uses of charts in data presentation

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

Published: June 08, 2023

There are more type of charts and graphs than ever before because there's more data. 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. Let's talk about the types of graphs and charts that you can use to grow your business.

Free Excel Graph Templates

Tired of struggling with spreadsheets? These free Microsoft Excel Graph Generator Templates can help.

• Simple, customizable graph designs.
• Data visualization tips & instructions.
• Templates for two, three, four, and five-variable graph templates.

You're all set!

Click this link to access this resource at any time.

Different Types of Graphs for Data Visualization

1. bar graph.

A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.

Best Use Cases for These Types of Graphs

Bar graphs can help you compare data between different groups or to track changes over time. 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.
• Use horizontal labels to improve readability.
• 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 chart a continuous data set.

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

Line graphs can help you 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, you 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. You can also use bullet graphs to 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.

Different Types of Charts for Data Visualization

To better understand these 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

You can use both column charts and bar graphs to display changes in data, but column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar graphs 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

2. dual-axis chart.

A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous data set, and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.

A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.

For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.

This makes it simple to see the connection between the number of customers and increased revenue.

You can use dual-axis charts to compare:

• Price and volume of your products.
• Revenue and units sold.
• Sales and profit margin.
• Individual sales performance.

Design Best Practices for Dual-Axis Charts

• Use the y-axis on the left side for the primary variable because brains naturally look left first.
• Use different graphing styles to illustrate the two data sets, as illustrated above.
• Choose contrasting colors for the two data sets.

3. Area Chart

An area chart is basically a line chart, but 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 you 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 graphs 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.

4. Stacked Bar Chart

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

These graphs 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 graphs 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 Graphs

• 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.

5. Mekko Chart

Also known as a Marimekko chart, this type of graph 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

You can 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 and graphs, 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.

6. Pie Chart

A pie chart shows a static number and how categories represent part of a whole — the composition of something. 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 graph 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.

7. Scatter Plot Chart

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 graph 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 graph 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.

8. 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.

9. Waterfall Chart

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.

11. 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 graphs 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.

12. 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.

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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Article • 11 min read

How to Use Charts and Graphs Effectively

Choosing the right visual for your data.

By the Mind Tools Content Team

Visual representations help us to understand data quickly. When you show an effective graph or chart, your report or presentation gains clarity and authority, whether you're comparing sales figures or highlighting a trend.

But which kind of chart or graph should you choose? If you click on the chart option in your spreadsheet program, you'll likely be presented with many styles. They all look smart, but which one works best for your data, and for your audience?

To figure that out, you need a good understanding of how graphs and charts work. This article explains how to use four of the most common types: line graphs, bar graphs, pie charts, and Venn diagrams.

How to Tell a Story With Charts and Graphs

The main functions of a chart are to display data and invite further exploration of a topic. Charts are used in situations where a simple table won't adequately demonstrate important relationships or patterns between data points.

When making your chart, think about the specific information that you want your data to support, or the outcome that you want to achieve .

Keep your charts simple – bombarding an audience with data will likely leave them confused and uncertain, so remove any unnecessary elements that could distract them from your central point.

Our brains process graphical data in a different way to text. Your audience will subconsciously seek a visual center that draws their attention. Only use bright colors for areas that you want to emphasize, and avoid tilting or angling your chart, as this can cause confusion.

If the data doesn't support your point of view, avoid manipulating it to do so. This is not only unethical, it's also relatively easy to spot for anyone who is experienced in analyzing data.

How to Create Basic Graphs and Charts

The word "chart" is usually used as a catchall term for the graphical representation of data. "Graph" refers to a chart that specifically plots data along two dimensions, as shown in figure 1.

Figure 1: x- and y -Axes

When you plot your data, the known value goes on the x -axis and the measured (or "unknown") value goes on the y -axis. For example, if you were to plot the measured average temperature for a number of months, you'd set up axes as shown in figure 2:

Figure 2: The Known Value Goes on the Horizontal x -Axis and the Measured Value on the Vertical y -Axis

The following sections cover the most commonly used types of data visualization.

Line Graphs

One of the graphs you will likely use most often is a line graph.

Line graphs simply use a line to connect the data points that you plot. They are most useful for showing trends and for identifying whether two variables relate to (or "correlate with") one another.

Examples of trend data include how sales figures vary from month to month, and how engine performance changes as the engine temperature rises.

You can use correlation data to answer questions like, "On average, how much sleep do people get, based on their age?" or "Does the distance a child lives from school affect how frequently they are late?"

Data can be continuous or discontinuous (or discrete).

Continuous data is measured , and can represent any value on a continuous scale: height, weight and time are all examples of continuous data.

Discontinuous data is not measured but counted : numbers of employees in a company or cars in a traffic jam are examples of discontinuous data.

Along the x -axis of a line graph, you can only use continuous data. This is because line graphs are used to make a direct link between the data points. If the variables are not continuous, a bar graph is probably more appropriate. (See the section on bar graphs, below.)

Using Line Graphs: An Example

ABC Enterprises' sales vary throughout the year. By plotting sales figures on a line graph (as shown in figure 3), you can see the main fluctuations during the course of a year. Here, sales drop off in June and July, and again towards the end of the year.

Figure 3: Example of a Line Graph

While some seasonal variation may be unavoidable for ABC Enterprises, it might still be possible to boost cash flows during the low periods through marketing activity and special offers.

Line graphs can show more than one line or data series, too. It's easy to compare trends when you represent them on the same graph.

For example, you might have different lines for different product categories or store locations, as shown in figure 4, below.

Figure 4: Example of a Line Graph With Multiple Data Series

Another type of graph that shows relationships between different data sets is the bar graph.

In a bar graph, the height of the bar represents the measured value: the higher or longer the bar, the greater the value.

Using Bar Graphs: An Example

ABC Enterprises sells three different models of its main product: the Alpha, the Platinum, and the Deluxe. By plotting the sales of each model over a three-year period, you can see trends that might be masked by a simple analysis of the figures themselves.

In figure 5, it's clear that although the Deluxe is the highest-selling, its sales have dropped off over the three-year period, while sales of the other two have continued to grow.

Perhaps the Deluxe is becoming outdated and needs to be replaced with a new model. Or it could be suffering from stiffer competition than the other two models.

Figure 5: Example of a Bar Graph

You could also represent this data on a multiple-series line graph, as shown in figure 6.

Figure 6: Data From Figure 5 Shown on a Line Graph

Often, the choice of which style to use comes down to how easy the trend is to spot. In this example, the line graph works better than the bar graph, but this might not be the case if the chart had to show data for 20 models, rather than just three.

Generally, if you can use a line graph for your data, a bar graph will often do the job just as well. However, the opposite is not always true: when your x -axis variables represent discontinuous data (such as employee numbers or different types of products), you can only use a bar graph.

Data can also be represented on a horizontal bar graph, as shown in figure 7. This is a better method when you need more space to describe the measured variable. It can be written on the side of the graph rather than squashed underneath the x -axis.

Figure 7: Example of a Horizontal Bar Graph

A pie chart compares parts to a whole. As such, it shows a percentage distribution. The pie represents the total data set, and each segment of the pie is a particular category within the whole.

To use a pie chart, the data you are measuring must depict a ratio or percentage relationship. Each segment must be calculated using the same unit of measurement, or the numbers will be meaningless.

Using Pie Charts: An Example

The pie chart in figure 8 shows where ABC Enterprises' sales come from.

Figure 8: Example of a Pie Chart

Be careful not to use too many segments in your pie chart. More than six and it gets far too crowded.

If you want to emphasize one of the segments, you can detach it a little from the main pie.

For all their obvious usefulness, pie charts do have limitations. For example, the layout can mask the relative sizes and importance of the percentages. Consider whether a bar graph would better illustrate your intentions.

Venn Diagrams

Venn diagrams show the overlaps between sets of data.

Each set is represented by a circle. The degree of overlap between the sets is depicted by the amount of overlap between the circles.

A Venn diagram is a good choice when you want to convey either the common factors or the differences between distinct groups.

Using Venn Diagrams: An Example

Figure 9 shows sales at Perfect Printing. There are three product lines: stationery printing, newsletter printing, and customized promotional items, such as mugs.

Figure 9: Example of a Venn Diagram

By separating out the customers by the type of product that they buy, it becomes clear that the biggest group of customers (55 percent of the total) are those buying stationery printing.

But, most stationery customers are only using Perfect Printing for stationery (40 percent). They may not realize that Perfect Printing could also print their company newsletters and promotional items. Perfect Printing could consider some marketing activity to promote these product lines to its stationery customers.

Newsletter customers, on the other hand, seem to be well aware that the company also offers stationery printing and promotional items – 23 percent of newsletter printing customers also buy other products.

Try creating a few example charts using Excel, Google Sheets or other chart-making software. Get comfortable entering data and creating the charts so that when you need to create one for real, you are well prepared.

Charts and graphs help to express complex data in a simple format. They can add value to your presentations and meetings, improving the clarity and effectiveness of your message.

There are many chart and graph formats to choose from. To select the right type, it's useful to understand how each one is created, and what type of information it is used for. Are you trying to highlight a trend? Do you want to show the overlap of data sets, or display your data as a percentage?

When you're clear about the specific type of data that each chart or graph can be used with, you'll be able to choose the one that best supports your point.

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Blog > Dataviz Resources

80 types of charts & graphs for data visualization (with examples).

Ask any dataviz expert and they will tell you there aren’t many things as annoying as the wrong use of data visualizations. Well, duh. It’s easy to say if your job is to know all about it. But what about the rest of us? What about those who don’t make a face when they look at a simple pie chart? How do we know when to pick the right chart type and avoid disapproval from the entire community of dataviz geeks and lovers?

First and foremost, ask yourself what is it you actually want to show and who is your audience? Sounds simple, I know. But remember, you can’t please everyone. And sometimes, a pie chart is really fine. We don’t hate pie charts and actually, there are cases when they’re quite appropriate charts to use to communicate data. 

Yes, you can try to explore variations and alternatives to different chart types, it is encouraged. But before you gather all of your data and start creating beautiful graphs and visualizations, take a step back for a second and think. Who do you want to show your data to? Are the viewers equally knowledgeable about dataviz best practices? It’s very likely that you just want to present your information to someone who needs to easily understand it.

For this reason, it’s equally important to consider the right type of data visualization for you.

Read this article if you want to learn about the way you can display your data and how to tell your data story to your specific audience.

Now, if you want to include different charts and graphs in your final product, it’s a great next step to explore your options. There are many, many chart types and we won’t be able to cover all of them. In this article, we will show you some of the most important charts that can effectively convey a message and communicate your data, creating engaging data storytelling for your readers. Below, you might find charts you are familiar with and some that are less common. Either way, we hope you explore all chart types and find the most suitable ones for you and your data visualization project. The list consists of eighty types of charts and graphs, many of which you can create online for free with Datylon Online , or with our chart maker plug-in Datylon for Illustrator .

We divided the charts below into six categories that vary per use case. Sometimes, some of the charts can fall under multiple categories, so to make it easier, we only listed them once.

1. Comparison

Alternative name: Bar graph

One of the most common chart types out there. A bar chart is a set of rectangles with a length proportional to the values it represents. Each rectangle – the bar, is a representation of one category. Bar charts are great for comparison. The differences in bar length are easier to perceive, than, for example, differences in size and color.

Bar charts are commonly used charts due to their simplicity. Viewers mostly need to decode their bars' length and position, making bar charts very easy to understand. The general public is fairly capable of reading bar charts, so no additional dataviz expertise is necessary. For this reason, bar charts are doing their job really well. That's why, if the data structure and the actual message you're trying to convey allow for it, you should consider using bar charts in your data visualization.

It’s worth noting that to be really correct, bar charts display the bars horizontally. If you turn them 90 degrees, you will get a column chart. But, remember that long labels don’t suit column charts because of easy overlapping. You don’t have that issue in a bar chart.

If you want to improve your dataviz skills and design the best bar chart, we recommend you read this article about bar charts . But you can also check our bar chart resource page and discover even more pro design tips. You can also find some bar chart examples on our inspiration page .

Column chart

Alternative names: Column graph , Vertical bar chart

Long story short, you can say that a column chart is the same thing as a bar chart, turned by 90 degrees. Indeed, a column chart is a type of chart that resembles a bar graph with bars positioned vertically. They are often considered the same type of chart but from the dataviz point of view, that’s wrong. The main difference between a column chart and a bar chart is in the usage of categorical labels. Long labels don't suit column charts because of easy overlapping. But it might be useful if the labels are short and don’t take up a lot of horizontal space. Still, when it comes to design recommendations, you can use our bar chart resource page to learn how to greatly improve the readability of your column chart as well. You can find column chart examples on the inspiration page .

Grouped bar/column chart

Alternative names: Paired bar/column chart , Clustered bar/column chart

Made with Datylon - Edit

A grouped bar chart (or a grouped column chart if the bars are positioned vertically) is a multi-series variation of a bar/column chart where every category is represented by several columns communicating different aspects of the main category. Columns of each category are separated from the other categories using spacing. We use this type of chart to compare multiple series. Opposite to a basic bar chart, which doesn’t require any data to be formatted, to create a grouped bar/column chart, the data must be first organized. You can find more grouped bar chart examples on inspiration page .

Lollipop chart

Alternative name: Lollipop plot

A lollipop chart can be a sweet alternative to a regular bar chart if you are dealing with a lot of categories and want to make optimal use of space. It shows the relationship between a numeric and a categorical variable. This type of chart consists of a line, which represents the magnitude, and ends with a dot, or a circle, which highlights the data value. So it probably suffices to say that it is designed to resemble a bunch of lollipops. You can find more examples of lollipop charts on inspiration page .

Bullet chart

Alternative name: Bullet graph

A bullet chart is a type of chart designed to benchmark against a target value and ranges. It’s a very space-efficient chart used primarily for displaying performance data. Visually, bullet charts resemble a combination of bar/column charts and progress bars. The results are shown in a single bar or column. The ranges bar is constructed based on values from a category that comparison will be based on (for example competitor sales figures). All these values are then divided into a certain number of sub-ranges (in most cases it’s quartiles). Target shows the value which is aimed for. And the bar shows the actual figures. You can find more examples of bullet charts on inspiration page .

Alternative name: Dot chart

A dot plot (shows one or more quantitative values per category by plotting one or more dots per category on a numerical (or date-time) axis. A dot plot with only one value per category makes a comparison between those categories very easy. When the dot plot has multiple values per category, you can also compare within the categories. This results in a chart type that packs a lot of information in a small space. This chart may need gridlines that turn a dot plot into a chart with a proper context. We wrote a very interesting article about dot plots.

Make sure to also check our dot plot resource page and discover pro design tips. You can find more examples of dot plot on inspiration page .

Alternative names: Dumbbell plot , Dumbbell chart , Connected dot plot , Dumbbell dot plot , DNA chart , Barbell chart

A dumbbell is a type of dot plot with two connected values per category. Use it when you want to emphasize the delta (change) between the two values (data points, i.e. two points in time) and to compare and visualize this size in a difference between these two values across all categories. A dumbbell consists of dots (or circles) and connectors (or lines). Not adding marks and only leaving the connector makes it a range chart. We mentioned dumbbells throughout deep dive article about dot plots . You can find more examples of dumbbell charts on inspiration page .

Alternative names: Pictorial chart , Proportional unit chart , Picture graph

A pictogram chart is a type of chart that uses icons or symbols, or even small images, to represent data. Each of these icons corresponds to a certain category. Pictogram charts to some extent resemble bar charts, but instead of using a bar, they show icons. Some data visualization experts might argue this type of chart is very basic, to the point that it’s widely used in schools and kindergarten. While this is true, it’s also very important to keep in mind that using a pictogram chart helps overcome language barriers and it’s really easy to interpret. Moreover, it makes your data story memorable!

Alternative name: Proportional area chart

An icon chart will be a perfect choice if the position of the marks is not driven by data. Values can be bound to the color and size of the icons. The icon chart uses area rather than length to visualize values, which allows it to display a larger range of values in a compact way. But keep in mind, if you’re planning to use an icon chart in your visualization, it’s important to use the area and not the radius to present your value. This helps better compare the icons visually, as the difference between the categories will be much bigger if you use the radius. This will be misleading to your readers. See other icon chart examples on the inspiration pagehere .

Alternative name: Range chart

A range plot sometimes looks like a bar chart. The difference is that a range plot shows two values of a category, instead of just one. A range plot shows two points with a connecting line between them. This line indicates the difference, or a gap, between these points and suggests a direction of such change. So using this type of chart is great if you want to highlight this difference, rather than the values themselves. A use case example is any sort of demographical gap, i.e. gender pay gap. See examples of similar charts on our inspiration page .

Radial bar chart

Alternative name: Circular bar chart

A radial bar chart is simply a variation of a regular bar chart with the main difference being the circular shape of the chart. The chart itself is plotted on what is called a polar coordinates system. It means that each bar appears in a circle. The larger the value, the longer the bar. What's really great about radial bar charts is they are really beautiful, even impressive charts that can be used to compare key metrics in your data. The challenge that comes with using radial bar charts is that they're not the easiest to interpret. Some websites refer to radial bar charts as multilayered donut charts or multi-level doughnut charts but it's worth pointing out that it's not the same type of chart. You can find more details about this chart type on Data Viz Project .

Parallel coordinates

Alternative names: Parallel plot , Parallel coordinates plot

The parallel coordinates chart resembles a line chart, but instead of time values, categories are plotted on the horizontal axis. It allows you to plot a multitude of categories/dimensions without compromising the readability in a simple 2d space - all of the dimensions follow the same pattern. A dimension can have both a separate axis or just one of the gridlines if all the dimensions share the same data range. The simplicity of the chart, however, adds some limitations. Maximum two neighboring dimensions relationships can be followed at a time, so the ordering plays a crucial role in this chart.

Radar chart

Alternative names: Spider chart , Spider graph , Web chart , Spider web chart , Star chart , Star plot , Cobweb chart , Irregular polygon , Kiviat diagram

A radar chart shows a comparison between multiple data points or groups (minimum of three). It consists of several axes, all coming from the same point in the center (which resembles a spider web). Although it’s a very interesting chart to use, it’s important to keep in mind that it is harder to read. As it is designed in a circular fashion, it requires extra visual perception, in contrast to the more common linear types of charts and graphs. It is often easier to replace it with another type of chart. If all axes in your chart have the same scale, then a bar chart or sometimes a lollipop will suffice. If the axes have a different scale, it’s good to use parallel coordinates.

Nightingale chart

Alternative names: Nightingale's graph , Nightingale rose chart , Rose diagram , Coxcomb chart ,  Polar area chart

This chart is visually similar to a pie chart, but a Nightingale chart does not communicate a part-to-whole relationship. It compares values between categories like a bar chart does, only this one is radial.

Waterfall chart

Alternative names: Flying bricks chart , Mario chart , Bridge chart , Cascade chart

A waterfall chart is a type of graph that usually shows positive and negative values of change between two points, which helps in understanding the cumulative effect of these changes (so the net change). This chart does not only look at the starting value and the ending value of your data set but also visualized each individual positive or negative change that happened. As you can imagine, this type of chart is quite useful in financial sectors or human resources, but also in other industries (think of inventories, revenue tracking, etc.). Last but not least, the waterfall chart takes its name from the fact it looks like a waterfall. In the chart, the first value (column) typically starts from the baseline of zero, as does the ending value. They are connected by a number of seemingly floating shorter bars (that represent the said changes). The whole shape of the chart resembles then a waterfall.

Matrix chart

Alternative name: Matrix diagram

A matrix chart is a very common type of chart that helps in visualizing the relationship between two or more variables in a data set. Specifically, it shows the presence and strengths of such relationships and it does so in a grid format. It can have six different forms (shapes) depending on how many groups must be compared (L, T, Y, X, C, R, and roof-shaped). This chart usually presents a huge amount of data, so its visual display is limited. A matrix chart is very suitable for (but not limited to) project managers.

Small multiples

Alternative name: Trellis chart , Lattice chart , Panel chart

Unlike all the other graphs in this article, Small multiples are more of a visualization concept than a graph itself. That is because Small multiples use the same type of chart in it and multiply it within a grid to show different slices of the data set. The main advantage of using small multiples is the possibility of showing three or (usually) more variables presenting different values in the same graph without confusing your audience. If you go for this type of data visualization, make sure not to apply multiple colors in the charts as it might decrease the readability. You can find more Small multiples examples on our inspiration page .

Alternative name: Tag cloud , word collage , wordle

A word cloud is not a typical type of chart but it deserves its place in this list as it still is an instrument used to visualize qualitative (text) data. A word cloud is nothing more than a visual cluster of different words which vary in size accordingly to their frequency within the data set. In other words, the more often a certain word (or a keyword) appears in the text, the bigger (and perhaps bolder) it will be in a cloud. This type of chart is quite common across so many industries and segments. It can be a great visualization tool for students working on their dissertation who want to analyze their interviews. But just so you know, there are much more creative ways to show qualitative data.

Slope chart

Alternative name: Slopegraph

A slope chart is a chart that emphasizes the evolution between two values by using the angle of the slope to communicate the difference. It can be a change over time or a transition. A slope chart can be a good alternative for a line chart, grouped- or stacked bar chart, if we only have two points in time we want to address. See other slope chart examples See other slope chart examples on inspiration page .

Table chart

A table chart is a chart that helps visually represent data that is arranged in rows and columns. Throughout all forms of communication and research, tables are used extensively to store, analyze, compare, and present data.

Categorical scatter plot

A categorical scatter plot differs from a regular scatter plot by the presence of a categorical axis. It can be just one categorical axis or both of them. A categorical scatter plot can be quite similar to a dot plot. See other scatter plot examples See other scatter plot examples on our inspiration page .

