular category contributes to the whole, with all slices together totaling 100 percent. Pie charts are effective for illustrating part-to-whole relationships, making them ideal for small data sets, usually with three to seven categories.

To create a pie chart, you need a list of categories and their corresponding values. Each slice should be clearly labeled, and it’s best to use consistent colors to help viewers easily associate colors with specific categories. This type of chart works well when comparing various budget allocations, population segments, or market research responses.

When designing a pie chart, limit the number of slices to keep it clear and avoid clutter. If you have similar categories, consider grouping them into one larger slice. To enhance clarity, position the largest slice at the 12 o’clock position and arrange the others in a logical order. 

Remember, pie charts shine when you want to emphasize the proportions of different segments, but they may confuse viewers if used with larger data sets. Pie charts are a simple and effective tool for visualizing how parts relate to the whole.

Column Chart

A column chart, also called a vertical bar chart, is a simple and versatile tool for visualizing data. It is especially effective for presenting chronological data when there are only a few key dates to highlight. This type of chart and graph is useful for comparing categories or items, showcasing qualitative data, and illustrating the situation at a single point in time with various data points.

Column charts display differences in numeric values clearly, making them ideal for highlighting significant changes in data. They are similar to bar charts but oriented vertically, which can create challenges with long categorical labels that may overlap. Shorter labels work best for column charts, as they minimize clutter.

To make the most of a column chart, follow these best practices:

– Plot bars against a zero-value baseline to enhance clarity.

– Keep bars rectangular and avoid 3D effects, which can distract viewers.

– Order categories consistently, either from highest to lowest or lowest to highest.

– Minimize visual distractions, such as excessive gridlines, to focus attention on key data points.

– Use contrasting colors to highlight specific columns, ensuring they stand out.

– Maintain consistent scaling on the axes for accurate interpretation of the data.

Overall, column charts are an effective way to compare data across different categories and quickly identify trends.

Wrap-up: Types of Graphs

Understanding the different graph types is crucial for effective data presentation . Data visualization, such as line charts, bar charts, and pie charts, simplify complex information, allowing audiences to grasp trends and relationships quickly. Each type serves a unique purpose; for example, line charts are great for showing trends across time, while bar charts make comparing categories straightforward. Pie charts highlight part-to-whole relationships, making them ideal for smaller datasets.

Selecting the right graph depends on your data and audience. A clear and engaging visual can transform raw numbers into compelling narratives, helping to communicate insights that drive decision-making. Ultimately, mastering all types of graphs enhances your ability to tell a meaningful data story, making your presentations more impactful in any field.

Frequently Asked Questions (FAQs)

1. Why are graphs important in data presentation?  

Graphs help simplify complex data, making it easier to understand patterns and insights. They can turn raw numbers into meaningful stories that resonate with audiences.

2. What are some of the names of graphs commonly used?  

Common graphs include line charts for trends, bar graphs for comparisons, pie charts for proportions, and scatter plots for correlations. Each type serves a unique purpose.

3. How do I choose the right graph for my data?  

First, consider what message you want to convey and who your audience is. Select a graph that highlights your data effectively and matches the audience’s understanding level.

4. Do I need advanced skills to create these data graphs?  

No, creating effective graphs doesn’t require advanced skills. With thoughtful design and simple tools, anyone can make clear and impactful visualizations.

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

Updated: May 22, 2024

Published: May 07, 2015

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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