Quadrant chart

Alternative names: matrix diagram , matrix chart , 4-quadrant matrix chart

A quadrant chart is very similar to a scatter plot but it’s divided into four equal parts (quadrants) in a 2x2 matrix. It is useful if we want to group distinctly data marks for some specific type of analysis. One of the best and most well-known examples of using the quadrant chart is for a SWOT analysis.

2. Correlation (relational)

Alternative names: Heat map , Heat table , Density table

A heatmap shows data variances, such as patterns, trends, and correlations. It does this by using color, hue, or intensity, as well as data labels, as a direct representation of the values. By adding a date or a time scale on the x-axis it shows how the values evolve over time. The data in a heatmap is structured as a table. Using a heatmap as a chart lets you explore the data and gives hints on where to look for outliers, other viewpoints, or specific angles. If you would like to explore the fascinating world of heatmaps, we definitely recommend you this article.

Also, make sure to check our heatmap resource page and discover pro tips on how to design the best heatmap chart yourself. You can find more heatmaps examples on the inspiration page.

Bubble chart

Alternative name: Bubble plot

Deriving from a scatter plot, a bubble chart is a chart that looks at a relation between three (numeric) variables. Two of those variables are represented by dots located between axes. The third value is represented by the size of a bubble. But with some expansions, a bubble chart can represent up to seven variables at once. But as it’s very easy to overwhelm a reader with too much information, it’s better not to plot too many variables. Being really popular among researchers and analysts, a bubble chart is also a chart with one of the best data/space ratios. One of the most interesting things about bubble charts is that they can be colored in many different ways. Make sure to check out a blog post taking a closer look at bubble charts .

Also, refer to our bubble chart resource page and discover pro tips on how to design the best bubble chart yourself. And if you want to see other bubble chart examples, find them on the inspiration page .

Scatter plot

Alternative names: Scatterplot , Scatter chart , Scattergram , Scatter diagram , Scatter graph

A scatter plot shows values for two numerical variables by plotting them as dots between horizontal and vertical axes. Simple one-sized data marks give a clear view of every observation’s positioning in a two-variable plane. A scatter plot is often used to show correlations between numeric variables and identify patterns. Being a swiss knife among the charts, a scatter plot is usually the first one for data exploration. It is a chart with one of the best data/space ratios. A scatter plot is also known for its versatility. It gives a lot of inspiration to infographic designers and data visualization specialists. It can be turned into almost any chart: heatmap, dot plot, icon chart, tilemap, or some hybrid chart. On the inspiration page you will find more scatter plot examples .

Connected scatter plot

Once upon a time, a line chart fell in love with a scatter plot. Were they to have a baby, it would look exactly like a connected scatter plot. This type of chart consists of a scatter plot with two variables and a line drawn between the dots in a continuous path. See other scatter plot examples on the inspiration page .

Hexagonal binning

Alternative names: hexagonal plot , hexagonal bin plot

A hexagonal binning is a method that uses hexagons in order to show the density of the data points. It is a good alternative to a scatter plot if the data gets too dense to interpret. The hexagons are binned into the area of the chart, and the color or hue (color intensity) is assigned accordingly to the number of observations it covers.

Contour plot

A contour plot allows you to visualize three-dimensional data in a two-dimensional plot/plane. Contour plots are typically used in cartography, as their contour lines can nicely indicate elevations. But they can also be used in meteorology, astrology, and similar scientific fields, where the contour lines would represent density or temperature.

3. Part-to-whole & hierarchical

Stacked bar chart & stacked column chart.

Being a variant of a bar chart (or a column chart, if plotted vertically), a stacked bar/column chart shows a relation of stacks to the whole bar or column and relations between whole bars/columns. The whole bar/column can be also presented as 100%. In this case, the stacks show a relative part to the whole bar/column in percentages. You can find more examples of bar chart on inspiration page .  

Diverging (stacked) bar/column chart

A diverging bar chart (or, if plotted vertically, a diverging column chart) is a chart that resembles a regular bar chart. However, a crucial difference is a baseline located in the middle (usually corresponding to a zero) and the bars extending to both sides of this midpoint. Often used to display results of a questionnaire or a survey, but definitely not limited to this use case, as seen in the example above. In a diverging bar chart, we use contrasting colors to show the categories being compared. A very common variation of this chart is called a ‘diverging stacked bar chart’, which adds additional segments. In other words, it’s very similar to a regular stacked bar chart but with an extra baseline in the middle. But a diverging stacked bar is a very good alternative to a stacked bar chart since it is easier to compare the stacks with it. That is because the stacks here share the same baseline, which makes comparison much easier. See more variations of bar charts on inspiration page .

Population pyramid

Alternative names: Age-sex pyramid , Age structure diagram

Very similar to a diverging bar chart, a population pyramid is a type of chart that specifically visualizes the age and gender distribution across populations. Typically used by demographers, population pyramids can be a very simple and nice addition to many reports. You can find other bar chart examples You can find other bar chart examples on the inspiration page .

Alternative name: Pictograph

An icon array is a graph that clearly visualizes a proportion of a unit. Icon arrays use a matrix of icons, usually a 100. Each one of those icons represents a unit of something (i.e. people). A portion of the icons is then colored to represent a numerical value in our data. The rest of the icons can be greyed out or even absent. A very common type of graph, icon arrays are extremely easy to interpret. You can see more icon array examples You can find more icon array examples on inspiration page .

Waffle chart

Alternative names: Square pie chart , Square area chart , Gridplot

A waffle chart is very similar to an icon array. However, instead of using different icons, it consists of a grid of 100 square (or even round) cells. Each cell represents 1%. This grid pattern typically displays progress towards a target (or a completion percentage) but can be also used to show parts-to-whole contribution. Waffle charts are often called a square alternative to a pie chart and are very easy to interpret. And they do look like waffles. See examples of similar charts on inspiration page .

Alternative names: Pie graph , Pizza chart, Circle chart

Arguably the most popular type of chart, a pie chart is a circular graph that visualizes a part-to-whole relationship. It shows how the data is divided into categories with a certain value (the slices), but it always keeps the link between the value of one category and the total sum of those categories (the pie). This means that the slices should add up to a logical sum. If the data is in percentages, the total should round up to a hundred. If the data is in absolute values, for example in dollars, the categories should form a meaningful total. A pie chart works best with only a few categories, otherwise, the chart becomes an unreadable clutter. It is also very suitable when one category is very big or very small compared to the other categories. Pie charts are often ridiculed by dataviz specialists. Read the deep dive pie chart article to see our arguments for using pie charts. And if you want to create a really good pie chart yourself, don’t miss out on the pie chart resource page full of pro design tips. Also you can find more pie chart examples on inspiration page .

Donut chart

Alternative names: Doughnut chart

A donut chart is practically the same thing as a pie chart, with an obvious difference of an empty round hole in the middle, making it resemble a donut. However, the data-ink ratio of a donut chart is better than that of a pie chart and the data is depicted by the length of the sectors, rather than the surface, which is easier to interpret. Another advantage of a donut chart is that the space in the center can be used to add a title or a significant value derived from the data. For your convenience, we also created a donut chart resource page with valuable design tips for your next donut chart. On inspiration page you will find more examples of pie and donut charts.

Semicircle donut chart

Alternative name: Half moon chart , Half donut chart , Semi-circle doughnut chart

This chart works the same as a normal pie or donut chart, only the sum of all categories results in half a circle instead of a full circle. It can serve as a basis for a gauge chart, by using the slices to show progress or by adding a pointer. We have more pie and donut chart examples on the inspiration page .

Marimekko chart

Alternative names: Mekko chart , Mosaic chart , Mosaic plot

A Marimekko chart is a type of two-dimensional stacked chart that depicts data through varying heights of different segments and widths of columns. These columns are scaled to fill up the entire available chart area. They can be hard to read, especially if there are many segments. Although Marimekko charts can be used to visualize different types of data, they are most commonly used for analyzing marketing and sales data.

Treemap charts come in handy when you are dealing with large numbers of categories with a hierarchical structure. A treemap consists of multiple categories and each category in the treemap is given a rectangle. The categories could be subdivided into smaller rectangles if you are dealing with subcategories in the data. The size of the area of the rectangles communicates the value. Therefore, treemaps are very useful charts in finding relationships fastly, both within and between categories. Another benefit of a treemap is the efficient use of space which makes it easy to show a lot of data at the same time. If you’re curious about the history and different features of a treemap chart, you can’t miss the deep dive article . We also have a very elaborate treemap resource page for you to check out before you start making your own treemap.

Circular treemap

Alternative name: Circular packing , Circle packing

This type of treemap consists of circles instead of squares, which makes them a bit less space-efficient. Though, because of the space in between the circles, the groups and subgroups are presented very neatly. Moreover, when designed properly, the circular treemap could be really pleasing to look at.

Convex treemap

Alternative names: Voronoi treemap , Polygonal partition

A convex treemap is essentially the same thing as a regular treemap but with convex polygons instead of rectangles. With this type of treemap, it is possible to create treemaps within arbitrary shapes like circles, triangles, or any shape you can think of. Convex treemaps are great if you wish to show grouping and relations instead of the hierarchical structure typically found in a regular treemap. We presented a very nice example of such a treemap in this article that generally looks closely at treemaps.

Alternative name: Phylogenetic tree

To put it simply, a dendrogram is a diagram representing a tree or a network structure. Consisting of stacked branches, it is used to visualize taxonomic relationships (hierarchical relationships between objects). Dendrograms are commonly used in biology to show the clustering of genes but they can illustrate any type of grouped data.

Venn diagram

Alternative name: Set diagram , Logic diagram

Originating in the 1800s, Venn diagrams are widely used within different industries to illustrate relationships (i.e. commonalities or differences) between two or more sets. This type of graph is commonly used in presentations and reports. They are closely related (and similar) to Euler diagrams with the difference that the Euler diagram will omit a set if no relationship exists.

Euler diagram

Euler diagrams are very similar to Venn diagrams, so it’s not surprising that people may occasionally confuse the two. The main difference is that the Euler diagram (which is pronounced Oy-ler) will omit a set if no relationship exists. What does it mean? A Venn diagram shows all possible logical relationships between a collection of sets, while an Euler diagram will only show the relationships that actually exist in real world. If you’re curious to understand it better, we recommend this article that explains the difference between mentioned charts .

Circular gauge

Alternative names: Angular gauge , Radial gauge chart

A circular gauge is a type of chart that uses a circular or half-circular scale with a needle indicating a value on that circular scale. For this reason, it resembles a speedometer or even an analog clock. The interesting thing about circular gauges is that they are so easy to customize and can take so many different, visually interesting forms. This type of chart is extremely useful in all sorts of dashboards.

Sunburst chart

Alternative names: Multi-level pie chart , Multilayer pie chart , Sunburst graph , Ring chart , Radial treemap

A sunburst chart has many names but whatever you call it, it’s still a spectacular type of graph. It shows a hierarchical dataset through a series of concentric outward rings. Each of those rings corresponds to a different hierarchy level. The inner circle looks like a donut chart, but each outer ring can be sliced up depending on its relationship to the inner (parent) circle. Sunburst charts are often a good alternative to treemaps, but if you do opt for this type of chart, keep in mind that its radial layout takes more space than a rectangular shape of a treemap.

Pyramid chart & Funnel chart

Alternative name: Triangle chart

If you work in sales or marketing, this type of chart definitely won’t be new to you. A pyramid chart and a funnel chart are visually almost the same - if you flip a pyramid chart, you get a funnel chart. Funnel charts are very commonly used to visualize the flow of users through a business or sales process. This information is usually paired with the revenue or potential revenue amount at each stage of the funnel. They are widely used in infographics and business presentations or dashboards. In the pyramid chart, each level of the pyramid indicates a different level of hierarchy (among the topics).

4. Data over time (temporal)

An area chart is similar to a line chart. Data values are plotted in a similar way, and connected with lines. The difference is that the area between these lines and the x-axis is filled with a color. This helps in visualizing the change in volume over time. It doesn’t focus on specific data values but more on showing a general change that occurs over a period of time. You will find more area chart examples on inspiration page .

Stacked area chart

Alternative name: Stacked area graph

A stacked area chart is a variation of an area chart. It visualized the evolution of multiple data series (value of several groups) over time. See other stacked area chart examples on the inspiration page .

Stream graph

Alternative names: Streamgraph , ThemeRiver

A stream graph is undoubtedly one of the most beautiful chart types available. This stunning type of chart derives from a stacked area chart, from which it differs by using a central baseline rather than a fixed axis. A stream graph then visualizes different values (compound volumes) around the baseline. This creates a visualization that resembles a river-like stream. The shape of the stream, which consists of peaks and troughs referencing different values over time, can also indicate seasonal patterns. See more similar chart examples on our inspiration page .

A bump chart is a very good choice if you’re interested in showing rankings over time. Since every step in ranking has the same size, this type of chart is not useful in showing the data precisely. See other bump charts and line charts examples on the inspiration page .

Bump area chart

A bump area chart (or an area bump chart) is a variation of a bump chart that instead of only displaying the ranking over time also shows the values on the y-axis. This helps in visualizing the number of different categories over time and their ranking. If you were to compare this chart to a stream graph, they’re actually visually not so far from each other. However, a bump area chart sorts the categories based on their ranking. So in other words, a bump area chart shows both magnitude and rank. And it’s also a stunning chart.

Alternative names: Line graph , Line plot

A line chart is a type of chart that comes in very handy when showing overall trends or progress. Line charts are among the oldest types of charts and are still one of the most popular. They are versatile, simple, and easy to understand. They can show a lot of information at once. What’s really nice about line charts is that they can be also very easily applied onto or merged with other charts like the bar chart or the area chart. In a line chart, the data points represent two variables and are connected by a line to show the changing trend of the data. The x-axis or independent axis shows a continuous variable (usually time) and the y-axis or dependent axis contains a numerical value for a metric of interest. If you’d like to design really stunning line charts, make sure to see our line chart resource page full of great tips and more line chart examples.

Spline chart

Alternative names: Spline graph , Curve chart

A spline chart is functionally the same thing as a line chart. The only difference is that a spline chart connects data points using a smooth curve, whereas a regular line chart uses a straight line to join those points. For this reason, a spline is also known as a curve chart. A combination of an area chart with a spline chart creates a variation called a spline area chart. Find the examples of similar charts on inspiration page .

Step line chart

Alternative names: Step chart , Stepped line graph

The step line chart only uses horizontal and vertical lines to connect the data points. It is convenient to use when you want to highlight the exact moment in time when the data changes and is, therefore, helpful when you must deal with data that changes in irregular intervals. See more examples of similar charts on the inspiration page .

Candlestick chart

Alternative name: Japanese candlestick chart

A candlestick chart is a chart typically used in the financial industry. It helps visualize the price movements over a period of time. For this reason, it helps detect and predict market trends. This type of chart is almost exclusively associated with stock price information. If you’re interested in designing a candlestick chart and adding it to your financial report, it’s possible to create it with Datylon for Illustrator. You can read more about creating a candlestick chart in our article .

Gantt chart

A Gantt chart is a graph that typically shows activities or tasks performed against time: a project plan over time. Used in project management, it helps in tracking project progress, schedule, changes, etc. In other words, a Gantt chart shows what has been done and what still needs to be done. However, it’s worth noting that although this type of graph is most commonly used in project management, it is definitely not limited to it. The idea behind this chart is that it visualizes the start and end time in form of period blocks. Therefore, it can be also used to illustrate seasonal occurrences, such as the availability of different fruits and vegetables throughout the year, or the appearance of mosquitoes in different months of the year.

Barcode chart

Barcode charts are used when one of the dimensions of the dataset is extensive while the space is limited. Barcode charts can be created in several ways. The first is to place a row of thin bars along the horizontal axis. It can be useful as an alternative to a strip plot when the density of data marks is too high and individual elements can be hardly recognized. The second way is to use the thickness of the bar for binding an additional dimension. The color is also often used to show a few states of the bar. In most cases the number of colors is limited due to bar width - it’s hard to recognize a wide range of colors when the bar is very thin.

The OHLC chart’s name stands for Open-High-Low-Close Chart. This type of chart is nearly exclusively used in the financial sector. It helps visualize price changes over time, typically in a trading stock market.

5. Distribution

Density plot.

Alternative names: Kernel density plot , Density trace graph

A density plot is a type of chart that helps us visualize how the numeric data is being distributed over a period of time. Density plots somewhat resemble smooth peaks and valleys plotted between two axes. These correspond to a higher or lower concentration of values. A density plot is a variation of a histogram. However, it is visually more appealing, as it loses the sharp edges typical for histograms and adds a smooth continuous curve. Find more examples on the inspiration page .

Ridgeline plot

Alternative names: Joy plot , Joyplot

A ridgeline plot is a somewhat special type of chart. A ridgeline plot shows the distribution of a numeric value for several groups of a category. It is done by illustrating partially overlapping line plots (that can be made of density plots or histograms), which then can resemble a mountain range. This beautiful chart can be useful to visualize distribution over time or space. But what is the most interesting about it is its history! The alternative name for a ridgeline plot is a joy plot because this very example above appeared on the first album cover of the British band Joy Division (‘Unknown Pleasures’ from 1979). See other examples of similar charts other examples of similar charts on the inspiration page .

Horizon chart

The horizon chart is for some an unfamiliar chart. Though, it is definitely worth getting to know this type of chart. When you are dealing with a lot of categories and you want to make efficient use of space, this chart is the way to go. It is perfect to show time series data on the horizontal axis and with colored bands, the values are represented on the vertical axis. The use of colored bands makes it possible to show great precision of the values. With the use of a diverging color scheme, it is even possible to show both positive and negative values. The difference with other charts is that both the positive and negative values are shown above the baseline, instead of showing negative values under the baseline. This allows you to show a lot of data in a very condensed manner.

Alternative names: Frequency distribution graph, Frequency distribution chart

A histogram is a type of chart that visually resembles a column chart. It’s a graph that consists of vertical rectangles (columns), whose length is proportional to the frequency of a variable (data items). The main visual difference between a histogram and a column chart is that there is no empty space between each rectangle. That’s because, unlike in column charts, in a histogram, the numbers are grouped into ranges. Then the columns have different heights because they correspond to the frequency of each group - meaning, how many items fall in a certain range.

Radial histogram

Alternative names: Angular histogram, Circular histogram, Polar histogram

A radial histogram is simply a variation of a histogram (see above) but with columns wrapped around a circle. It functions the same way as a regular histogram. And it’s very likely going to grab your readers’ attention. See examples of similar charts on the inspiration page .

Alternative names: Individual value plot, Single-axis scatter plot

A strip plot is a type of scatter plot but it only has one categorical and one numerical axis. It is a chart used to illustrate the distribution of many individual one-dimensional values. These values look like dots located along a single (category) axis in this chart. If some of the dots have the same value, they can overlap, creating something that looks like a strip.

Jitter plot

Alternative names: Jittered strip plot , Jittered individual value plot

A jitter plot is an alternative to a strip plot (see above). It is used to visualize the relationship between a measurement variable and a categorical variable. The main difference from a strip plot is that the dots used in the charts are shifted on the horizontal y-axis, to avoid overlapping (overplotting), which in turn allows avoiding lack of clarity.

One dimensional heatmap

If you want to zoom in on one category and focus on the evolution of that variable, you can use heatmaps in only one dimension. These charts are very popular in climate communication and often visualize temperatures.

Beeswarm chart

Alternative name: Swarm plot

A beeswarm chart is like a dot plot with a lot of values per category. These values are each represented by one dot, and the swarm of dots represents the distribution found in the data. Instead of packing them in bins, the dots are scattered around each other and plotted on one single axis. This kind of chart is very useful when you want to display a lot of data points at once.

Alternative names: Box plot , Boxplot , Box-and-whisker plot/chart , Whisker plot

A box chart uses boxes and lines to depict the distributions of one or more groups of numeric data. They are meant to provide a high level of information at glance - a summary of data. In a box plot, boxes are the main part of the chart, and they represent the range of the central 50% (middle portion) of the data. There is also a line visible within the boxes that indicates the median value. The remaining half of the data is visualized with the lines (whiskers) extending out of each box. This type of graph is quite popular in the research and financial fields. See similar chart examples here .

Violin plot

A box chart (above) can be useful for comparing summary statistics (such as range and quartiles), but it doesn't let you see variations in the data - unlike a violin plot. This type of chart is a hybrid of a box plot and a density plot. Thanks to this, a violin plot depicts distributions of numeric data for one or more groups using density curves. Of course, visually, it resembles a violin, hence its name. 

6. Geospatial & other charts

Geographic heatmap.

Alternative names: hot spot map , geo heat map , density heatmap

A geographic heatmap is a geographical representation of data that demonstrates where something occurs, specifying the areas of data’s high and low density. Unlike a choropleth map, a geo heatmap does not limit displaying geospatial data to specified boundaries. Therefore, using the data’s location radius, it can cover a small and specific geographic area, as well as large regions, such as oceans or coasts. It uses color to highlight the areas of occurrence.

Choropleth map

A choropleth map is a type of map in which different administrative areas are colored (or shaded) according to the magnitude of their numeric value. The main difference between a choropleth map and a geographic heatmap is that a choropleth map uses border-defined areas, such as countries, states, or neighborhoods. A common example of the use of choropleth maps can be a visualization of population density.

A tile map is a type of geographical map where a larger area (usually a country or a continent) is visualized by multiple equal-size and shape tiles, often square rectangles. Each tile represents a different region. A simple example of a tile map can be a collection of tiles forming the shape of the United States, where each tile corresponds to a state. What is important about tile maps is that all tiles don’t vary in size, meaning that larger regions can’t dominate the visualization and smaller regions are not harder to read.

Chord diagram

A chord diagram is used for showing the structure of paired connections between the instances of the same level. Every instance is represented by an arc. Every connection is shown as a band with various start and end widths which depicts differences in input and output. Common examples of chord diagrams vary from international trade flows to text and script analysis.

Arc diagram

An arc diagram in its essence is similar to a chord diagram. While the chord diagram focuses mostly on the quantitative aspect of the connection, the arc diagram is more focused on the existence of the link. The arc diagram shows the connections between points that are placed on the line axis with the arcs. Arcs could be placed on both sides of the axis showing the different aspects of the connection. Although the focus of the arc diagram is to show the existence of the connection it can also be used to show the quantitative aspect of the connection using the thickness of the arc.

A Sankey diagram is a type of visualization that allows you to display flows from one set of values to another. It shows entities that represent the values and connects them by links, or flows. Each flow has a varying height, which depends on its quantity. They can also differ in color. For this reason, it’s really common to use Sankey diagrams in visualizing supply chains, engineering and production processes, energy efficiency, etc. A known example is Google Analytics, which uses Sankey to depict the customer journey between pages of a website., The disadvantage of using this otherwise really beautiful graph is that inexperienced users will find it difficult to digest this visualization. Sankey diagrams are very often also called Alluvial diagrams. For an untrained eye, they will indeed appear to be the same chart. There is, however, a bit of a difference between the two. If you’re interested in learning more, we found this post quite a nice resource .

Network diagram

Alternative names: Network graph, Network mapping, Network visualization A network diagram is used to show the connections between multiple elements. The structure of the data and the purpose is somehow similar to the arc diagram. But while in the arc diagram, all of the points are placed on the same line, in the network diagram positioning of the peaks can vary. In some variations of the network diagram, the position of the point depends on the number of connections this point has and the group it belongs to. Network diagrams are often used to show the clusters of members based on the intensity of the connections.

A flowchart is a visualization of a workflow. It’s a diagram that depicts subsequent steps in the process. In other words, it shows what steps need to be followed to complete an action. A flowchart uses connecting lines and arrows to allow viewers to follow the process. It has many organizational use cases and can be a good tool to map out the customer journey, and step-by-step instructions. It’s also popular in project management.

Charts in Illustrator

As mentioned at the beginning, many of the charts and graphs listed in this post can be made with Datylon. Currently, we offer 130+ chart templates in our Chart Library. You can sign up for free and try it for yourself.

What is even more interesting, a lot of charts from this list can be designed in Adobe ® Illustrator ® . Of course, Illustrator has a built-in graphing tool but unfortunately for many graphic designers and data visualization experts, it is seriously limited .  Check out the walk-through for our graph maker by "Yes I'm a Designer".

With Datylon for Illustrator , you get full freedom of chart design. It's a chart maker plug-in for Adobe Illustrator with extraordinary features that will help you make the most captivating chart design! Hey, did anyone say fully resizable charts?

➡️ Create an account and don't forget to download Datylon for Illustrator with a free 14-day trial (no credit card needed) and supercharge your data visualization!

Kosma Hess - Marketing Manager

Global citizen, world traveler, content creator, marketing specialist, can't sing to save his life. In his free time, he's mastering Datylon for Illustrator for no reason.

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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How to choose the best chart or graph for your data

Content Strategist, Customer Education at Looker

The quality of our lives is determined by the choices we make. Sometimes the choices are simple, like which flavor of ice cream to indulge in. Let’s say the choices are vanilla ice cream, chocolate ice cream, and strawberry sorbet. Sweetness is great, but on a hot day, vanilla and chocolate lack that light and fruity feeling I want.

Here’s how I analyzed my ice cream choice:

Question : Which flavor of ice cream should I get?

Goal : I want the flavor to complement this hot summer day, and to me, that means something light and fruity.

Outcome : Strawberry sorbet

Yum. But what about the more serious choices, like deciding how much income to squirrel away for retirement?

The same process still works:

Question : How much of my income should I save?

Goal : I want to enjoy a good life post-retirement. Research suggests that means saving at least 20%.

Outcome : I’ll save at least 20%.

Each choice can be broken down with the framework of “question, goal, outcome.” In this framework, you have a question and a goal that you’re trying to achieve. Your goal motivates how you choose between options to get to the outcome.

Choosing the best chart or graph for your data is similar, in that the outcome depends on your goal. You can even use the same “question, goal, outcome” framework. I’ll provide some examples of choosing a chart with this framework further on.

For now, let’s focus on the “goal” part of the framework as it relates to displaying data. We see most visualizations as fulfilling one of four main objectives:

• Showing how values compare to each other
• Showing how the data is distributed
• Showing how the data is composed
• Showing how values relate to one another

The challenge of choosing the right visualization lies in finding the goal beneath your data question. Once you identify the goal, choosing the right chart becomes easier, particularly when you have a reference like the one below.

Types of Charts & Graphs

This points you in the right direction, but there are multiple charts in each category. How’s an analyst to choose? Familiarizing yourself with the nuances of each graph will help.

How to choose the best chart for your data

What’s the best chart to show comparison.

Comparison questions ask how different values or attributes within the data compare to each other.

Tables help you compare exact values to one another. Column and bar charts showcase comparisons across different categories, while line charts excel at showing trends over time.

What’s the best chart to show distribution?

Distribution questions seek to understand how individual data points are distributed within the broader data set.

Box plots show distribution based on a statistical summary, while column histograms are great for finding the frequency of an occurrence. Scatter plots are best for showing distribution in large data sets.

What’s the best chart to show composition?

Composition questions ask what general features are present in the data set.

Donut and pie charts are great choices to show composition when simple proportions are useful. Area charts put the composition of data within the context of trends over time. Stacked bar, percent, and column charts show an overview of the data’s composition.

What’s the best chart to show a relationship between values?

Questions in this category ask how values and attributes relate to each other.

Bubble charts and heat maps can help you quickly identify relationships between data points.

Identifying the goal beneath the question

Now you have references to help you choose between chart types. These resources are most powerful when you understand what motivates those choices in the first place.

What’s your data question really asking for? Is the answer to compare data, look at its distribution, examine its composition, or show a particular relationship between data points?

You can recognize that goal using our “question, goal, outcome” framework from the top of the post.

For the sake of putting this framework into action, I’ll don the hat of a marketing analyst creating visualizations for my colleagues. The morning is dedicated to coffee and acquisition metrics, essentially answering questions like: “Are we gaining enough customers? Where are they coming from?”

Question : How many new users are we acquiring every day?

Goal : Compare values (number of users) over time (days)

Outcome : A line chart

A dip in traffic on the weekends is expected and indeed appears in the data. Now I want to dive into this traffic further and see where these users are coming from.

Question : What channels are these new users coming from?

Goal : Display the composition of the data (which channel source users came from) over time (still comparing the number of new users across days).

Outcome : An area chart

Looks like the organic search category currently accounts for the largest amount of users—meaning that we're doing well in search engine rankings, thanks to our efforts with search engine optimization. We’re getting a lot of traffic from referrers, too—I want to look at that more closely.

Question : Which referrers are driving the most traffic to our website?

Goal : Compare values (number of sessions) across categories (referrers).

Outcome : A bar chart

The partner website drives the most traffic by a long shot, followed by a stellar blog post. But how does the traffic break down between desktop and mobile devices?

Question : Which referrers tend to drive more traffic to our website from desktops, and which ones tend to drive more traffic from mobile devices?

Goal : Comparing values (number of sessions) across categories (referrers) and looking at composition within each bar (mobile vs. web traffic).

Outcome : A stacked bar chart

Question : How does the traffic from mobile and desktop stack up across referrers?

Goal : Comparing values (number of sessions) across categories (referrers) in multiple dimensions (mobile and desktop).

Outcome : A grouped bar chart

That should be enough to get the team started on the acquisition metrics.

Another area of marketing that generates interesting questions is behavior. Knowing what people are up to will help determine the best times and places to reach out to them.

Question : What time of day sees the highest number of users on our website?

Goal : Comparing values (number of sessions) over time (hours) across multiple dimensions (days).

Outcome : Overlay line chart

Looks like noon to four o’clock are our most popular times by far. Which pages are our visitors looking at in the afternoon?

Question : Which landing pages are driving the most engagement by channel?

Goal : Look at the relationship between channels and landing pages to see how the different combinations influence average session duration.

Outcome : Heat map

Interesting— sessions coming from paid searches and direct traffic show the most engagement.

Of course, after understanding visitor behavior better, a marketer’s eye naturally turns toward conversion. How do we get our visitors to become customers?

Questions in the conversion realm can be broken down like this:

Question : Where do we have opportunities to drive more traffic to high-performing web pages?

Goal : Show the relationship between values (conversion rates and number of sessions) to help pinpoint pages with high conversion rates that could be better promoted.

Outcome : Scatterplot

In this example, the page represented by the upper-left corner dot represents an opportunity to promote a page. This page has an extremely high conversion rate compared to the rest but barely sees any traffic.

This framework works for data across all industries, not just marketing.

Try it out for yourself. Take the next data question you encounter and write it out, identify what the goal behind the question is, and use the guidance above to choose the best chart.

Which chart you use impacts how people understand your data and what decisions they make based on that understanding. Happily, you now have some great tools in your pocket to help guide your choices.

Choosing the right chart is one part of the bigger story of communicating with data—but the colors you choose and the way you construct a dashboard also matter. To that end, keep an eye out for the next installment in our data visualization series on designing dashboards for UX/UI.

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Presentation ideas • Tips and Tricks

15 Creative Ways to Use Charts and Graphs in Presentations

Emily Bryce

12 December 2022

In today’s data-driven world, presentations are no longer just about presenting ideas and concepts, but also about presenting data in an engaging and easy-to-understand manner. This is where charts and graphs come in. They help to visualize data, making it easier for the audience to grasp and retain information. In this blog post, we will explore creative ways to use charts and graphs in presentations.

1. Use charts and graphs to compare data

One of the most common uses of charts and graphs is to compare data. Whether you are comparing sales figures, market trends or customer feedback, charts and graphs can help you present the information in a visually compelling way. Use bar charts, line graphs, pie charts, and scatter plots to showcase the data in a way that makes it easy to understand and compare.

2. Use charts and graphs to show trends

Another way to use charts and graphs in presentations is to show trends over time. For example, if you are presenting the growth of your business over the last five years, use a line graph to show the upward trend. If you want to show the fluctuations in your business over a period of time, use a scatter plot to highlight the highs and lows.

3. Use charts and graphs to show relationships

Charts and graphs can also be used to show the relationship between different sets of data. For example, if you are presenting the correlation between customer satisfaction and sales, use a scatter plot to show the relationship between the two variables. You can also use bubble charts to show the relationship between three different variables.

4. Use charts and graphs to show distribution

If you are presenting data that is distributed across a range, such as the ages of your customers, use a histogram to show the distribution. Histograms are great for showing the frequency distribution of data, and they can help you identify patterns and trends in the data.

5. Use charts and graphs to show proportions

Pie charts are a great way to show proportions. Use pie charts to show the proportion of sales for different products or the proportion of the budget allocated to different departments. Make sure to keep the number of categories to a minimum to ensure that the chart is easy to read.

6. Use creative chart and graph designs

Charts and graphs don’t have to be boring. Use creative designs and colors to make your charts and graphs stand out. For example, you can use a bar chart with a gradient background to make it more visually appealing. You can also use icons and images to make your charts and graphs more engaging.

7. Use charts and graphs to tell a story

Finally, use charts and graphs to tell a story. Don’t just present the data, but use it to support your message. Use a combination of charts and graphs to create a narrative that engages your audience and leaves them with a clear understanding of your message.

In conclusion, charts and graphs are a powerful tool for presenting data in an engaging and easy-to-understand manner. Use them creatively to showcase data, tell a story, and leave a lasting impression on your audience. With the right use of charts and graphs, you can take your presentations to the next level.

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Use charts and graphs in your presentation

You can make a chart in PowerPoint or Excel. If you have lots of data to chart, create your chart in Excel , and then copy it into your presentation . This is also the best way if your data changes regularly and you want your chart to always reflect the latest numbers. In that case, when you copy and paste the chart, keep it linked to the original Excel file .

To create a simple chart from scratch in PowerPoint, click Insert > Chart and pick the chart you want.

Click Insert > Chart .

Click the chart type and then double-click the chart you want.

Tip:  For help deciding which chart is best for your data, see Available chart types .

In the worksheet that appears, replace the placeholder data with your own information.

When you’ve finished, close the worksheet.

Create an org chart in PowerPoint

Create charts in Excel

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Discover 20 Essential Types Of Graphs And Charts And When To Use Them

Table of Contents

1) What Are Graphs And Charts?

2) Charts And Graphs Categories

3) 20 Different Types Of Graphs And Charts

4) How To Choose The Right Chart Type

Data and statistics are all around us. It is very likely that you have found yourself looking at a chart or graph at work, in the news, sports, media, advertising, and many other places at some point in your life. That is because graphical representations of data make it easier to convey important information to different audiences. That said, there is still a lack of charting literacy due to the wide range of visuals available to us and the misuse of statistics . In many cases, even the chart designers are not picking the right visuals to convey the information in the correct way. 

So, how do you make sure you are using and understanding graphs and charts in the right way? In this post, we will provide you with the necessary knowledge to recognize the top 20 most commonly used types of charts and graphs and give insights into when and how to use them correctly. Each type of chart will have a visual example generated with datapine’s professional dashboard software . 

This knowledge will be valuable if you are a data visualization designer, a business user looking to incorporate visual analytics into his/her work, or just an average viewer looking to learn more about the topic. Let’s start this journey by looking at a definition. 

What Are Graphs And Charts?

A graph or chart is a graphical representation of qualitative or quantitative data. It uses different symbols such as bars, lines, columns, tables, box plots, maps, and more, to give meaning to the information, making it easier to understand than raw data. 

As you probably already know, multiple kinds of graphs and charts are widely used in various fields and industries such as business decision-making or research studies. These visual tools are used to find relationships between different data sets and extract valuable conclusions from them. In some cases, they are built by hand, and in others, most commonly, they are built using visualization tools. 

That said, the type of chart or graph you use will vary depending on the aim of the analysis. For instance, percentages are better viewed through a pie or bar chart while data that is changing over time is better viewed over a line chart. For that reason, it is important to have a clear understanding of the different chart types to make sure you are using the right one. 

Below we will discuss the graph and chart categories. These categories will build a solid foundation that will help you pick the right visual for your analytical aims. Let’s dive into them. 

Charts And Graphs Categories

As mentioned, asking the right questions will form the foundations for choosing the right types of visualization graphs for your project, strategy, or business goals. The fundamental categories that differentiate these questions are based on:

• Relationship : Understanding connections between different data points can significantly help discover new relevant insights. For instance, in the medical field, analyzing relationships between diseases and gene interactions can help discover a treatment for a particular disease. When it comes to visuals, a few graphics can help you easily identify and represent relationships. Scatter plots are valuable when you want to represent smaller data sets of two variables while bubble graphs are best for larger information with three or more variables. 
• Distribution: In statistics, distribution refers to the possibility of the occurrence of an outcome. To understand this, scientists and analysts use charts to represent the frequency distribution of the data and extract conclusions from it. For this purpose, they use line charts to analyze trends, scatter plots to highlight similarities across variables, and histograms to see the frequency distribution of a single variable across categories. 
• Composition : The purpose of business graphs and charts for composition is to compare parts to a whole in absolute numbers and normalized forms, usually a percentage. It is one of the most common and traditionally used visualization categories and it is usually limited by the simplicity of the chart types. Common composition graphs include pies, tree maps, and stacked bar charts. 
• Comparison: As its name suggests, this category refers to the comparison of multiple variables or multiple categories within a single variable. When comparing information it is fundamental to pick a chart that will make it easier to understand the differences. These differences can be within multiple elements, for example, top-selling products, or over time, such as the development of sales for different products over a year. For this purpose, tables, spiders, lines, columns, or area graphs are always a good choice.

To get a clearer impression, here is a visual overview of which chart to select based on what kind of data you need to show:

**click to enlarge**

Discover 20 Different Types Of Graphs And Charts

Now that you understand the key charting categories you are ready to dive into the main types of graphs and when to use them. Here, we will focus on the 20 most common types of visuals to represent your data in the most meaningful way possible. Each chart type has a visual example generated with datapine .

1) Number Chart

When to use 

A real-time number chart is essentially a ticker that will give you an immediate overview of a particular KPI . At a glance, you can see any total such as sales, percentage of evolution, number of visitors, etc. This is probably the easiest visualization type to build with the only consideration being the period you want to track. Do you want to show an entire history or simply the latest quarter? It is crucial to label the period clearly so your audience understands what story you are telling. Adding a trend indicator compares your number to the previous period (or to a fixed goal, depending on what you are tracking).

Other considerations

Number charts are often the first thing people see and are the quickest to read, so if there are too many, your narrative can get diluted. Using too many can also make your dashboard a little superficial. If you want more in-depth information, limit the number of number graphs and leave room for other types of data visualization that drill down a little deeper.

When you add a trend indicator, we suggest you compare numbers from the same period. For example, if you are tracking total sales for the current quarter, compare that data to the same quarter last year (or last period – depending on your story). If you select a target manually (perhaps you have no accurate past data), be sure to set realistic goals to be able to get on top of your KPI management practice. Again, remember to label the trend indicator clearly so your audience knows exactly what they are looking at.

2) Line Chart

The purpose of a line chart is to show trends, accelerations (or decelerations), and volatility. They display relationships in how data changes over a period of time. In our example above, we are showing Revenue by Payment Method for all of 2019 . Right away, you can see that the credit card payments were the highest and that everything took a dip in September. The takeaways are quick to register yet have depth.

Too many lines (variables) can make your chart complicated and hard to decipher. You may also find your audience constantly referencing the legend to remind them which one they are looking at. If you have too many variables, it’s time to consider a second (or even third) chart to tell this story.

When it comes to layout, keep your numbers relevant. When you set up your axis scale, keep it close to the highest data point. For example, if we had set the y-axis above to track all the way to 200K (when our highest data point is just over 90K), our chart would have been squished and hard to read. The top half would have been wasted space, and the data crammed. Let your data breathe a little!

One more thing!

A great feature of line graphs is that you can combine them with other types of visualizations, such as bar graphs. Using a double y-axis, one for the bar graph and one for the line, allows you to show two elements of your story in one graph. The primary y-axis below shows orders (bar graph), and the secondary y-axis is sales totals (line). The metrics are different and useful independently, but together, they tell a compelling story.

Maps are great at visualizing your geographic data by location. The information on a map is often displayed in a colored area map (like above) or a bubble map. Because maps are so effective at telling a story, they are used by governments, media, NGOs, nonprofits, public health departments – the list goes on. Maps aren’t just for displaying data; they also direct action. This was seen most recently through the Zika outbreak. Mapping the spread of the disease has helped health officials track it and effectively distribute resources where they are most needed.

Even if you aren’t saving the world from Zika, maps can help! For example, they are great at comparing your organization’s sales in different regions.

Everyone loves maps. However, that doesn’t mean you always need to display one. If the location isn’t a necessary part of your analytics story, you don’t need a map. They take up a lot of room, so only use them when necessary. Also, don’t just fill your maps with data points. Clickhole did a good job of satirizing this common data visualization type by placing 700 red dots on a map. Filling your map with data points doesn’t tell a compelling story; it just overwhelms the audience.

4) Waterfall Chart

This extremely useful chart depicts the power of visualizing data in a static, yet informative manner. It shows the composition of data over a set time period, illustrating the positive or negative values that help in understanding the overall cumulative effect. The decrements and increments can cause the cumulative to fall below or above the axis at various points, causing a clear overview of how the initial value is affected. It is often used in financial departments for analytical purposes, usually depicting the changes in revenue or profit. For example, your MRR ( monthly recurring revenue ), new revenue, upsell, lost, and current revenue. In our example above, we can conclude that our current revenue increased in our set time period.

Waterfall charts are static in their presentation so if you need to show dynamic data sets, then stacked graphs would be a better choice. Also, showing the relationship between selected multiple variables is not optimal for waterfall charts (also known as Cascade charts), as bubble plots or scatter plots would be a more effective solution.

5) Bar Graphs

There are three key types of bar graphs that we will cover in this section: Horizontal, Grouped and Stacked. Although all are in the same chart family, each serves a distinct purpose. Let’s discuss each of them in detail below. 

a) Horizontal Bar Graphs

Horizontal charts are perfect for comparative ranking, like a top-five list. They are also useful if your data labels are really long or complex. Keep them in an order that makes sense, though. Either list by value (like we did above) or, if that’s not the strength, choose a logic for the labels that makes sense, like listing them alphabetically.

Because time is best expressed left to right, it’s better to leave showing an evolution for the column chart. Also, like many charts, when you have too many values, a horizontal bar graph quickly becomes cluttered.

b) Grouped bar graph

When to Use 

Grouped bar charts follow the same logic as horizontal bars, except that they show values for two variables instead of one. The two variables are often displayed in disparate colors to help differentiate them from each other. It is recommended to use this chart type when you want to compare elements within a specific category or across other categories on the chart. For instance, in our example generated with a customer service analytics tool, we can see customer service tickets by channel divided between the total and solved ones. In this case, the grouped chart can help compare the values between the total and unsolved tickets as well as compare the number of solved tickets across channels and extract conclusions.  

Just like with the horizontal one, you need to be careful not to add too many categories into this graph type as they can make it look cluttered. The chart becomes difficult to read with the increase in categories, therefore, it is not the best when it comes to relationship or distribution analysis. 

c) Stacked bar chart

When to Use

A stacked bar chart is a variation of the traditional bar graph but, instead of dealing with one categorical variable, it deals with two or more. Each bar is divided into multiple subcategories that are compared with each other usually using a percentage. In the example above, the chart is comparing management and non-management positions (first categorical variable) that are being occupied by female and diverse employees vs male employees (second categorical variable). This allows the users of the bar to not only focus on the comparisons between the bars themselves but also extract conclusions based on the subcategories from each individual bar. 

When building a successful stacked bar graph it is important to carefully decide which of the two categorical variables will be the primary one. The primary one is the most important one and it will define the overall bar lengths. The secondary one will define the subcategories. Usually, if you are dealing with time ranges or numerical values, these make the best primary variables. However, it will vary from case to case. 

6) Column Graphs

When to use

Column charts are the standard for showing chronological data, such as growth over specific periods, and for comparing data across categories  (you can see this in the example where the accounts payable turnover is being compared based on date ranges). In general, for these kinds of charts, the categories are typically displayed on the horizontal axis while the numerical values are displayed vertically using rectangular columns. The size of the columns is proportional to the values displayed on the chart and their height allows people to easily extract conclusions at a glance. Unlike the bar chart which can display larger or more complex datasets, the column chart has a size limitation making it best to display smaller data. This makes it the go-to visualization for anyone looking for an easy and understandable way to display their information. 

Aside from the obvious design mistakes like using too many colors or too many categories, other things you want to make sure of are: if there is no natural order for the data (e.g. age categories or time ranges), it is recommended to order the values from higher to lowest or lowest to highest. Additionally, the y-axis should always start at 0, otherwise, the height of the columns can become misleading.  

c) Grouped column chart 

Just like the grouped bar chart, the grouped column chart compares two categorical variables instead of one using vertical columns instead of horizontal bars. The purpose of this graph is to see how the subcategories from the secondary variable change within each subcategory of the primary variable. Comparisons can be done within-group or between groups depending on the aim of the analysis.  In our sales data analysis example, Amount of Sales per Channel and Country (last year) , it is clear that we are comparing six regions and five channels. The color coding keeps the audience clued into which region we are referencing, and the proper spacing shows the channels (good design is at the heart of it all!). At a glance, you can see that SEM was the highest-earning channel, and with a little effort, the Netherlands stands out as the region that likely enjoyed the highest sales.

An important consideration when it comes to this graphic is to not use it to compare totals within the different levels of the categorical values. For this purpose, it is better to use a stacked column chart which we will discuss below.  

b) Stacked Column Chart

Stacked charts handle part-to-whole relationships. This is when you are comparing data to itself rather than seeing a total – often in the form of percentages. In the example above, the story isn’t about the total number of customers aged 15-25, but that 22% of the customers were 15-25 in the first quarter of 2014 (and 26% in Q4). The numbers we are working with are relative only to our total.

When showing single part-to-whole relationships, pie charts are the simplest way to go. Twenty-two percent of our customers are 15-25, leaving the other 78% to fit into the pie somehow. People get pie charts. They’re easy. But what if we want to show the same information over different periods? This would be a multiple part-to-whole relationship, and for this, we use a stacked bar graph. Again, we are telling the story of the percentage of customers in a certain age range, per quarter . The total number of each isn’t relevant here (although that information is used in the calculations). With proper spacing, we see each quarter clearly, and the color coding shows that overall, 46-55-year-olds are the most difficult customers to attract.

Aesthetically speaking, when you have too much data, columns become very thin and ugly. This also leaves little room to properly label your chart. Imagine we had 10 different age ranges per column. Some results, if not most, would be only slivers. To make your chart easy to understand, use good colors, proper spacing, and a balanced layout. This invites people to look at your chart and even enjoy it. A pretty chart is a much nicer way to consume data than squinting at a table.

7) Pie Charts

The much-maligned pie chart has had a bad couple of years. In fact, it has become pretty cliché to talk about how bad pie charts are. We understand the pie chart doesn’t do a lot, but it does do some things quite well. Pie charts are useful when demonstrating the proportional composition of a particular variable over a static timeframe. Let’s look at some particular cases:

• When the parts add up to 100%: The “part-to-whole relationship” is built right into it a pie chart in an obvious way. At a glance, any user knows a pie chart is splitting a population into parts and that the total of those parts equals 100%.
• When approximating is okay: The pie chart is particularly effective when eyeballing values are enough to get the conversation going. Also, it’s easier to estimate the percentage value of a pie chart compared to, let’s say, a bar chart. That’s because pies have an invisible scale with 25%, 50%, 75%, and 100% built-in at four points of the circle. Our eyes can easily decipher these proportions, driving the conversation about what variables do and don’t take up most of the pie. Your audience doesn’t have to guess the proportions – you can easily add data labels or build the sister of the pie chart, the donut chart, to display additional information.
• When there aren’t many proportions to the variable or they are combined: Pie charts are great when answering questions like, “What two largest suppliers control 65% of the market?”

Your audience isn’t always going to be comprised of data scientists. Accordingly, your presentation should be tailored to your particular audience. This brings us to another pie chart strength: people are familiar with pie charts. Any audience member will feel comfortable interpreting what the pie chart is presenting. As a bonus, circles generate more positive emotions: our brains like to look at circles over sharp corners. In the end, a pie chart simplifies the data story and encourages the audience.

Data visualization guru Edward Tufte famously declared that “pie charts are bad, and the only thing worse than one pie chart is lots of them.” We already talked about the pros of pie charts and why we don’t adhere to this strict no-pie-chart philosophy. We should also state that there are plenty of instances where you should not use a pie chart. First off, pie charts portray a stagnate time frame, so trending data is off the table with this visualization method. Make sure your audience understands the timeframe portrayed and try to document or label this applied filter somewhere.

Pie charts are also not the best types of data charts to make precise comparisons. This is especially true when there are multiple small pieces to the pie. If you need to see that one slice is 1% larger than another, it’s better to go with a bar chart. Another thing about multiple pieces to your pie – you don’t want too many. Pie charts are most effective when just displaying two portions. They lose presentation value after six segments. After six, it is hard for the eyes to decipher the slice's proportion. It also becomes difficult to label the pie chart, and valuable online dashboard /reporting real estate is often wasted in the process.

This brings us to the last issue: circles take up space. If you are using multiple pie charts in a dashboard, it is probably best to combine the data in one chart. We recommend checking out the stacked bar chart for these cases. You can also have a look at the different pie charts that are commonly used and explore the disadvantages of pie charts .

8) Gauge Charts

Gauge charts , also known as dial charts or speedometer charts, use needles and colors to show data similar to reading on a dial/speedometer, and they provide an easily digested visual. They are great for displaying a single value/measure within a quantitative context, such as to the previous period or to a target value. The gauge chart is often used in executive dashboards and reports to display progress against key business indicators. All you need to do is assign minimum and maximum values and define a color range, and the gauge chart will display an immediate trend indication.

Gauge charts are great for KPIs and single data points. After that, they can get a bit messy. With only one data point, you can’t easily compare different variables. You also can’t trend data using gauge charts. All of this makes taking actionable insight from a gauge chart difficult. Furthermore, they take up a lot of space – if your live dashboard has precious real estate, it may not be most efficient to fill it with multiple gauge charts. Using one chart to summarize multiple KPIs, you will likely get more bang for your buck.

9) Scatter Plot

Scatter plot is not only fun to say – it’s what you need when looking for the correlation in a large data set. The data sets need to be in pairs with a dependent variable and an independent variable. The dependent (the one the other relies on) becomes the y-axis, and the independent – the x-axis. When the data is distributed on the plot, the results show the correlation to be positive, negative (each to varying degrees), or nonexistent. Adding a trend line will help show the correlation and how statistically significant it is.

Scatter plots only work when you have a lot of data points and a correlation. If you are only talking about a few pieces of information, a scatter plot will be empty and pointless. The value comes through only when there are enough data points to see clear results. If you only have a little data or if your scatter plot shows no correlation at all, this chart has no place on your business dashboard. 

10) Spider Chart

Spider charts, or radar charts, are comparative charts used when multivariate data is displayed with three or more quantitative variables (aspects). This is useful when you want to evaluate two or more “things” using more than three aspects, all of which are similarly quantifiable. It’s certainly a mouthful, but it’s simple when you put it into use. Spider charts are great for rankings, appraisals, and reviews. For example, the three “things” we are comparing in our e-commerce example above are regions: Australia, Europe, and North America. The aspects we are comparing against are products sold are Cameras, TVs, Cell Phones, Games, and Computers. Each variable is being compared by how many units were sold – between 0 and 500. Europe is clearly outselling in all areas, and Australia is particularly weak in Cameras and Cell Phones. The concentration of strengths and weaknesses is evident at a glance.

This is not the easiest data analysis chart to pull off, but it really impresses when done correctly. Using this chart if you have more than five values in your dimension (five “things” to evaluate) makes it hard to read, which can make it pointless altogether. Whether you use solid lines or shaded areas, too many layers are difficult to interpret. Naturally, it is not a choice when you want to show time (the whole circular thing...).

We know – tables aren’t technically a graph. But sometimes, you really just need a table to portray your data in its raw format. With a table, you can display a large number of precise measures and dimensions. You can easily look up or compare individual values while also displaying grand totals. This is particularly beneficial when your audience needs to know the underlying data or get into the “weeds.” Tables are also effective if you have a diverse audience where each person wants to look at their own piece of the table. They are also great at portraying a lot of text or string values.

Remember – just because you are using a table doesn’t mean it can’t be visually pleasing. You can use various colors, border styles, font types, number formats, and icons to highlight and present your data effectively.

There are many reasons to use a table, but there are also many instances where different types of charts are a better choice. It all comes down to our eyes and brain. Tables interact primarily with the verbal system – we read tables. This reading includes processing the displayed information in a sequential fashion. Users read down columns or across rows of numbers, comparing one number to another. The keywords here are reading, processing, and time. Tables take longer to digest.

Graphs, on the other hand, are perceived by our visual system. They give numbers shape and form and tell a data story. They can present an immense amount of data quickly and in an easy-to-consume fashion. If data visualization is needed to identify patterns and relationships, a table is not the best choice. Also, while it is fun to get creative with colors, formatting, and icons, make sure your formatting and presentation choices are increasing your perception. The tables are hard enough to read as is!

12) Area Charts

The area chart is closely related to the line chart. Both chart types depict a time-series relationship, show continuity across a dataset, and are good for seeing trends rather than individual values. That said, there are some key differences between the two. Because of these differences, “when to use area charts” does not equal “when to use line charts.”

Line charts connect discrete but continuous data points through straight line segments. This makes them effective for facilitating trend analyses. Area charts technically do the same, except that the area below the plotted lines is filled with color. In this case, an un-stacked area chart is the same thing as a line chart – just with more coloring. The problem you run into here is occlusion: when you start comparing multiple variables/categories in an unstacked area chart, the upper layers obscure the lower layers. You can play around with transparency, but after three variables, un-stacked area charts are hard to read.

This brings us to the most commonly used area chart: the stacked area chart. Like stacked bar charts, stacked area charts portray a part-to-whole relationship. The total vertical of a stacked area chart shows the whole, while the height of each different dataset shows the parts. For example, a stacked area chart can show the sales trends for each region and the total sales trend. There are two different stacked area chart types you can use to portray the part-to-whole relationship.

Traditional Stacked Area Chart: The raw values are stacked, showing how the whole changes over time.

Stacked Percentage Area Chart: Percentages are stacked to show how the relationship between the different parts changes over time. This is best used to show the distribution of categories as parts of a whole where the cumulative total is less important.

As we hinted earlier, for the most part, you should stay away from un-stacked area charts. If you are just comparing 2-3 different variables that don’t obscure each other, then go ahead. But in general, they are often messy and don’t follow data visualization and dashboard design best practices . When it comes to stacked area charts, don’t use them when you don’t need to portray a part-to-whole relationship – use a line graph instead. Also, if you are trying to compare 7+ series, a stacked area graph becomes hard to read. In this case, you should once again turn to the line graph.

13) Bubble Plots

Bubble charts, or bubble graphs, are among the best types of data graphs for comparing several values or sets of data at a glance. If you’re looking to show the relationship between different product categories, revenue streams, investment risks, costs, or anything similar, bubble charts or plots are incredibly effective.

For instance, our example bubble plot showcases the relationship between a mix of retail product categories, primarily the number of orders and profit margin.

Here, you can tell that the TV & Home Theater product category has the highest number of orders (around 3,000 as you can see from the number scale on the left) as well as the highest profit margin, and therefore, it is the biggest bubble on the chart. Comparatively, the camera category shows the lowest number of orders in addition to the smallest profit margin and naturally is the smallest bubble on the chart.

The bubble plot is extremely powerful for visualizing two or more variables with multiple dimensions. And here, the bigger the bubble, the higher the profit margin. Not only are bubble plots visually stimulating, but they are also incredibly effective when building a comparative narrative for a specific audience.

It's difficult to go too far wrong with bubble charts, but the most common mistake with these types of business charts is focusing on varying the “radius” of the values rather than the “area” they take up on the chart. Doing so sometimes makes the bubbles on the plot disproportionate to the graph, making the information misleading at a glance. In short, your bubbles should be accurate in terms of size compared to the values. Get this right, and you’ll get the results you deserve. 

14) Boxplot 

Just like the histogram, the box plot is a graph that is used to represent the distribution of numeric data using boxes and lines. Each box is composed of five elements also known as the “Five-number summary” which are the minimum, first quartile, median (second quartile), third quartile, and maximum. Each of these elements represents a value and how it is distributed within the data set. Anything outside these values would be considered an outlier. An outlier is any value that is extremely high or extremely low compared to the nearest data point. Outliers (which are usually plotted as dots in the chart) need to be identified because they can affect the end result of the analysis and box plots are the best visuals to do so.  

Just like other types of charts on this list, box plots are not the best choice when it comes to big data sets. Their visual simplicity makes it hard to see details about the distribution results which makes it more difficult to interpret, especially when dealing with complex data. Plus, this chart works at its best when comparing different groups (as seen in our example above). So, if you are trying to look at the distribution of one single group a histogram is a better choice. 

15) Funnel chart 

As its name suggests, a funnel chart is a visualization type used to show how data moves or flows through a specific process. They are commonly utilized to display sales, recruitment, or order fulfillment funnels where the values are often decreasing as the funnel becomes smaller. This can be seen in the example above in which the number of potential clients decreases at each stage of the sales funnel. This is a natural progression that happens because not every person that shows interest in the opportunities stage will end up buying the product or subscribing to the service. 

In some cases, the sizes of the sections of the funnel chart are plotted proportionately with the value they are representing. This means the top section is 100% and the rest will represent their corresponding percentage with their size. This is not the case with our example in which the sections are sized to match the funnel shape, not the values contained in each section. 

Funnel graphics are very specific visuals that can only be used in particular cases. You should only use it if your data goes through a sequence of stages and the values are decreasing with each stage. Plus, they are only useful to represent a single variable which means they cannot be used to visualize relationships between variables. A good alternative for a funnel graph is a bar or column chart. 

16) Bullet chart 

A bullet chart is a variation of a bar or column chart but it provides some extra visual elements to give more context to the data. It is usually used for performance tracking to make comparisons against a goal or other relevant values and it is composed of three key elements. A single measure is represented by a darker shade bar with a length that represents the performance of that value, qualitative ranges are represented by lighter shades in the background, and a target or comparative measure which is represented with a small line that is perpendicular to the orientation of the graph. Bullet charts are great alternatives to gauge charts, especially when you are working with a KPI dashboard and don’t want to take up too much space from it. 

It is important to note that bullet charts are complex visuals that might be challenging to understand for non-technical audiences. In some cases, some people might choose to remove the shaded background to focus only on the actual value against the target or remove the target and focus on the qualitative ranges to make the chart friendlier to analyze.    This variation is also known as an overlapping bullet chart and it can be done using columns and bars, as we will see in our two examples below.

1. Overlapping bars bullet chart 

As we saw with different graph types previously, the bullet chart can be vertical (using columns) or horizontal (using bars). It is recommended to use bars when you want to display more categories or longer category names to avoid making the visual cluttered. In the example above, we can see the number of orders by product of the current year compared to a target. In this case, due to the number of products, a bar bullet graph is the best choice as it contains a lot of information without affecting the readability of the data.

2. Overlapping columns bullet chart 

On the other side, a bullet column chart is a better choice when you want to organize categories from left to right or when you have fewer categories to show. In this case, we can see the number of orders by product category. Given that product categories are fewer than the actual number of products, it is a good choice to pick columns to represent this data. In a traditional bullet chart, the number of orders by a quarter could be added for additional context as qualitative measures.

17) Treemap chart 

A treemap is a chart type used to display hierarchical data through rectangles that decrease their size as the value decreases, this process is also referred to as nesting. It is used to display large amounts of raw data in a visually appealing way that allows users to easily extract valuable conclusions at a glance. Its name comes from the shape of a tree, as the chart can be divided into multiple categories with different “branches” and “sub-branches”. Each of these categories should have a different color and the dimensions of the rectangles are based on the size of the data being displayed. 

Given that a treemap is used to visualize massive amounts of raw data, they can display an infinite amount of subcategories (or sub-branches) which can make them harder to understand. However, in most cases, users can drill down into the different categories to dig deeper into the data and answer different questions that might arise. 

If you are not trying to show hierarchical data then you should stay away from treemaps. Just like it happens with pie charts, this visualization is simply showing parts-to-whole relationships, therefore, it becomes useless for other purposes. You should also avoid treemaps if the data being displayed is too close in size. This defeats the purpose of the graph which is to easily identify the largest item from a specific category. A few alternatives for treemaps include column charts and scatter plots.

18) Stream charts 

A stream graph is considered a variation of the area chart with the difference that, while the area chart is plotted with a fixed x-axis, the stream graph has values displayed around a central axis. Hence, the flowing river-like shape. They are frequently used to identify trends and patterns in big datasets with multiple categories and evaluate how they change over time. Just like with other kinds of charts on this list, the width and length of the streams are proportional to the values being displayed. The colors can represent different categories or other specific criteria. In our example above, we can see the number of orders by product categories each month. The width of each stream can provide valuable insights into the performance of each category. For instance, from June to August orders for TVs and Home Theaters decreased a lot compared to other months so some conclusions need to be drawn.

In general, if your aim is to use the chart to deeply analyze the data and extract conclusions from it, then the stream is not your best option. They are often cluttered with a lot of information which can lead to legibility issues. This can happen especially when you have smaller categories that end up looking way too small compared to bigger ones. For that reason, it is best to use stream charts as interactive visuals instead of static or printed ones. 

19) Word Cloud 

A word cloud is a straightforward type of graph that displays a set of words concerning a specific topic. The words are arranged in different directions and the sizes of the words will vary depending on specific criteria. For example, if a word cloud is generated based on a text from product reviews, the size of the words can be influenced by the number of times each word is mentioned within the text. On the other hand, if you are generating a word cloud of all the countries in the world, the names of the countries can be bigger or smaller depending on their population. From an analytical perspective, word clouds don’t provide a lot of value apart from being an engaging and visually appealing way of presenting a topic or supporting discussions. 

There is no general rule when it comes to colors on a word cloud. Some might use different colors to provide meaning to certain words while others might use standard colors to match their branding. Whichever case you are using, the rule of not adding too many colors to avoid overcrowding the visual still applies when it comes to word clouds. 

20) Progress chart 

As its name suggests, this chart is used to track the progress of a specific activity or scenario usually in a percentage form. It can be represented using bars or columns and is often tracked against a set target, as seen in the graph examples below, in which you can see a colored area representing the completed percentage and a lighter shade representing the remaining percentage to complete 100%. Progress graphs are wildly popular when tracking the development of a project as they provide a clear overview of the status of different tasks. They are also valuable visuals when you are trying to show any kind of percentage value or progress against a target. 

Progress charts are very straightforward and don’t provide a lot more information than the development of a metric. If you want to gain more insights you can explore using a bullet chart as they provide more context to the data. 

• Progress bar graph 

The progress bar chart is used to track the progress of a specific activity or metric using horizontal bars. The example above is tracking the percentage of purchases in time and budget from a procurement department. Ideally, the end goal for each category would be 100% as this means all purchases are made on the expected time and budget. However, this is not always the case and the progress bar is a great way to see how far from the expected target the values actually are. In this case, the average is represented by a darker color of green, and the remaining percentage to reach 100% is represented by a lighter shade.  

• Progress column graph 

The progress column chart is a type of progress chart that uses columns to represent different data values. In this case, our example is showing the average time to fill a position by the department where each department has a predefined target they are expected to reach. In this case, the target value is represented by a dark purple dot, in other cases, it could be represented with a lighter shade of the same purple from the column. Using a progress chart to represent this metric is a great way to compare the different departments and see if any of the processes need to be optimized to better reach the expected target. 

How To Choose The Right Chart Type: 9 Essential Questions To Ask

To go further into detail, we have selected the top 9 questions you need to consider to ensure success from the very start of your journey.

1. What story do you want to tell?

At its core, data charts are about taking data and transforming it into actionable insight by using visuals to tell a story. Data-driven storytelling is a powerful force as it takes stats and metrics and puts them into context through a narrative that everyone inside or outside of the organization can understand.

By asking yourself what kind of story you want to tell with your data and what message you want to convey to your audience, you’ll be able to choose the right data visualization types for your project or initiative. And ultimately, you’re likely to enjoy the results you're aiming for.

For more on data storytelling, check out our full guide for dashboard presentation and storytelling.

2. Who do you want to tell it to?

Another key element of choosing the right data visualization types is gaining a clear understanding of who you want to tell your story to – or in other words, asking yourself the question, “ Who is my audience ?”

You may be aiming your data visualization efforts at a particular team within your organization, or you may be trying to communicate a set of trends or predictive insights to a selection of corporate investors. Take the time to research your audience, and you’ll be able to make a more informed decision on which data visualization chart types will make the most tangible connection with the people you’ll be presenting your findings to.

3. How big is your data? 

As you probably learned from our list of the essential types of charts and when to use them, the size of your data will significantly affect the type of visualization you decide to use. Some charts are not meant to be used with massive amounts of data due to design aspects while others are perfect for displaying larger information. 

For example, pie charts are not good if you are trying to show multiple categories. For that purpose, a scatter plot works best. Another example is with column and bar charts. Bar charts use horizontal bars that make it easier to represent larger data sets. On the other side, column charts are limited by size due to their vertical orientation, making them better for smaller data. 

4. What is the type of data you are using? 

Another important question to ask yourself is what type of data you are using. As we saw at the beginning of the post, there are 4 key categories when it comes to data visualization: composition, distribution, relationship, and comparison. There are also qualitative and quantitative data that can be better represented using a particular graphic. For this reason, it is important to carefully define the type of data you are using before thinking about visualizing it. In the following questions, we will see what you need to ask yourself based on the mentioned categories. 

5. Are you looking to analyze particular trends?

Every data visualization project or initiative is slightly different, which means that different data visualization chart types will suit varying goals, aims, or topics.

After gaining a greater level of insight into your audience as well as the type of story you want to tell, you should decide whether you're looking to communicate a particular trend relating to a particular data set, over a predetermined time period. What will work best?

• Line charts
• Column charts
• Area charts

6. Do you want to demonstrate the composition of your data?

If your primary aim is to showcase the composition of your data – in other words, show how individual segments of data make up the whole of something – choosing the right types of data visualizations is crucial in preventing your message from becoming lost or diluted.

In these cases, the most effective types of visual charts include:

• Waterfall charts
• Stacked charts
• Map-based graphs (if your information is geographical)

7. Do you want to compare two or more sets of values?

While most types of data visualizations will allow you to compare two or more trends or data sets, there are certain graphs or charts that will make your message all the more powerful.

If your main goal is to show a direct comparison between two or more sets of information, the best choice would be:

• Bubble charts
• Spider charts
• Columned visualizations
• Scatter plots

Data visualization is based on painting a picture with your data rather than leaving it sitting static in a spreadsheet or table. Technically, any way you choose to do this count, but as outlined here, there are some charts that are way better at telling a specific story.

8. Is timeline a factor?

By understanding whether the data you’re looking to extract value from is time-based or time-sensitive, you’ll be able to select a graph or chart that will provide you with an instant overview of figures or comparative trends over a specific period.

In these instances, incredibly effective due to their logical, data-centric designs, functionality and features are:

• Dynamic line charts

9. How do you want to show your KPIs?

It’s important to ask yourself how you want to showcase your key performance indicators as not only will this dictate the success of your analytical activities but it will also determine how clearly your visualizations or data-driven stories resonate with your audience.

Consider what information you’re looking to gain from specific KPIs within your campaigns or activities and how they will resonate with those that you’ll be sharing the information with - if necessary, experiment with different formats until you find the graphs or charts that fit your goals exactly.

Here are two simple bonus questions to help make your data visualization types even more successful:

• Are you comparing data or demonstrating a relationship?
• Would you like to demonstrate a trend?

At datapine, data visualization is our forte. We know what it takes to make a good dashboard – and this means crafting a visually compelling and coherent story.

"Visualization gives you answers to questions you didn’t know you had." – Ben Shneiderman

Design-thinking In Data Visualization

When it comes to different data visualization types, there is no substitute for a solid design. If you take the time to understand the reason for your data visualization efforts, the people you’re aiming them at, and the approaches you want to take to tell your story, you will yield great results.

Here at datapine, we’ve developed the very best design options for our dashboard reporting software , making them easy to navigate yet sophisticated enough to handle all your data in a way that matters.

With our advanced dashboard features , including a host of global styling options, we enable you to make your dashboard as appealing as possible to the people being presented with your data.

Your part in creating an effective design for the different types of data charts boils down to choosing the right visualization to tell a coherent, inspiring, and widely accessible story. Rarely will your audience understand how much strategic thought you have put into your selection of dashboards – as with many presentational elements, the design is often undervalued. However, we understand how important this is, and we’re here to lend a helping hand.

In this guide, we covered different types of charts to represent data, explored key questions you need to ask yourself to choose the right ones, and saw examples of graphs to put their value into perspective. By now, you should have a better understanding of how each type of visual works and how you can use them to convey your message correctly. 

To summarize, here are the top types of charts and their uses:

• Number Chart - gives an immediate overview of a specific value .
• Line Chart - shows trends and changes in data over a period of time .
• Maps - visualizes data by geographical location.
• Waterfall Chart - demonstrates the static composition of data.
• Bar Graphs - used to compare data of large or more complex items .
• Column Chart - used to compare data of smaller items. 
• Gauge Chart - used to display a single value within a quantitative context.
• Pie Chart - indicates the proportional composition of a variable.
• Scatter Plot - applied to express relations and distribution of large sets of data.
• Spider Chart - comparative charts great for rankings, reviews, and appraisals.
• Tables - show a large number of precise dimensions and measures .
• Area Chart - portrays a part-to-whole relationship over time .
• Bubble Plots - visualizes 2 or more variables with multiple dimensions.
• Boxplot -  shows data distribution within multiple groups.
• Funnel Chart - to display how data moves through a process.
• Bullet Chart - comparing the performance of one or more primary measures .
• Treemap - to plot large volumes of hierarchical data across various categories.
• Stream Graph - shows trends and patterns over time in large volumes of data.
• Word Cloud - to observe the frequency of words within a text.  
• Progress Chart - displays progress against a set target or goal.

But in our hyper-connected digital age, there are many more different kinds of graphs you can use to your advantage. Putting everything together in a professional business dashboard is even better. These visual tools provide centralized access to your most important data to get a 360-view of your performance so you can optimize it and ensure continuous growth.

Complete with stunning visuals, our advanced online data visualization software can make it easy for you to manipulate your data and visualize it using professional dashboards. The best part is, you can try it for a 14-day trial , completely free!

24 Essential Types of Charts for Data Visualization: Examples & Use Cases

19 min read

There are more types of charts and graphs than ever before. Graphs and charts are a great way to display statistics and visualize data points.

As we move deeper into the era of data, data visualization is even more important. It helps product managers motivate teams to action, impress stakeholders, and quickly derive actionable insights.

But as any data visualization expert will tell you, few things are as annoying as the wrong use of visualizations. This article explores 24 types of charts and graphs and how they are used.

• Funnel charts are excellent tools for visualizing how data flows through a funnel. It illustrates how sequential data progresses until the final step.
• The bar chart is a chart for numerical data that uses the length of bars to indicate the size of data points.
• Stacked bar graphs are modified bar charts that show the composition of each variable through the divisions on the bar.
• Dual-axis charts combine two different charts to show the relationship between data sets of different scales.
• Pie charts are circular graphs and charts divided into slices to represent the size of each component variable.
• Heatmaps use colors on a grid to represent large and complex numeric data.
• Flow charts illustrate how a process flows from its beginning to its end.
• The scatter plot shows the correlation between two numeric variables. It is also a great tool for finding outliers.
• The bubble chart is a modified scatter plot that demonstrates the relationship between three numerical variables.
• A bullet graph is a merger of a progress bar and a bar chart. It is used to compare actual data with a target value.
• A radar chart compares a minimum of three quantitative values using a series of radii. The length of the radii corresponds to the data value.
• Others include area charts, control charts, Pareto charts, treemaps, etc.

Funnel charts

A funnel chart is useful in business contexts for tracking how visitors or users flow through a pipeline. The chart shows a series of steps and tracks the completion rates for those steps.

The width of each part of the chart shows how many users make it to each stage relative to other stages. If you have two or more stages of sequential data, the funnel chart can help you see how each stage impacts the final result.

The most common use cases for funnel charts include:

• Conversion tracking and analysis.
• Measuring progression through sales and marketing funnels .
• Identifying bottlenecks in multi-step processes like manufacturing.
• Analyzing deal pipelines.

A practical example of a funnel chart usage

In this example, a funnel chart displays an onboarding or signup process . The chart helps you to see which steps have the most drop-offs in the process, enabling you to track the problem source easily.

The bar chart is one of the first types of charts you’ll learn about. This chart indicates values by the length of bars, with each bar corresponding to a measured group.

A bar chart can be oriented vertically or horizontally. The horizontal bar chart is ideal when you have a lot of data points and, as a result, a lot of bars to plot. It also gives you more space to write out bar labels.

Bar charts are excellent numeric comparison tools, with bar sizes revealing the relative difference in sizes. This visual comparison chart makes it easy to see which data group is the biggest or most common. Its use cases include:

• Comparing products or categories.
• Visualizing web traffic or product usage by month or year.
• Tracking marketing conversions , etc.

A practical example of a bar chart usage

As earlier noted, bar charts are great when comparing numeric values. In this example, the bar chart compares the number of standard, free, and enterprise invoices created over the last 7 days.

Stacked bar chart

The stacked bar chart is a modified bar chart with each bar divided into smaller bars. This division allows you to compare different items and show the composition of each item.

So, in addition to comparing primary groups like a regular bar chart, the stacked chart also illustrates the breakdown of each group’s constituent parts.

Stacked bar charts are useful for comparing more than one grouping of variables. This can be ideal, for example, when comparing sales distribution over time. Each bar can represent a period (say, a month) and be further segmented to show sales across different categories.

Other use cases include:

• Tracking website traffic and its composition.
• Comparing survey results .
• Analyzing event distribution over multiple platforms.

A practical example of a stacked bar chart usage

In this example, the stacked bar chart is used to compare how many users created a specific type of invoice each day for the last 7 days.

Dual-axis charts

Dual-axis charts are visualization tools that use two different axes to display the relationship between data series with potentially different scales or units of measurement.

Also known as combination charts, they overlay two different charts for easy comparison. This can be useful to compare two perspectives on the same chart, allowing for deeper insights.

The typical dual-axis chart combines a line chart with a bar chart. Some common use cases for this chart type include:

• Analyzing trends with different units.
• Highlighting correlation across scales.
• Identifying hidden patterns and outliers.

A practical example of a dual-axis chart usage

In this example, the dual-axis chart compares sales values and profit margins for each year – from 2016 to 2021. Here, we see that profit margins are unconnected to sales values, and they’ve declined since peaking in 2019.

One of the most popular types of charts, pie charts are circular charts that visualize a part-to-whole relationship. The complete chart is a pie, with each slice representing the portion of the pie occupied by a variable.

Each slice in the pie, therefore, represents a percentage of the pie, with the total sum of all slices adding up to 100%. This form of data representation, thus, makes it easy to see the relative size of each data point to the whole.

Typical use cases for pie charts include:

• Percentage of profit or sales from different channels .
• Number of users to perform one action versus another.
• Size of different customer personas versus the total number of customers, etc.

A practical example of a pie chart usage

This pie chart example illustrates the number of new signups who perform a key action. From the chart, you can easily see that more users tried to identify a user, with fewer users creating or saving a contract.

Heatmaps present data as a grid of values and are useful for showing variances between two items. Each data point on the heatmap is assigned a color based on its value.

Higher values are represented on the chart using hotter colors or saturation, while lower values are indicated with cooler colors. This gradient creates a visual hierarchy that instantly guides your eye to areas of concentration.

Heatmaps are ideal for complex information and large datasets. Thanks to how they represent data, they are great for comparison, trends, and correlations. Typical use cases include:

• Website or product analytics .
• Analysis of marketing campaign performance and impact.
• Product usability tests , etc.

A practical example of a heatmap usage

In this example, a product analyst uses a heatmap to visualize feature engagement across different user segments.

Flow charts

Flow charts are used to visualize workflows, depicting each step in the process and how they relate. It enables you to sequence a process step-by-step, from beginning to end, making analysis and documentation easy.

A simple flow chart can connect straight lines from point A through C. However, flow charts can also have multiple pathways and journeys, illustrating more complex sequences with multiple conditions and decisions.

The typical flow chart enjoys many organizational uses, including:

• Mapping out the customer journey from discovery to activation, retention, etc.
• Planning and documenting project flows in project management.
• Planning and auditing UX and UI flows and processes.

A practical example of a flow chart usage

This fairly simple flow chart illustrates the flow of a user from new signup to referrer. Given how referral programs work – with users bringing new users who bring others – it’s no surprise that the chart’s design is cyclical.

Scatter plots

Also known as scatter charts, scatter plots visualize the relationship between two numeric variables. It consists of two axes, each representing a data set, with points plotted using dots at the intersection of each axes.

A scatter plot shows the correlation between numeric variables – whether that correlation is linear or non-linear, strong or weak, positive or negative. It enables you to identify outliers and understand your data’s distribution easily.

The scatter plot is ideal when you have two numeric data sets and want to quickly see the pattern in the data or the relationship between both variables. This type of chart can be useful when comparing:

• Web traffic numbers with the number of new customers.
• Sales growth versus advertising spend.
• Trial users versus paid users, etc.

A practical example of a scatter chart usage

The scatter plot in this example shows the relationship between car prices and the car’s age. Very quickly, we notice this is a negative relationship, with prices dropping as the car’s age increases.

Bubble charts

Also known as a bubble plot, the bubble chart derives from a scatter plot. It is a chart that demonstrates the relationship between three numerical data points. It can also be modified for three numeric and one categorical variable.

Your typical scatter plot shows the distribution or relationship between two numeric variables. When a third numeric variable is added, the bubble chart builds on the base plot by having the third variable’s value determine the size of each point.

Bubble charts are great for visualizing trend lines over time. They’re also great for demonstrating the relationship between multiple variables. Some ideal use cases include:

• Analyzing product usage and feature adoption patterns.
• Tracking campaign performance metrics such as clicks, conversions, and impressions to visualize their impact on investments.
• Analyzing team performances. For example, you could compare sales reps based on their close rate, pipeline value, and deal size.

A practical example of a bubble chart usage

The bubble chart below demonstrates how many hours each age group spends online. The bubble colors are a factor of the gender of the group, and bubble sizes are affected by their engagement level while online.

Bullet charts/Bullet Graphs

The bullet chart is a compact and visually compelling tool used to present data comparisons within a single chart or metric. This type of chart is like a progress bar fused into a bar/column chart.

How the chart works is fairly simple. An external bar shows the values from the desired metric, maxing out at a target value. Then, an inner “bullet” bar shows the actual value.

A bullet chart is an excellent tool for comparing performance against goals. It’s ideal for visualizing KPIs (such as revenues, profits, etc.) against their goals/targets. It’s also great for:

• Comparing product usage data year-over-year.
• Analyzing actual customer satisfaction scores versus target values.
• Analyzing year-over-year customer shopping habits, etc.

A practical example of a bullet chart usage

In the above example, the bullet graph compares the target revenue (120k) versus the actual revenue (82k) for the previous month.

Radar charts

Also known as a spider chart, star chart, or web chart, the radar chart compares a minimum of three quantitative variables. The chart consists of a series of radii, each representing a different category, splayed out from a center point.

Data points are plotted along their corresponding axes, forming a polygon. The closer a data point is to the outermost circle, the higher its value. This visual representation enables quick comparison across metrics.

The radar chart type can be useful when:

• Comparing marketing campaign performances across metrics like impressions, clicks , conversions , cost per acquisition , etc.
• Measuring feature impact using metrics like user adoption, revenue growth, and customer satisfaction.

A practical example of a radar chart usage

In this example, a radar chart is used to compare three products across six quantitative data points (price, usability, quality, advertising, etc.).

Area charts

Area charts and graphs begin with the same foundation as line charts. Data points are plotted using dots, with a line connecting each point. The only difference is that the space between the x-axis and the line is shaded.

The shading on the area chart makes it easy to visualize changes in volume over time. Thus, rather than focus on specific values, it shows the general changes that occur over a period.

A practical example of an area chart usage

In this example, the area chart visualizes the page views of a segment of users over the last 7 days.

Stacked area charts

The stacked area chart is a combination of multiple area charts (multiple data series) on the same graph. It is particularly designed as a comparison chart and is useful for showing part-to-whole relations.

Stacked area charts are comparison tools for multiple data sets (or series) over time. Some use cases include:

• Comparing products or product categories’ performances over time.
• Visualizing the outcome vs. goals of key performance indicators (KPIs).
• Comparing overall trends in the industry, etc.

A practical example of a stacked area chart usage

The stacked area chart below visualizes the number of user actions (page views, invoice creation, and project creation) over 7 days.

Column charts

Column charts are very similar in style to bar charts. Also known as the grouped bar chart, you can think of these types of charts as bar charts in columns.

Each group contains color-coded bars for different categories or items, with each category making an appearance for each data point. Columns for each data point are separated using spacing.

Column charts are ideal when comparing multiple series, as opposed to a bar chart which represents data from one series only. Example use cases include:

• Comparing average order value for different customer segments .
• Charting the number of active users or feature adoption rates for different features, etc.

A practical example of a column chart usage

In the example below, the column chart shows the evolution of a company’s sales volume over an 8-month period. You can easily see that sales peaked in April 2015 and were lowest in November 2014.

Gantt charts

Gantt charts are visual project management tools that help you plan, track, and coordinate tasks over time. They bring all completed and uncompleted tasks into one place.

The left side of the chart displays all tasks, while the right side represents the project timeline (in days, weeks, or months). In addition to schedules and timelines, the chart also highlights dependencies and milestones.

Gantt charts are great tools for analyzing, planning, and monitoring projects. This makes it excellent for:

• Planning and tracking product development tasks and timelines.
• Tracking leads in a sales process, etc.

A practical example of a Gantt chart usage

The Gantt chart below was designed for the project planning phase. Using color codes, it identifies who is in charge of each task. Other graphics also identify different milestones and their expected completion dates.

Line charts

A line chart or a line graph is one of the best types of charts for visualizing data trends over time. Amongst the oldest charts and graphs out there, they are versatile, simple, and easy to use.

The chart typically has a numeric variable on the vertical axis and a timeline on the horizontal axis (in days, weeks, months, etc.). The intersection of both values is plotted with a dot, and all dots are connected by a line.

Line charts are ideal for visualizing trend lines over a period. They make it easy to see positive spikes and negative downturns. Some of its use cases include:

• Comparing sales prices over time.
• Visualizing trends in customer acquisition over a period.
• Measuring the performance of your service channels over time, etc.

A practical example of a line graph usage

The chart below combines two line graphs for comparison purposes. It compares the number of users who started onboarding versus the number who completed it in the last 180 days.

Sunburst charts

The sunburst chart is a type of multi-level pie chart used to illustrate hierarchical data. It consists of a series of concentric outward rings, with each ring corresponding to a different hierarchical category.

Each outer ring represents a higher hierarchy and is sliced up according to its relationship to the inner (parent) circle. Although the sunburst chart is often a better alternative to treemaps, it typically takes up a lot more space.

The sunburst chart is ideal for illustrating hierarchical data, such as an organizational chart, or creating a sort of historical hierarchy by breaking down data by periods.

For example, branches of an organization can be represented by designated colors, with different levels showing divisions in the organization and taking on the parent color.

A practical example of a sunburst chart usage

The sunburst chart in this example breaks down a store’s sales of mobile devices. It starts from the total sales, moves out to sales by brand, and ends at sales by device models.

Waterfall charts

A waterfall chart is a special chart type for showing how an initial value changes over time. It records all intermediate changes, whether positive or negative, as well as the final value.

By providing the story beyond the starting and ending points, the waterfall chart makes it easy to understand how different factors impact your data.

This type of chart is useful for understanding the progression of numbers. This makes it ideal in financial sectors, human resources, and customer analysis. Some use cases include:

• Visualizing positive and negative changes in customer churn over a period.
• Illustrating changes in free trial, new, or current users over time.
• Visualizing changes in revenue or profit over time, etc.

A practical example of a waterfall chart usage

The waterfall chart below illustrates a product’s financial performance over a financial year. Here, we notice every revenue and expense that impacts its growth positively or negatively in the lead-up to the reported growth for financial year 13.

Treemap chart

Much like sunburst charts, treemap charts are ideal for hierarchical data. They use nested rectangles of varying sizes and colors to represent data points within a defined hierarchy.

Categories in a treemap can be subdivided into smaller rectangles if the data contains subcategories. This makes them a great tool for visualizing relationships, both within and between categories.

The treemap chart is a major upgrade on the regular bar chart as it easily visualizes which items (products, features, functionalities, etc.) contribute the most to an overall value/goal.

For example, you can use a treemap chart to visualize feature engagement levels. Each rectangle represents a feature (e.g. file sharing, task management, etc.), with its size reflecting the feature’s usage share.

Other sample use cases include:

• Analyzing user behavior and feature preferences across different customer segments to tailor your offerings.
• Uncovering unexpected relationships between feature/product categories.
• Comparing product performances (revenue, sales, adoption, etc.) to identify similarities or anomalies within and between different categories.

A practical example of a treemap chart usage

The detailed treemap chart below shows the sales volume for different product categories and sub-categories for a company that sells bikes and bike gears.

There are four main categories – bikes, components, clothing, and accessories, each sub-divided into smaller product categories for easy comparison.

Mekko charts

Also known as the Marimekko chart, the Mekko chart is a powerful visualization tool for dissecting data and representing part-to-whole relationships.

Mekko charts resemble treemap charts but work like stacked bar graphs. Like the treemap, it begins with a large rectangle divided into groups and sub-groups according to size.

However, these groups are organized side-by-side, with each group sub-divided proportionally based on their sub-categories. Unlike stacked bar graphs, though, Mekko’s x-axis isn’t limited to comparing time progression.

Although it is a rarely used chart type, the Mekko is ideal for situations where you want to emphasize scale differences between data groups. For example, you could use the chart to:

• Track the composition of leads generated by different channels (paid, organic, referral, etc.) and their conversion rates .
• Analyze feature adoption rates for different features across customer segments to reveal product-market fit variations.
• Visualize the progression of users through different user journey stages to identify drop-off points and areas for improvement, etc.

A practical example of a Mekko chart usage

The Mekko chart below illustrates the market share of different smartphone operating systems and the age composition for each. A glance at the chart reveals that Android, Apple, and BlackBerry enjoyed the majority (and similar) market shares at the time of publishing.

Hierarchy diagrams

Hierarchy diagrams are similar in appearance to flow charts. Also known as an organizational chart or organigram, this chart type illustrates the structure of an organization and the relationships within it.

Hierarchy diagrams excel at visually depicting relationships within a system or organization, revealing the order and flow of information. It usually consists of shapes that represent individual elements and clear connection lines depicting relationships and hierarchical flow.

Common use cases for hierarchy diagrams include:

• Depicting organizational structure, relationships, roles, and responsibilities.
• Visualizing the architecture and flow of features and functionalities within a product.
• Mapping out user access levels and permissions to ensure data security and control, etc.

A practical example of a hierarchy diagram usage

Below is an example of a simple hierarchy diagram depicting roles and communication lines for a new project. There is an overall project manager and three teams, each with a head and two members.

Pareto charts

A Pareto chart is designed to reveal the most important information among many. Also known as the 80/20 chart, it provides a visual representation of the Pareto principle: 20% of causes influence 80% of outcomes.

To do this, it combines a bar graph with a line graph to illustrate the individual values of each category and the cumulative value of the entire total.

The bars on the chart reveal the value of each item, ranked in descending order of value. Meanwhile, the cumulative line graph shows the percentage of the total value added by each bar.

Pareto charts highlight the top contributors to an outcome, making it easy to identify priority areas. Some sample use cases include:

• Analyzing traffic sources for a SaaS website or product to find the top 20%.
• Identifying the 20% of customer care issues that generate the most support tickets.
• Determining the 20% of product features used by 80% of users to guide future resource allocation and development plans, etc.

A practical example of a Pareto chart usage

In this chart, a company tries to determine the top 20% of defects affecting food products. The visualization makes it easy to see that the first three problems account for over 80% of food defects.

Control charts

A control chart compares a data set to a predetermined “control” set. Also known as a process behavior chart, it excels at visualizing process performance over time, enabling you to detect variations promptly.

Control charts typically display a central line representing an average or expected value. There may also be upper and lower control limits to define expected boundaries for normal data.

Points outside of these boundaries, therefore, are identified as outliers and marked for further investigation.

There are many different types of control charts, each designed for a specialized purpose. Depending on your choice of control chart, sample use cases may include:

• Tracking average ticket resolution times to identify outliers that may indicate bottlenecks.
• Tracking the frequency of software errors to identify patterns and unmask underlying issues.
• Monitoring churn rates to identify spikes in subscription cancellation and take remedial action, etc.

A practical example of a control chart usage

In this example, a control chart is used to track NPS history and compare it to prior trends, enabling you to easily identify progress or setbacks.

Stock charts

Stock charts are visual representations of a stock’s price movement over time. These charts are popular in the financial market and are used by traders and investors to analyze trends, identify opportunities, and make decisions.

The typical stock chart is an adaptation of the line graph into a histogram. Each line tracks the changes in a stock’s price, and multiple lines can be layered over themselves to compare multiple stock performances.

Stock charts are hyperspecific to the financial market. Use cases include:

• Portfolio management.
• Trading signals and algorithms.
• Investment research and analysis, etc.

A practical example of a stock chart usage

The stock chart below tracks the performance of the $NYAD index, an excellent tool for assessing the overall health of the market.

When to use each type of charts and graphs?

Knowing the different types of charts and graphs is only the first step. To succeed with data visualization, you must also know how to choose the right chart for your data.

What follows is a good rule of thumb that can help you make that decision.

• Comparison charts : The column , mekko , bar , pie , line , scatter , and bullet charts are ideal if you want a visualization that helps you compare data points and value sets.
• Part-to-whole charts : These charts help you visualize the parts of a larger item. They include the pie, stacked bar, waterfall, area, stacked area charts, etc.
• Distribution charts : These charts help you to visualize the distribution of your data and identify outliers. They include the scatter plot, Mekko, line, column, and bar charts, among others.
• Trend Analysis : Graphs and charts like the line , dual-axis , and column charts help you easily determine how a data set performed at a specific time.
• Relationship charts : Charts such as the bubble chart and scatter plot are ideal when you want to show how one variable relates to one or more variables.

There are many more charts and graphs not mentioned in this piece. However, all of the most popular types of charts and some not-so-popular but very useful ones have all been captured.

Thankfully, with Userpilot, you don’t have to think too hard for the best chart for visualizing your product data. Once connected, Userpilot does the rest. To get started, book a demo today !

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Data Visualization in Business Presentations: Graphs and Charts Best Practices

Aayush Jain

Navigating the graphs and charts galaxy: a guide for data storytellers.

Introduction to Graphs and Charts Selection

When it comes to data visualization in business presentations , the choice of graphs and charts is pivotal. These visual aids are the storytellers of numerical tales, translating complex datasets into comprehensible narratives. The art lies in matching the story you wish to tell with the appropriate visual vehicle. Whether it’s a bar graph illustrating quarterly sales or a line chart tracking website traffic trends, the right selection can illuminate insights and guide decision-making processes. A well-chosen chart or graph not only enhances the business case presentation but also reinforces the message with clarity and impact.

Historically, visual representations of data have been instrumental in facilitating understanding. From Florence Nightingale's Coxcomb plots to John Snow's cholera outbreak maps, the ability to see patterns and relationships in data has shaped decision-making in business and beyond. Today, with the surge in big data and analytics, the role of presentation design services has grown exponentially. They specialize in harnessing the power of PowerPoint presentation design and other tools to distill and display data. The choice of graph or chart hinges on the nature of the data – categorical, time-series, comparative, or relational – and the story it is intended to convey.

Real-world Application and Current Trends

In the realm of real-world application, companies often turn to presentation design agencies to help them craft a compelling business case slide. For instance, a product launch with right presentation design agency might leverage a combination of pie charts and histograms to depict market segmentation and consumer preferences. Meanwhile, presentation design companies are increasingly adopting interactive dashboards, allowing for a dynamic and engaging data experience. These tools not only display static figures but also enable viewers to interact with the data, drilling down into specifics and exploring different scenarios on the fly.

Credibility in data representation is paramount, and referencing high-quality external sources is key to achieving this. For a presentation redesign for example, the use of business case templates with proven effectiveness can be backed by statistics from reputable agencies. According to a recent survey by a leading PowerPoint design agency, presentations that utilized tailored, data-specific graphs saw a 30% increase in audience engagement. Furthermore, a study in the Journal of Business Communication found that incorporating clear and relevant graphs in sales presentations increased the persuasiveness of the argument by 43%.

Simplifying the Complex: The Art of Streamlining Data Visualization

Introduction to Simplifying Complex Data

Data, by its nature, can be intricate and overwhelming, especially when large volumes are involved. This is where the second rule of data visualization in business presentations comes into play: simplification. The goal is to strip down complexity without losing the essence of the information. Simplifying complex data visualization involves choosing the right powerpoint presentation design services that can distill vast amounts of data into easily digestible visuals. By doing so, presentation design agencies ensure that the audience can quickly grasp the underlying patterns and messages without getting lost in a sea of numbers.

Delving into Simplification Techniques

The process of simplification is not about dumbing down data; it's about smart consolidation and prioritization. Techniques vary from employing data aggregation—whereby data is categorized into larger groups for a broader view—to highlighting significant data points to draw attention to critical trends or outliers. Google slides and other presentation software offer tools that can enhance simplicity, such as clean lines and comparative scales. These techniques by skilled presentation designers help to avoid cognitive overload and make the data more approachable and memorable.

Real-world examples abound where complex data visualization has been successfully simplified. Presentation design companies often showcase before-and-after scenarios to demonstrate their skill in transforming convoluted spreadsheets into intuitive charts. For instance, a recent corporate presentation by a tech giant exhibited a remarkable use of a minimalist interactive data dashboard that allowed the audience to explore various market scenarios with ease. Additionally, many presentation design agencies have created visual case studies that display their expertise in simplifying complex projects, making a strong case for their presentation services here.

Statistics show that presentations with simplified visuals have a higher retention rate. A survey by a top presentation design company revealed that their clients reported a 50% better understanding of data when it was presented in a simplified manner. The Harvard Business Review also emphasizes the effectiveness of simple visual aids, citing that investors and executives often prefer straightforward graphs over complex ones because they can quickly comprehend and act on the information. Thus, the importance of simplification in data visualization cannot be overstated, as evidenced by the expertise and recommendations of industry leaders.

Precision in Presentation: Ensuring Accuracy and Clarity in Data Visualization

Introduction to Accuracy and Clarity in Data

The integrity of a business case presentation hinges on the accuracy and clarity of its data visualization. This concept is a cornerstone in communicating information effectively, as even the most visually appealing business case presentation template can be rendered useless if the data it conveys is not accurate and clear. The challenge for presentation design services is to maintain the delicate balance between aesthetic appeal and the precise portrayal of data. It is a balance that requires a deep understanding of data nuances and a keen eye for design.

Background on the Importance of Accurate Data Representation

The history of data visualization is littered with examples where inaccuracies in data representation have led to misinformed decisions with significant consequences. Thus, presentation designers place a high emphasis on data integrity. Accuracy involves double-checking data sources, using appropriate scales, and avoiding misleading graph elements such as truncated axes or inappropriate intervals. Clarity is about making the data accessible, using the right type of graph to match the message, and avoiding clutter that can obscure the data's meaning.

Illustrating with Real-World Examples

Consider a PowerPoint presentation for a major product launch presentation. Here, precise market data visualization could be the difference between securing stakeholder buy-in or not. A presentation design agency might use clear, accurate bar charts to depict market share, while a sales presentation may require precise line graphs to forecast sales trends. Google Slides and other tools now offer advanced features that help ensure accuracy, such as live data integration, which can update charts in real-time to reflect the most current data.

The best presentation design companies not only focus on the aesthetics of graphs but also on the verifiability of the data presented. They often cite and link to data from credible sources, such as industry reports or academic studies, which adds a layer of trust to the presentation. For instance, a business case analysis might be supported by data from financial institutions or market research firms, complete with citations. According to a survey by a leading business school, presentations that referenced authoritative data sources were rated as more than persuasive presentations by 65% of executives surveyed.

Unveiling Success Stories: Real-world Examples of Effective Data Visualization

Introduction to Effective Data Visualization

Effective data visualization acts as a bridge between complex data sets and decision-making processes. It is a potent tool in a business case presentation, with the power to influence and inform. But what makes a data visualization effective? The answer lies in its ability to tell a story that is both compelling and understandable. It’s not just about presenting data; it’s about crafting a narrative that resonates with the audience. Presentation design services strive to achieve this by focusing on design elements that highlight the key messages within the data.

Background on Data Visualization Success

The success stories of data visualization are as varied as the fields that use them. For example, the use of infographics in business case slides has transformed dry statistics into engaging stories. Historical examples, like the work of Edward Tufte, who emphasized the importance of displaying data with integrity and simplicity, continue to influence modern presentation design. Moreover, both powerpoint presentations and presentation design services have evolved to incorporate storytelling elements, making data not just informative but also memorable.

Real-world Examples of Visualization

Among the notable instances of effective data visualization are the interactive dashboards used by major consulting firms. These dashboards have revolutionized the way data is presented, allowing users to interact with the information dynamically. For example, a business case analysis presented through an interactive dashboard can help the audience visualize the impact of different business scenarios. Another example is the annual reports of leading tech companies, which often employ a mix of charts, graphs, and animations to present financial data, user statistics, and performance metrics.

The real-world effectiveness of data visualization is often supported by research and expert opinion. A study published in the 'International Journal of Business Communication' found that incorporating well-designed charts and graphs could increase comprehension by up to 70%. Presentation design agencies often highlight these findings to validate their approaches. Additionally, companies like Google have published white papers on the effectiveness of visualization in Google Slides, demonstrating how visual storytelling aids information retention and decision-making.

The Art of Data Storytelling: Leveraging Infographics

Introduction to Data Storytelling Through Infographics

Infographics have revolutionized the way we understand and interact with data in a business case presentation. They blend visual appeal with information-rich content to create a compelling narrative. The essence of data storytelling lies in its ability to make complex information easily digestible, and infographics are the perfect vessel for this. Presentation design services use infographics to transform rows of data into a visual story that engages and informs the audience, making them a staple in the arsenal of best presentation design company.

Exploring the Evolution of Infographics

Infographics are not a modern invention; they have been used for centuries, from ancient cave paintings to the elaborate diagrams of the Renaissance. In the context of business, they have taken center stage as a means to convey business cases, market analyses, and statistical data. The evolution of presentation design tools, including PowerPoint presentation design services and Google Slides, has given rise to innovative infographic formats that combine images, charts, and text to depict information more dynamically and interactively.

Real-world Applications of Infographics

Business presentations , especially those related to marketing presentations and sales presentations, have benefited greatly from the use of infographics. For example, an infographic detailing customer demographics and purchasing behaviors can provide a clear picture of market segments in a business presentation. Presentation design agencies often showcase their expertise by creating infographics that distill complex data, such as financial reports or consumer surveys, into concise visuals that highlight key trends and insights.

Infographics must be rooted in reliable data to maintain credibility. Top presentation design companies ensure that the data they visualize comes from authoritative sources, often referencing industry reports, academic research, or proprietary data. For instance, a business case slide that includes an infographic might cite data from a well-known market research firm to back up the market trends it presents. The use of verifiable sources not only strengthens the message but also enhances the reputation of the business or individual presenting the data.

Enhancing Engagement: Animations and Transitions in Data Visualization

Introduction to Dynamic Visualizations in Presentations

In the landscape of business presentations , animations, and transitions are not mere embellishments; they serve as critical tools for enhancing the understanding and retention of data. When used judiciously, these dynamic elements can guide the audience through a narrative, revealing insights step-by-step and keeping them engaged. The judicious use of animations in PowerPoint presentation design services, for instance, can turn a static business case slide into an interactive storytelling experience.

The Role of Animations in Simplifying Information

Animations can break down complex information into manageable sequences, making it easier for the audience to follow and absorb. By progressively disclosing information, animations help to maintain focus and can effectively highlight changes over time or the relationship between data points. Presentation design agencies understand the cognitive load of their audience and use transitions to direct attention, reduce overwhelm, and facilitate the digestion of complex data.

Real-world Examples of Effective Use of Animations

Consider the impact of a well-animated product launch presentation. It can reveal market trends, product features, and user statistics in a manner that builds anticipation and keeps the audience engaged. Real-world examples include sales presentations that use motion to track the journey of a product from conception to market leader. Presentation designers may use animated charts to depict the growth trajectory, with each phase of growth revealed through a transition, creating a more memorable presentation and persuasive narrative.

While animations can add a layer of sophistication to presentations, their accuracy and relevance should be grounded in solid data. Top presentation design service companies often quote statistics on the effectiveness of animated visuals. For instance, research has shown that animated visuals in a PowerPoint presentation can increase audience engagement by up to 40%. Presentation design services ensure that the animated elements are not only visually appealing but also represent the data faithfully, often referencing and displaying source data on-screen during the presentation for transparency and trustworthiness.

Interactive Insights: The Power of Data Dashboards in Presentations

Introduction to Interactive Data Dashboards

Data dashboards are at the forefront of modern business presentations, offering a dynamic and interactive way to display complex data. They serve as a control panel, providing a comprehensive view of metrics and key performance indicators (KPIs) at a glance. In a business case presentation, an interactive dashboard can empower the audience to engage directly with the data, fostering a deeper understanding and a more personalized experience.

The Evolution and Importance of Dashboards

Historically, data was presented in static tables and charts, which could be difficult to interpret and act upon. The advent of interactive dashboards has changed the game, allowing users to filter, drill down, and manipulate data in real time. This evolution has been driven by advances in presentation design services and software, with PowerPoint presentation design services now integrating dashboard functionality into their offerings. Dashboards have become an essential tool for project managers and business analysts to convey complex data succinctly and effectively.

Dashboards in Action: Case Studies

Real-world examples of dashboard implementation include multinational corporations that use them for tracking sales and operations across different regions. Presentation design agencies have crafted bespoke dashboards for these companies, which are used in executive summaries and monthly reports. These dashboards often include interactive elements like sliders and dropdown menus, allowing viewers to select different data segments and receive immediate, visual feedback.

The inclusion of interactive dashboards is supported by research indicating their effectiveness. For instance, a study from a presentation design company found that presentations featuring interactive dashboards saw a 50% increase in audience participation. Furthermore, industry experts from presentation design agencies have noted that interactive dashboards can lead to more informed decision-making, as they allow users to explore and interrogate the data themselves, leading to a more engaging and insightful presentation.

Future-Proofing Presentations: Trends in Data Visualization for Business

Introduction to Data Visualization Trends

As the business landscape evolves, so do the trends in data visualization. Keeping abreast of these trends is crucial for any business case presentation, as it reflects a company's ability to adapt and innovate. From AI-powered analytics to real-time data feeds, the latest trends in data visualization are shaping the way businesses present and interpret data. Presentation design services that stay ahead of these trends not only offer cutting-edge solutions but also provide their clients with a competitive edge.

Understanding the Impact of New Visualization Techniques

Emerging technologies are continuously influencing the techniques used in presentation design. For instance, augmented reality (AR) and virtual reality (VR) are beginning to find their way into powerpoint presentation design services, offering immersive ways to explore data. Big data and machine learning are also impacting the field, with algorithms now capable of identifying patterns and insights that might be missed by the human eye, and presenting them in novel and compelling ways.

Case Studies: Innovation in Data Visualization

Innovative companies are already harnessing these new trends to powerful effect. A business case slide that uses AR can allow stakeholders to visualize product placement in a real-world setting, while VR can create an immersive environment for exploring complex datasets. Presentation design agencies are also leveraging these technologies to offer more impactful and engaging presentation experiences. For example, a financial firm used an interactive, real-time data visualization to illustrate market fluctuations during a high-stakes investment meeting, enabling investors to make more informed decisions on the spot.

The future of data visualization is a hot topic among industry experts and thought leaders. Research papers and tech conferences often highlight the potential for AI and interactive elements to transform business presentations. A recent article in a tech journal by a leading presentation design company projected that the use of dynamic and interactive data visualization will increase by 70% in the next five years. This suggests a future where data visualization not only informs but also interacts with the audience in real-time, making presentations more engaging and informative.

Mastering the Numbers: Strategies for Presenting Statistical Data

Introduction to Statistical Data Presentation Strategies

Presenting statistical data effectively is a cornerstone of a persuasive business case for best presentation design agency. The challenge is to present statistics in a way that is accurate, accessible, and engaging. This requires a blend of clear communication, appropriate visualization, and a narrative that connects the data to the audience's needs and interests. Successful presentation design services understand that the right strategies can transform raw numbers into powerful insights.

Deep Dive into Effective Data Strategy

The key to presenting statistics effectively lies in understanding the audience and the context. For a product launch presentation, this might involve focusing on consumer statistics and market potential. Presentation design agencies have developed a range of strategies to handle such data, such as segmenting information, using relative comparisons, and emphasizing change over time. These techniques help to contextualize data, making it more meaningful and impactful for the audience.

Real-World Examples of Statistical Presentation

Real-world examples of statistical data presentation abound in annual business reports and market analysis presentations. For instance, sales presentations often use comparative bar graphs to show performance against competitors, while business case analyses might employ scatter plots to identify correlation between variables. In these cases, presentation designers aim to highlight the most important statistics to support the narrative of the presentation.

Credibility in statistical presentation is reinforced by sourcing data from reputable institutions. Presentation design companies often cite market research firms, government databases, and academic studies to back up the data they present. For example, a business case slide might include statistics from an industry report, adding authority to the presentation. According to a study by a leading university, presentations that include cited statistics are considered more trustworthy by 80% of business executives.

The Persuasive Power of Data: Visualization for Impact

Introduction to Persuasive Data Visualization

In the world of business presentations , data visualization is not just about displaying information; it's about persuasion. It's about using data effectively to influence and drive decision-making. A business case presentation, for instance, needs to do more than just inform—it must convince. To achieve this, presentation design services must leverage data visualization techniques that not only present data but also tell a compelling story, creating an emotional connection with the audience and emphasizing the key takeaways.

Background on the Art of Persuasion through Data

The art of persuasion in data visualization is rooted in an understanding of human psychology and the principles of design. It involves the strategic use of colors, shapes, and layouts to emphasize important data points and guide the audience's perception. For instance, a PowerPoint presentation design that wants to underscore growth will use ascending bar charts with bold, upward-pointing arrows. Presentation designers are adept at using visual hierarchies to direct attention to key factors and employ contrast to create focal points for persuasive storytelling.

Case Studies of Impactful Data Visualization

Looking at case studies, we find numerous instances where data visualization has been used to great persuasive effect. Sales presentations, for instance, often use infographics to highlight customer satisfaction rates or to compare feature benefits. A presentation design company might produce a business case slide that uses a combination of pie charts and pictograms to represent market share and demographic segments, making the data more relatable and impactful.

The persuasive power of visualizations is often backed by data and research from credible sources. Presentation design agencies typically reference statistical evidence to support the choices made in data representation. For instance, a study might show that presentations with data visualizations are 43% more persuasive than those without. Furthermore, experts from presentation design services can cite cases where their visual strategies have directly contributed to successful business outcomes, such as increased investment or improved stakeholder buy-in.

What should be included in a business case presentation?

A business case presentation should include an executive summary, problem statement, proposed solution, benefits and drawbacks, cost-benefit analysis, risk assessment, and implementation plan. Visual aids like graphs and charts should be used to support the data presented.

What are the 4 key elements that a business case should contain?

The four key elements include the executive summary, business idea, analysis of the problem or opportunity, discussion of possible solutions, and a recommendation with justification.

How do you present a business use case?

Present a business use case by starting with a clear problem statement, a simple business case template followed by the proposed solution, the benefits, and how it aligns with business objectives. Use data visualization to make your case compelling.

How do you present an effective business case?

An effective business case is presented by clearly defining the problem, showcasing the benefits of your solution, providing a detailed analysis and evidence, and concluding final presentation with a strong call to action. Incorporate visual data representation for clarity and impact.

What is a business case slide?

A business case slide is a part of a presentation deck that succinctly presents the rationale for a project or initiative, highlighting the benefits, costs, and impact on the organization. It uses graphs, charts, and bullet points for easy comprehension.

How do you structure a business case presentation?

Structure a business case presentation by including an introduction, background information, presentation of the business case, analysis of alternatives, recommended solution, implementation plan, and conclusion. Use charts and graphs to visualize data.

What should a business case include?

A business case should include an introduction, problem statement, analysis of options, technical details, recommended solution, implementation plan, financial analysis, risk assessment, and conclusion. Visual aids can enhance understanding and retention.

How do you write a case study slide?

Write a case study slide by summarizing the background, challenge, solution, and results. Use visuals like graphs, charts, and images to illustrate key points and outcomes effectively.

How much does it cost to design a PowerPoint presentation?

The cost to design a PowerPoint presentation can vary widely, from $100 to over $1,000, depending on complexity, length, and the expertise of the designer or agency involved.

How do I get a PowerPoint designer?

PowerPoint Designer is a feature within Microsoft PowerPoint that offers design ideas for slides. To use it, simply start creating a slide, and the Designer panel will offer suggestions. Ensure you have an active internet connection and a valid Microsoft 365 subscription.

How much does the presentation design cost per page?

Presentation design costs per page can range from $10 to $50 or more, based on the the design style, complexity, amount of content, and the designer's expertise.

How much does Slide Genius cost?

Slide Genius offers custom quotes based on the specific needs of a project. Pricing can vary based on the complexity of the presentation, the number of slides, and additional services required.

How do you start a product launch presentation?

Start a product launch presentation with a compelling story or statistic that highlights the need or opportunity for the product. Follow with an overview of the product, its features, benefits, and market potential, using engaging visuals to captivate the audience.

How do you introduce a product in a presentation?

Introduce a product by clearly defining the problem it solves, its unique value proposition, key features, and benefits. Use visuals and data to support your points and make the presentation partner introduction memorable.

How do you introduce a new product launch?

Introduce a new product launch by setting the stage with market insights, the inspiration behind the product, its differentiation points, and expected impact. Engage the audience with dynamic visuals and compelling data.

What are the five steps to launching a product?

The five steps to launching a product include market research, product development, creating a marketing plan, preparing the launch, and executing the launch strategy. Incorporating visual data presentations can enhance each step.

How much do presentation designers charge?

Presentation designers charge based on the project scope, with rates ranging from $50 to $200 per hour. Fixed project rates are also common, varying based on complexity and requirements.

What is a presentation design agency?

A presentation design agency specializes in creating visually appealing and effective presentation slides keynote presentations. They offer services like custom design, storytelling, and data visualization to enhance the impact of presentations.

Are presentation designers in demand?

Yes, presentation designers are in high demand, especially in fields requiring complex data visualization, persuasive storytelling, and professional branding in presentations.

Which company is best for presentation design?

The best company for presentation design depends on specific needs, but firms like Slide Genius, Presentation Design Co., and Buffalo 7 are highly regarded for their expertise and creativity in creating impactful corporate presentations.

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What Is a Chart & Why Is It Important for Businesses?

A well-known adage says, "a picture is worth a thousand words." Pictures can capture attention, quickly convey information and clarify complex concepts. And, research shows, images can be effective in business.

For instance, a study by BuzzSumo looking at more than 100 million Facebook updates found those with images had 2.3 times more engagement than those without. Still, the impact of pictures extends beyond social media. Another study from TechSmith found roughly two-thirds of people understand information better when it’s communicated visually.

Charts are frequently used in business settings and can help viewers quickly grasp the information being conveyed — a valuable benefit, given the volume of information everyone must process each day, especially in today’s hyper-connected world.

What Is a Chart?

A chart combines text, symbols and/or graphics to show the relationship between multiple data sets. They are a popular method for sharing information in different settings. For instance, a bar chart could show how sales of a particular flavor of ice cream have changed over the past five years. The length of each bar would indicate sales for that year.

Key Takeaways

• Charts enable you to visually compare multiple sets of data.
• Charts can help people better understand and remember information. Many people understand a picture more quickly than blocks of text.
• A compelling chart can help you make your point more convincingly and lend credibility to your presentation.

Charts Explained

What’s the difference between a chart and a graph? While the terms “chart” and “graph” may seem interchangeable, there are subtle differences. To start, graphs are a type of chart, but not all charts are graphs. Graphs focus on raw data and depict trends over time, which is not true of all types of charts.

In contrast, a chart conveys data sets in a format that helps viewers understand them. That is, the quality of the presentation helps determine the effectiveness of a chart.

Why Are Charts Important to Businesses?

Most business presentations include at least one chart, and often several. There’s a reason for this — because charts can quickly convey a great deal of information and help viewers remember the data presented, they enable businesspeople to more quickly make informed decisions and take action.

As companies become increasingly data-driven and monitor more data, charts have become an increasingly valuable tool. They help executives quickly get the pulse of their business, while department heads might use charts to examine the effectiveness of a new strategy or campaign and its impact on their team.

When and How Should Charts Be Used?

Charts are most valuable when you need to quickly convey the relationship between two or more sets of data and you’d like your audience to retain this information. They have countless uses, as modern businesses lean on data to shape their strategies and priorities.

For instance, a chart can quickly show how sales and/or expenses have changed for a business unit over a set period of time. Or it could display something more specific, like an increase or decline in the number of orders fulfilled per hour at a particular warehouse. We’ll show examples of the various types below.

Benefits of Businesses Using Charts in Data Visualization

By visually showing the relationship(s) between sets of data, a chart can help bring information to light in an easier-to-comprehend manner than reading blocks of text. This is a much better approach than showing lists or tables of numbers that force viewers to process the information and determine trends. Technology has made it easier and, in many cases, automated the creation of charts, so business leaders have come to rely on them when they make decisions.

For example, a bar chart that shows how expenses vary between two similar manufacturing plants can help management quickly pinpoint which expenses appear higher than they should. Armed with this information, they can look into the issue to find the cause and then identify actions to reduce expenses.

Parts of a Chart

While the various types of charts show information in different ways, most share a few common elements. These include the following:

The title should let viewers quickly grasp the data presented in the chart. An example: “Sales of Products A, B and C Over the Past Five Years.”

The Horizontal and Vertical Axes

With the exception of pie charts, most charts have vertical and horizontal axes. Each is used to present at least one set of data. Both also incorporate scales that indicate the starting and ending point of the information presented.

Horizontal Axis

The horizontal axis, also known as the x-axis, typically represents the value that’s known — often the time period. Say a chart shows ice cream sales over time. The years typically would go on the horizontal axis.

Vertical Axis

The vertical or y-axis usually shows the “unknown” value. In the above example of ice cream sales, the volume of ice cream sales for each of the relevant years would be shown on the vertical access.

Data Series

The data series refers to the information set presented in the chart. All charts are based on some type of data series. Returning to the example of ice cream sales, it would be the amount of ice cream sales each year.

A key or legend helps explain the information in the chart. Say the chart breaks out ice cream sales by region and uses differently colored bars for each region. The key would indicate which color correlated with which region.

There are five basic types of charts that people will come across regularly. They are line charts, bar charts, pie charts, column charts and area charts.

Common Types of Charts

Each type of chart has its pros and cons and will work better for certain use cases than others. Some of the more common types include:

• Line Graphs

Line graphs typically are used to show changes or trends in continuous data over a period of time, with a line connecting the dots that represent the different values. For instance, in a line chart showing a company's stock price over the past week, a line would connect the dots that visualize the change in price each day.

A bar graph, as you might guess, uses bars to convey data. The longer the bar, the greater the value. Say a bar graph shows sales per store for the past quarter. If sales at Store 100 were twice the sales at Store 200, its bar would be twice as long.

Bar graphs can be vertical or horizontal. One axis shows the categories; in the above example, this is the stores. The other shows the values, such as sales. Generally, horizontal bar charts allow you to compare more data sets.

Bar graphs are useful in showing disparate data sets, such as sales by store or of different flavors of ice cream. Each flavor can get its own bar. They also tend to work well when the changes you’re trying to convey are larger. If the changes are small, the differences in the bars' lengths might be too small to easily discern. They also can show negative numbers with bars below the x-axis.

Bubble Chart

Bubble charts can be an effective way to show how different values compare to each other, with larger bubbles representing larger values. For example, a chart showing the populations of the 10 largest cities in the world would include a larger bubble for Tokyo, Japan, which has about 37 million people, than for Beijing, China, which is home to about 19 million people.

An area chart is an offshoot of the line chart. It’s often used to compare several quantities, such as sales in different regions. After plotting a line that shows each region’s sales, the area between each of the lines is filled in with different colors.

Area charts tend to be most effective when you have several variables to compare, but typically no more than three or four. Any more than that, and deciphering the graph becomes difficult.

A pie chart is one way to display how various parts make up a whole or 100 percent. Each data point is represented by a "slice" of the pie; the slice's size correlates to the percentage of the whole it comprises. Say a bakery uses a pie chart to show the breakdown of muffins by flavor: blueberry (50%), almond poppy seed (25%) and banana nut (25%). In the chart, the slice for blueberry would be half the pie, while the slice for almond poppy seed and banana nut would each be one-quarter.

Pie charts tend to be most effective when each individual has their own copy of the chart. If you're presenting to a large group and using a whiteboard or screen, pie charts can be challenging to interpret.

A heat map uses color to depict data. While reporting on the COVID-19 pandemic, for instance, many news outlets used heat maps to show which parts of the country were experiencing outbreaks. States with severe outbreaks often were colored red, those with less severe outbreaks were orange and those with even fewer cases were yellow. Viewers could quickly assess the severity of the outbreaks in their state or region.

• Scatter Plots

Scatter plots use dots to show the relationship between two variables. For instance, you might compare the players' average height on 10 basketball teams against the number of games each won during the season. The horizontal axis would show the number of games won, and the vertical axis, each team's average height. If a team's average height is 6 feet, 4 inches and it won 25 games, you'd place a dot where those two variables on the axes intersect. As you fill out the chart for each team, you can determine how strongly height correlates with the number of games won.

In interpreting scatter plots, it's important to keep in mind that they show correlation, but not necessarily causation. Whether one variable — in the above example, team height — causes the other variable — games won — would require further investigation.

Dual Axis Chart

A dual axis chart allows you to show the relationship between two sets of variables. In a business setting, the two variable sets often are revenue and net profit. To show each by month over the past year, you’d use both vertical axes — sometimes referred to as the y- and y2-axes.

On the left axis or y-axis, you might show revenue in $1 million increments from $1 million to $10 million. Then you’d use the right or y2-axis to show profit, in $100,000 increments from $100,000 to $1 million.

Dual axis charts also often combine several chart types. In this example, you might use bar charts to show monthly revenue and a line chart to represent profit.

• Gantt Charts

Gantt charts are used to manage project schedules. The horizontal axis shows the time frame, while the tasks required to complete the project are on the vertical axis. The bar for each task correlates with its start and end date.

Radar Chart

Radar charts are one way to represent multiple variables. They’re also called spider or web charts due to their shapes, which often take the form of hexagons, octagons or similar forms. In other words, instead of two axes, radar charts typically incorporate between five and eight. They’re particularly effective at showing where data overlaps and where it differs.

You might use a radar chart to compare the strengths, such as size, market penetration and number of product lines, of different competitors. You'd rank each competitor on each attribute, with each angle joined in the chart referring to a specific attribute.

• Column Charts

These are similar to bar charts in that the length of each column corresponds with the value of the data it represents. They're well suited for comparing several sets of data over time. For example, you might use a column chart to show sales of both red T-shirts and blue T-shirts by month over the past year. Each month identified on the horizontal axis would incorporate two columns: one for the red T-shirts and another for the blue.

A pictograph uses pictures or symbols to display data. A pictograph with multiple rows of people in different colors could show how many people work in different departments at a company. For instance, all the people in green might work in sales and those in yellow in accounting.

• Venn Diagrams

Venn diagrams use overlapping circles to show how data sets overlap. The greater the degree of overlap between the data, the more the circles themselves will overlap. Say you want to show how many customers purchase Product A, Product B or both. Your data shows that 25% of those that purchase one product also buy the other. The circles would overlap by about 25%.

How to Create Basic Charts

To ensure your chart adds interest to your presentation and engages your audience or viewers, a few steps are key:

Assess the information you need to present. Review the various types of charts to identify the type that can most accurately convey this information in a way that’s easy to understand.

Consider your audience and how they'll use the information. For instance, charts used in a presentation to the executive team often need to quickly convey information that helps them make decisions. Conversely, a project manager would use a detailed Gantt chart to help keep a project on schedule.

Similarly, consider how you’ll be presenting the information. Pie charts, for instance, can be difficult to interpret if viewers are looking at them from a distance, especially if there are many narrow “slices.”

Use bright and distinct colors for the information you want to highlight. Red, for instance, conveys urgency — a reason it often was used to show the areas of the country experiencing the most serious COVID-19 outbreaks.

Resist any temptation to adjust a chart to make the results appear different than they are. Say a marketing promotion was expected to boost sales by 10%, but sales only increased by 3%. By adjusting the increments used on the vertical axis, it may be possible to make the sales increase look larger than it was. However, such maneuvers are misleading and once your audience realizes what you’ve done, you lose credibility.

Clear, compelling, and easy-to-read charts and data visualizations can strengthen and lend credibility to a presentation and help others understand complicated information more quickly. Most people understand that it’s much easier to process and hold onto information seen in an image rather than text, whether in their personal or professional lives. As data interpretation and management becomes a key responsibility for more employees, it’s critical that they learn how to create and use charts.

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How do I choose the best chart for my data?

What types of data correspond to which charts?

What are the 16 types of charts?

Here’s a list of 16 popular types of charts:

• Area Charts
• Bubble Charts
• Dual Axis Charts
• Flow Charts
• Gauge Charts
• Pictographs
• Radar Charts
• Waterfall Charts

How Do I Make a Google chart?

Google Charts helps you convert data to a chart. More information is available at https://developers.google.com/chart .

What is Cloud ERP and How Does It Work?

Cloud ERP is an enterprise resource planning (ERP) system that runs on a vendor’s cloud platform as opposed to an on-premises network, allowing organizations to access over the internet. ERP software integrates and…

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21 Best Data Visualization Types: Examples of Graphs and Charts Uses

Those who master different data visualization types and techniques (such as graphs, charts, diagrams, and maps) are gaining the most value from data.

Why? Because they can analyze data and make the best-informed decisions.

Whether you work in business, marketing, sales, statistics, or anything else, you need data visualization techniques and skills.

Graphs and charts make data much more understandable for the human brain.

On this page:

• What are data visualization techniques? Definition, benefits, and importance.
• 21 top data visualization types. Examples of graphs and charts with an explanation.
• When to use different data visualization graphs, charts, diagrams, and maps?
• How to create effective data visualization?
• 10 best data visualization tools for creating compelling graphs and charts.

What Are Data V isualization T echniques? Definition And Benefits.

Data visualization techniques are visual elements (like a line graph, bar chart, pie chart, etc.) that are used to represent information and data.

Big data hides a story (like a trend and pattern).

By using different types of graphs and charts, you can easily see and understand trends, outliers, and patterns in data.

They allow you to get the meaning behind figures and numbers and make important decisions or conclusions.

Data visualization techniques can benefit you in several ways to improve decision making.

Key benefits:

• Data is processed faster Visualized data is processed faster than text and table reports. Our brains can easily recognize images and make sense of them.
• Better analysis Help you analyze better reports in sales, marketing, product management, etc. Thus, you can focus on the areas that require attention such as areas for improvement, errors or high-performing spots.
• Faster decision making Businesses who can understand and quickly act on their data will gain more competitive advantages because they can make informed decisions sooner than the competitors.
• You can easily identify relationships, trends, patterns Visuals are especially helpful when you’re trying to find trends, patterns or relationships among hundreds or thousands of variables. Data is presented in ways that are easy to consume while allowing exploration. Therefore, people across all levels in your company can dive deeper into data and use the insights for faster and smarter decisions.
• No need for coding or data science skills There are many advanced tools that allow you to create beautiful charts and graphs without the need for data scientist skills . Thereby, a broad range of business users can create, visually explore, and discover important insights into data.

How Do Data Visualization Techniques work?

Data visualization techniques convert tons of data into meaningful visuals using software tools.

The tools can operate various types of data and present them in visual elements like charts, diagrams, and maps.

They allow you to easily analyze massive amounts of information, discover trends and patterns in data and then make data-driven decisions .

Why data visualization is very important for any job?

Each professional industry benefits from making data easier to understand. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPI s.

We need to constantly analyze and share data with our team or customers.

Having data visualization skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Before start using visuals, you must know…

Data visualization is one of the most important skills for the modern-day worker.

However, it’s not enough to see your data in easily digestible visuals to get real insights and make the right decisions.

• First : to define the information you need to present
• Second: to find the best possible visual to show that information

Don’t start with “I need a bar chart/pie chart/map here. Let’s make one that looks cool” . This is how you can end up with misleading visualizations that, while beautiful, don’t help for smart decision making.

Regardless of the type of data visualization, its purpose is to help you see a pattern or trend in the data being analyzed.

The goal is not to come up with complex descriptions such as: “ A’s sales were more than B by 5.8% in 2018, and despite a sales growth of 30% in 2019, A’s sales became less than B by 6.2% in 2019. ”

A good data visualization summarizes and presents information in a way that enables you to focus on the most important points.

Let’s go through 21 data visualization types with examples, outline their features, and explain how and when to use them for the best results.

21 Best Types Of Data Visualization With Examples And Uses

1. Line Graph

The line graph is the most popular type of graph with many business applications because they show an overall trend clearly and concisely.

What is a line graph?

A line graph (also known as a line chart) is a graph used to visualize the values of something over a specified period of time.

For example, your sales department may plot the change in the number of sales your company has on hand over time.

Data points that display the values are connected by straight lines.

When to use line graphs?

• When you want to display trends.
• When you want to represent trends for different categories over the same period of time and thus to show comparison.

For example, the above line graph shows the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

Here, you can see at a glance that the top-performing product over the years is product C, followed by Product B.

2. Bar Chart

At some point or another, you’ve interacted with a bar chart before. Bar charts are very popular data visualization types as they allow you to easily scan them for valuable insights.

And they are great for comparing several different categories of data.

What is a bar chart?

A bar chart (also called bar graph) is a chart that represents data using bars of different heights.

The bars can be two types – vertical or horizontal. It doesn’t matter which type you use.

The bar chart can easily compare the data for each variable at each moment in time.

For example, a bar chart could compare your company’s sales from this year to last year.

When to use a bar chart?

• When you need to compare several different categories.
• When you need to show how large data changes over time.

The above bar graph visualizes revenue by age group for three different product lines – A, B, and C.

You can see more granular differences between revenue for each product within each age group.

As different product lines are groups by age group, you can easily see that the group of 34-45-year-old buyers are the most valuable to your business as they are your biggest customers.

3. Column Chart

If you want to make side-by-side comparisons of different values, the column chart is your answer.

What is a column chart?

A column chart is a type of bar chart that uses vertical bars to show a comparison between categories.

If something can be counted, it can be displayed in a column chart.

Column charts work best for showing the situation at a point in time (for example, the number of products sold on a website).

Their main purpose is to draw attention to total numbers rather than the trend (trends are more suitable for a line chart).

When to use a column chart?

• When you need to show a side-by-side comparison of different values.
• When you want to emphasize the difference between values.
• When you want to highlight the total figures rather than the trends.

For example, the column chart above shows the traffic sources of a website. It illustrates direct traffic vs search traffic vs social media traffic on a series of dates.

The numbers don’t change much from day to day, so a line graph isn’t appropriate as it wouldn’t reveal anything important in terms of trends.

The important information here is the concrete number of visitors coming from different sources to the website each day.

4. Pie Chart

Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes.

What is a pie chart?

A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data.

A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole.

The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole.

When to use a pie chart?

• When you want to represent the share each value has of the whole.
• When you want to show how a group is broken down into smaller pieces.

The above pie chart shows which traffic sources bring in the biggest share of total visitors.

You see that Searches is the most effective source, followed by Social Media, and then Links.

At a glance, your marketing team can spot what’s working best, helping them to concentrate their efforts to maximize the number of visitors.

5. Area Chart 

If you need to present data that depicts a time-series relationship, an area chart is a great option.

What is an area chart?

An area chart is a type of chart that represents the change in one or more quantities over time. It is similar to a line graph.

In both area charts and line graphs, data points are connected by a line to show the value of a quantity at different times. They are both good for showing trends.

However, the area chart is different from the line graph, because the area between the x-axis and the line is filled in with color. Thus, area charts give a sense of the overall volume.

Area charts emphasize a trend over time. They aren’t so focused on showing exact values.

Also, area charts are perfect for indicating the change among different data groups.

When to use an area chart?

• When you want to use multiple lines to make a comparison between groups (aka series).
• When you want to track not only the whole value but also want to understand the breakdown of that total by groups.

In the area chart above, you can see how much revenue is overlapped by cost.

Moreover, you see at once where the pink sliver of profit is at its thinnest.

Thus, you can spot where cash flow really is tightest, rather than where in the year your company simply has the most cash.

Area charts can help you with things like resource planning, financial management, defining appropriate storage space, and more.

6. Scatter Plot

The scatter plot is also among the popular data visualization types and has other names such as a scatter diagram, scatter graph, and correlation chart.

Scatter plot helps in many areas of today’s world – business, biology, social statistics, data science and etc.

What is a Scatter plot?

Scatter plot is a graph that represents a relationship between two variables . The purpose is to show how much one variable affects another.

Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable.

But it is also possible to have no relationship between 2 variables at all.

When to use a Scatter plot?

• When you need to observe and show relationships between two numeric variables.
• When just want to visualize the correlation between 2 large datasets without regard to time.

The above scatter plot illustrates the relationship between monthly e-commerce sales and online advertising costs of a company.

At a glance, you can see that online advertising costs affect monthly e-commerce sales.

When online advertising costs increase, e-commerce sales also increase.

Scatter plots also show if there are unexpected gaps in the data or if there are any outlier points.

7. Bubble chart

If you want to display 3 related dimensions of data in one elegant visualization, a bubble chart will help you.

What is a bubble chart?

A bubble chart is like an extension of the scatter plot used to display relationships between three variables.

The variables’ values for each point are shown by horizontal position, vertical position, and dot size.

In a bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z).

When to use a bubble chart?

• When you want to depict and show relationships between three variables.

The bubble chart above illustrates the relationship between 3 dimensions of data:

• Cost (X-Axis)
• Profit (Y-Axis)
• Probability of Success (%) (Bubble Size).

Bubbles are proportional to the third dimension – the probability of success. The larger the bubble, the greater the probability of success.

It is obvious that Product A has the highest probability of success.

8. Pyramid Graph

Pyramid graphs are very interesting and visually appealing graphs. Moreover, they are one of the most easy-to-read data visualization types and techniques.

What is a pyramid graph?

It is a graph in the shape of a triangle or pyramid. It is best used when you want to show some kind of hierarchy. The pyramid levels display some kind of progressive order, such as:

• More important to least important. For example, CEOs at the top and temporary employees on the bottom level.
• Specific to least specific. For example, expert fields at the top, general fields at the bottom.
• Older to newer.

When to use a pyramid graph?

• When you need to illustrate some kind of hierarchy or progressive order

Image Source: Conceptdraw

The above is a 5 Level Pyramid of information system types that is based on the hierarchy in an organization.

It shows progressive order from tacit knowledge to more basic knowledge. Executive information system at the top and transaction processing system on the bottom level.

The levels are displayed in different colors. It’s very easy to read and understand.

9. Treemaps

Treemaps also show a hierarchical structure like the pyramid graph, however in a completely different way.

What is a treemap?

Treemap is a type of data visualization technique that is used to display a hierarchical structure using nested rectangles.

Data is organized as branches and sub-branches. Treemaps display quantities for each category and sub-category via a rectangle area size.

Treemaps are a compact and space-efficient option for showing hierarchies.

They are also great at comparing the proportions between categories via their area size. Thus, they provide an instant sense of which data categories are the most important overall.

When to use a treemap?

• When you want to illustrate hierarchies and comparative value between categories and subcategories.

Image source: Power BI

For example, let’s say you work in a company that sells clothing categories: Urban, Rural, Youth, and Mix.

The above treemap depicts the sales of different clothing categories, which are then broken down by clothing manufacturers.

You see at a glance that Urban is your most successful clothing category, but that the Quibus is your most valuable clothing manufacturer, across all categories.

10. Funnel chart

Funnel charts are used to illustrate optimizations, specifically to see which stages most impact drop-off.

Illustrating the drop-offs helps to show the importance of each stage.

What is a funnel chart?

A funnel chart is a popular data visualization type that shows the flow of users through a sales or other business process.

It looks like a funnel that starts from a large head and ends in a smaller neck. The number of users at each step of the process is visualized from the funnel width as it narrows.

A funnel chart is very useful for identifying potential problem areas in the sales process.

When to use a funnel chart?

• When you need to represent stages in a sales or other business process and show the amount of revenue for each stage.

Image Source: DevExpress

This funnel chart shows the conversion rate of a website.

The conversion rate shows what percentage of all visitors completed a specific desired action (such as subscription or purchase).

The chart starts with the people that visited the website and goes through every touchpoint until the final desired action – renewal of the subscription.

You can see easily where visitors are dropping out of the process.

11. Venn Diagram 

Venn diagrams are great data visualization types for representing relationships between items and highlighting how the items are similar and different.

What is a Venn diagram?

A Venn Diagram is an illustration that shows logical relationships between two or more data groups. Typically, the Venn diagram uses circles (both overlapping and nonoverlapping).

Venn diagrams can clearly show how given items are similar and different.

Venn diagram with 2 and 3 circles are the most common types. Diagrams with a larger number of circles (5,6,7,8,10…) become extremely complicated.

When to use a Venn diagram?

• When you want to compare two or more options and see what they have in common.
• When you need to show how given items are similar or different.
• To display logical relationships from various datasets.

The above Venn chart clearly shows the core customers of a product – the people who like eating fast foods but don’t want to gain weight.

The Venn chart gives you an instant understanding of who you will need to sell.

Then, you can plan how to attract the target segment with advertising and promotions.

12. Decision Tree

As graphical representations of complex or simple problems and questions, decision trees have an important role in business, finance, marketing, and in any other areas.

What is a decision tree?

A decision tree is a diagram that shows possible solutions to a decision.

It displays different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s why it is called a decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

When to use a decision tree?

• When you need help in making decisions and want to display several possible solutions.

Imagine you are an IT project manager and you need to decide whether to start a particular project or not.

You need to take into account important possible outcomes and consequences.

The decision tree, in this case, might look like the diagram above.

13. Fishbone Diagram

Fishbone diagram is a key tool for root cause analysis that has important uses in almost any business area.

It is recognized as one of the best graphical methods to understand and solve problems because it takes into consideration all the possible causes.

What is a fishbone diagram?

A fishbone diagram (also known as a cause and effect diagram, Ishikawa diagram or herringbone diagram) is a data visualization technique for categorizing the potential causes of a problem.

The main purpose is to find the root cause.

It combines brainstorming with a kind of mind mapping and makes you think about all potential causes of a given problem, rather than just the one or two.

It also helps you see the relationships between the causes in an easy to understand way.

When to use a fishbone diagram?

• When you want to display all the possible causes of a problem in a simple, easy to read graphical way.

Let’s say you are an online marketing specialist working for a company witch experience low website traffic.

You have the task to find the main reasons. Above is a fishbone diagram example that displays the possible reasons and can help you resolve the situation.

14. Process Flow Diagram

If you need to visualize a specific process, the process flow diagram will help you a lot.

What is the process flow diagram?

As the name suggests, it is a graphical way of describing a process, its elements (steps), and their sequence.

Process flow diagrams show how a large complex process is broken down into smaller steps or tasks and how these go together.

As a data visualization technique, it can help your team see the bigger picture while illustrating the stages of a process.

When to use a process flow diagram?

• When you need to display steps in a process and want to show their sequences clearly.

The above process flow diagram shows clearly the relationship between tasks in a customer ordering process.

The large ordering process is broken down into smaller functions and steps.

15. Spider/Radar Chart

Imagine, you need to rank your favorite beer on 8 aspects (Bitterness, Sweetness, Sourness, Saltiness, Hop, Malt, Yeast, and Special Grain) and then show them graphically. You can use a radar chart.

What is a radar chart?

Radar chart (also called spider, web, and polar bar) is a popular data visualization technique that displays multivariate data.

In can compare several items with many metrics of characteristics.

To be effective and clear, the radar chart should have more than 2 but no more than 6 items that are judged.

When to use a radar chart?

• When you need to compare several items with more than 5 metrics of characteristics.

The above radar chart compares employee’s performance with a scale of 1-5 on skills such as Communications, Problem-solving, Meeting deadlines, Technical knowledge, Teamwork.

A point that is closer to the center on an axis shows a lower value and a worse performance.

It is obvious that Mary has a better performance than Linda.

16. Mind Map

Mind maps are beautiful data visuals that represent complex relationships in a very digestible way.

What is a mind map?

A mind map is a popular diagram that represents ideas and concepts.

It can help you structure your information and analyze, recall, and generate new ideas.

It is called a mind map because it is structured in a way that resembles how the human brain works.

And, best of all, it is a fun and artistic data visualization technique that engages your brain in a much richer way.

When to use a mind map?

• When you want to visualize and connect ideas in an easy to digest way.
• When you want to capture your thoughts/ideas and bring them to life in visual form.

Image source: Lucidchart

The above example of a mind map illustrates the key elements for running a successful digital marketing campaign.

It can help you prepare and organize your marketing efforts more effectively.

17. Gantt Chart

A well-structured Gantt chart aids you to manage your project successfully against time.

What is a Gantt chart?

Gantt charts are data visualization types used to schedule projects by splitting them into tasks and subtasks and putting them on a timeline.

Each task is listed on one side of the chart. This task also has a horizontal line opposite it representing the length of the task.

By displaying tasks with the Gantt chart, you can see how long each task will take and which tasks will overlap.

Gantt charts are super useful for scheduling and planning projects.

They help you estimate how long a project should take and determine the resources needed.

They also help you plan the order in which you’ll complete tasks and manage the dependencies between tasks.

When to use a Gantt chart?

• When you need to plan and track the tasks in project schedules.

Image Source: Aha.io

The above example is a portfolio planning Gantt Chart Template that illustrates very well how Gantt Charts work.

It visualizes the release timeline for multiple products for an entire year.

It shows also dependencies between releases.

You can use it to help team members understand the release schedule for the upcoming year, the duration of each release, and the time for delivering.

This helps you in resource planning and allows teams to coordinate implementation plans.

18. Organizational Charts

Organizational charts are data visualization types widely used for management and planning.

What is an organizational chart?

An organizational chart (also called an org chart) is a diagram that illustrates a relationship hierarchy.

The most common application of an org chart is to display the structure of a business or other organization.

Org charts are very useful for showing work responsibilities and reporting relationships.

They help leaders effectively manage growth or change.

Moreover, they show employees how their work fits into the company’s overall structure.

When to use the org chart?

• When you want to display a hierarchical structure of a department, company or other types of organization.

Image Source: Organimi

The above hierarchical org chart illustrates the chain of command that goes from the top (e.g., the CEOs) down (e.g., entry-level and low-level employees) and each person has a supervisor.

It clearly shows levels of authority and responsibility and who each person reports to.

It also shows employees the career paths and chances for promotion.

19. Area Map

Most business data has a location. Revenue, sales, customers, or population are often displayed with a dimensional variable on a map.

What is an area map?

It is a map that visualizes location data.

They allow you to see immediately which geographical locations are most important to your brand and business.

Image Source: Infogram

The map above depicts sales by location and the color indicates the level of sales (the darker the blue, the higher the sales).

These data visualization types are very useful as they show where in the world most of your sales are from and where your most valuable sales are from.

Insights like these illustrate weaknesses in a sales and marketing strategy in seconds.

20. Infographics

In recent years, the use of infographics has exploded in almost every industry.

From sales and marketing to science and healthcare, infographics are applied everywhere to present information in a visually appealing way.

What is an infographic?

Infographics are specific data visualization types that combine images, charts, graphs, and text. The purpose is to represent an easy-to-understand overview of a topic.

However, the main goal of an infographic is not only to provide information but also to make the viewing experience fun and engaging for readers.

It makes data beautiful—and easy to digest.

When you want to represent and share information, there are many data visualization types to do that – spreadsheets, graphs, charts, emails, etc.

But when you need to show data in a visually impactful way, the infographic is the most effective choice.

When to use infographics?

• When you need to present complex data in a concise, highly visually-pleasing way.

Image Source: Venngage

The above statistical infographic represents an overview of Social Buzz’s biggest social platforms by age and geography.

For example, we see that 75% of active Facebook users are 18-29 years old and 48% of active users live in North America.

21. T-Chart

If you want to compare and contrast items in a table form, T-Chart can be your solution.

What is a T-Chart?

A T-Chart is a type of graphic organizer in the shape of the English letter “T”. It is used for comparison by separating information into two or more columns.

You can use T-Chart to compare ideas, concepts or solutions clearly and effectively.

T-Charts are often used for comparison of pros and cons, facts and opinions.

By using T-Chart, you can list points side by side, achieve a quick, at-a-glance overview of the facts, and arrive at conclusions quickly and easily.

When to use a T-Chart?

• When you need to compare and contrast two or more items.
• When you want to evaluate the pros and cons of a decision.

The above T-Chart example clearly outlines the cons and pros of hiring a social media manager in a company.

10 Best Data Visualization Tools

There is a broad range of data visualization tools that allow you to make fascinating graphs, charts, diagrams, maps, and dashboards in no time.

They vary from BI (Business Intelligence) tools with robust features and comprehensive dashboards to more simple software for just creating graphs and charts.

Here we’ve collected some of the most popular solutions. They can help you present your data in a way that facilitates understanding and decision making.

1. Visme is a data presentation and visualization tool that allows you to create stunning data reports. It provides a great variety of presentation tools and templates for a unique design.

2. Infogram is a chart software tool that provides robust diagram-making capabilities. It comes with an intuitive drag-and-drop editor and ready-made templates for reports. You can also add images for your reports, icons, GIFs, photos, etc.

3. Venngage is an infographic maker. But it also is a great chart software for small businesses because of its ease of use, intuitive design, and great templates.

4. SmartDraw is best for those that have someone graphic design skills. It has a slightly more advanced design and complexity than Venngage, Visme, and Infogram, … so having some design skills is an advantage. It’s a drawing tool with a wide range of charts, diagrams, maps, and well-designed templates.

5. Creately is a dynamic diagramming tool that offers the best free version. It can be deployed from the cloud or on the desktop and allows you to create your graphs, charts, diagrams, and maps without any tech skills.

6. Edraw Max is an all-in-one diagramming software tool that allows you to create different data visualization types at a high speed. These include process flow charts, line graphs, org charts, mind maps, infographics, floor plans, network diagrams, and many others. Edraw Max has a wide selection of templates and symbols, letting you to rapidly produce the visuals you need for any purpose.

7. Chartio is an efficient business intelligence tool that can help you make sense of your company data. Chartio is simple to use and allows you to explore all sorts of information in real-time.

8. Sisense – a business intelligence platform with a full range of data visualizations. You can create dashboards and graphical representations with a drag and drop user interface.

9. Tableau – a business intelligence system that lets you quickly create, connect, visualize, and share data seamlessly.

10. Domo is a cloud business intelligence platform that helps you examine data using graphs and charts. You can conduct advanced analysis and create great interactive visualization.

Data visualization techniques are vital components of data analysis, as they can summarize large amounts of data effectively in an easy to understand graphical form.

There are countless data visualization types, each with different pros, cons, and use cases.

The trickiest part is to choose the right visual to represent your data.

Your choice depends on several factors – the kind of conclusion you want to draw, your audience, the key metrics, etc.

I hope the above article helps you understand better the basic graphs and their uses.

When you create your graph or diagram, always remember this:

A good graph is the one reduced to its simplest and most elegant form without sacrificing what matters most – the purpose of the visual.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Blog Data Visualization

10 Data Presentation Examples For Strategic Communication

By Krystle Wong , Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 Apr 2024 • 11 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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Data Presentation - Pie Charts

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A pie chart is a type of graph in which a circle is divided into sectors that each represents a proportion of the whole. Pie charts are a useful way to organize data in order to see the size of components relative to the whole, and are particularly good at showing percentage or proportional data. While pie charts are popular data representations, they can be hard to read, and it can be difficult to compare data from one pie chart to another. Pie charts are a useful way to visualize information that might be presented in a small table.

source: ers.usda.gov

The pie chart above shows the distribution of fluid milk in the U.S. in 2013. Note: the percentages in the names of the milk, "plain 1% milk," "plain 2% milk," etc. indicate different types of milk.

The entire circle encompasses all of the fluid milk, and the sectors represent specific products that use a percentage of that milk. For example, 35% of fluid milk was used in plain 2% milk, and 1% of fluid milk was used in eggnog and buttermilk.

This milk data is well represented in a pie chart since there are a small number of categories (types of milk) that can be modeled as a portion of a whole (total amount of milk).

Say the pie chart above represents the breakdown of 100 gallons of milk. Create a 2-column table that lists the type of milk in the first column and the number of gallons of each type of milk in the second column. Show Answer Type of Milk Number of Gallons Plain 2% 35 Plain 1% 14 Skim 14 Plain Whole 27 Flavored 9 Eggnog and Buttermilk 1 If there are 100 gallons of milk, and 27% of that milk is whole milk, then 27 gallons is whole milk. If 14% of the milk is skim milk, 14 percent of 100 gallons is 14 gallons, and so on.

Making a Pie Chart

Pie charts are good for showing how the size of one part relates to the whole.

To make a pie chart, separate the data into components. In the pie chart above, the data is about all fluid milk in the U.S., and the categories are plain 2% milk, plain whole milk, plain 1% milk, skim milk, flavored milk, and eggnog and buttermilk. Next, determine the ratio or percentage that each component takes up out of the whole. The total sum of percentages should sum to 100%. Divide the circle into proportional sectors. If a particular component made up 25% of the whole, a quarter of the circle would represent that component. Color is often used to differentiate sectors.

Good practices for making pie charts: Title the chart. Use labels to denote categories. If ratios are unclear, label sectors with the percentages they represent. Use different colors for each sector. Don't repeat colors. Use accurate sizing when making sectors. For example, a sector representing 50% of the whole should take up exactly half of the circle. Make sure that the number of slices is small enough that the reader can make sense of the chart.

What is wrong with this pie chart? source: wikipedia Show Answer This pie chart has too many sectors. It is hard to see how sectors compare with one another.

What could be improved about this pie chart? This pie chart describes the distribution of native English speakers by country. source: wikipedia Show Answer This pie chart could benefit from a descriptive title, and it might be helpful to include labels for the percentages of each sector as the “Canada,” “Other,” and “Australia” sectors look roughly the same size.

What food group has about the same daily calories per capita as grain products?

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• E-books in the U.S. ›

E-Books Still No Match for Printed Books

E-books vs. printed books.

Happy World Book Day! While UNESCO's General Conference probably thought of ink on paper when it first celebrated the event in 1995, some 21st century book lovers have moved onto enjoying the pastime in the electronic form. In the following chart, we compare just how popular e-books are versus those in print.

According to data from Statista’s Market Insights: Media & Advertising , e-book penetration still trails that of printed books in the vast majority of countries around the world. In the United States for example, 20 percent of the population are estimated to have purchased an e-book last year, compared to 30 percent who bought a printed book. China is the only country of those studied that saw the opposite trend, with only 24 percent of people having bought a printed book in the 12 months prior to the survey, while around 27 percent of people bought an e-book in that time frame.

Looking at forecasts for the book market on a worldwide scale, Statista analysts predict that while e-books have grown in popularity, they will not be the final nail in the coffin of printed books but rather a complementary product that should ultimately benefit the publishing industry.

Description

This chart shows the estimated share of the population in selected countries that purchased an e-book / a printed book in 2023.

Can I integrate infographics into my blog or website?

Yes, Statista allows the easy integration of many infographics on other websites. Simply copy the HTML code that is shown for the relevant statistic in order to integrate it. Our standard is 660 pixels, but you can customize how the statistic is displayed to suit your site by setting the width and the display size. Please note that the code must be integrated into the HTML code (not only the text) for WordPress pages and other CMS sites.

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What the New Overtime Rule Means for Workers

One of the basic principles of the American workplace is that a hard day’s work deserves a fair day’s pay. Simply put, every worker’s time has value. A cornerstone of that promise is the  Fair Labor Standards Act ’s (FLSA) requirement that when most workers work more than 40 hours in a week, they get paid more. The  Department of Labor ’s new overtime regulation is restoring and extending this promise for millions more lower-paid salaried workers in the U.S.

Overtime protections have been a critical part of the FLSA since 1938 and were established to protect workers from exploitation and to benefit workers, their families and our communities. Strong overtime protections help build America’s middle class and ensure that workers are not overworked and underpaid.

Some workers are specifically exempt from the FLSA’s minimum wage and overtime protections, including bona fide executive, administrative or professional employees. This exemption, typically referred to as the “EAP” exemption, applies when: 

1. An employee is paid a salary,  

2. The salary is not less than a minimum salary threshold amount, and 

3. The employee primarily performs executive, administrative or professional duties.

While the department increased the minimum salary required for the EAP exemption from overtime pay every 5 to 9 years between 1938 and 1975, long periods between increases to the salary requirement after 1975 have caused an erosion of the real value of the salary threshold, lessening its effectiveness in helping to identify exempt EAP employees.

The department’s new overtime rule was developed based on almost 30 listening sessions across the country and the final rule was issued after reviewing over 33,000 written comments. We heard from a wide variety of members of the public who shared valuable insights to help us develop this Administration’s overtime rule, including from workers who told us: “I would love the opportunity to...be compensated for time worked beyond 40 hours, or alternately be given a raise,” and “I make around $40,000 a year and most week[s] work well over 40 hours (likely in the 45-50 range). This rule change would benefit me greatly and ensure that my time is paid for!” and “Please, I would love to be paid for the extra hours I work!”

The department’s final rule, which will go into effect on July 1, 2024, will increase the standard salary level that helps define and delimit which salaried workers are entitled to overtime pay protections under the FLSA. 

Starting July 1, most salaried workers who earn less than $844 per week will become eligible for overtime pay under the final rule. And on Jan. 1, 2025, most salaried workers who make less than $1,128 per week will become eligible for overtime pay. As these changes occur, job duties will continue to determine overtime exemption status for most salaried employees.

The rule will also increase the total annual compensation requirement for highly compensated employees (who are not entitled to overtime pay under the FLSA if certain requirements are met) from $107,432 per year to $132,964 per year on July 1, 2024, and then set it equal to $151,164 per year on Jan. 1, 2025.

Starting July 1, 2027, these earnings thresholds will be updated every three years so they keep pace with changes in worker salaries, ensuring that employers can adapt more easily because they’ll know when salary updates will happen and how they’ll be calculated.

The final rule will restore and extend the right to overtime pay to many salaried workers, including workers who historically were entitled to overtime pay under the FLSA because of their lower pay or the type of work they performed. 

We urge workers and employers to visit  our website to learn more about the final rule.

Jessica Looman is the administrator for the U.S. Department of Labor’s Wage and Hour Division. Follow the Wage and Hour Division on Twitter at  @WHD_DOL  and  LinkedIn .  Editor's note: This blog was edited to correct a typo (changing "administrator" to "administrative.")

• Wage and Hour Division (WHD)
• Fair Labor Standards Act
• overtime rule

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