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Visualizations That Really Work

• Scott Berinato

Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.

By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz.

This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

Know what message you’re trying to communicate before you get down in the weeds.

Idea in Brief

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine…

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it’s easy to create terrible charts. Visualization can be so much more: It’s an agile, powerful way to explore ideas and communicate information.

…But Strategy Is Key

Don’t jump straight to execution. Instead, first think about what you’re representing—ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

• Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts Workbook: Tips Tools, and Exercises for Making Better Data Visualizations and Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations .

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

Published: May 22, 2024

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

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

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

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

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

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

Free Excel Graph Templates

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Charts vs Graphs: What's the Difference?

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

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

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

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

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

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

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

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

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

Image source

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

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

Different Types of Graphs for Data Visualization

1. bar graph.

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

Best Use Cases for These Types of Graphs

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

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

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

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

Other use cases for bar graphs include:

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

Design Best Practices for Bar Graphs

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

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

2. Line Graph

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

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

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

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

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

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

Design Best Practices for Line Graphs

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

3. Bullet Graph

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

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

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

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

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

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

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

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

Design Best Practices for Bullet Graphs

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

4. Column + Line Graph

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

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

Best Use Cases

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

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

Design Best Practices 

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

The Four Basic Types of Charts

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

1. Bar Chart

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

2. Line Chart 

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

3. Scatter Plot

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

4. Pie Chart

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

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

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

Different Types of Charts for Data Visualization

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

1. Column Chart

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

Best Use Cases for This Type of Chart

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

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

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

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

Design Best Practices for Column Charts

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

2. Area Chart

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

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

It helps me analyze both overall and individual trend information.

Best Use Cases for These Types of Charts

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

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

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

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

Area charts  can help your business to:

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

Design Best Practices for Area Charts

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

3. Stacked Bar Chart

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

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

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

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

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

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

You can also use these charts to:

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

Design Best Practices for Stacked Bar Charts

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

4. Mekko Chart

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

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

Image Source

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

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

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

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

Other use cases for Mekko charts include:

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

Design Best Practices for Mekko Charts

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

5. Pie Chart

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

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

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

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

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

Design Best Practices for Pie Charts

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

6. Scatter Plot Chart

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

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

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

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

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

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

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

Design Best Practices for Scatter Plots

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

7. Bubble Chart

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

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

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

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

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

You can also use bubble charts for:

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

Design Best Practices for Bubble Charts

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

8. Waterfall Chart

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

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

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

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

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

Other stellar options for these types of charts include:

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

Design Best Practices for Funnel Charts

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

10. Heat Map

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

Best Use Cases for Heat Maps

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

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

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

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

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

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

Design Best Practices for Heat Map

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

11. Gantt Chart

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

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

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

This chart type allows you to:

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

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

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

Design Best Practices for Donut Charts 

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

How to Choose the Right Chart or Graph for Your Data

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

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

1. Identify your goals for presenting the data.

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

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

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

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

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

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

3. Gather your data.

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

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

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

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

Fill out the form to get your templates.

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

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

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

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

Download the Excel templates mentioned in the video here.

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

1. do you want to compare values.

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

• Scatter plot

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

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

To show composition, use these charts:

• Stacked bar

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

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

Use these charts to show distribution:

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

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

You should choose one of the following:

• Dual-axis line

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

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

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

Featured Resource: The Marketer's Guide to Data Visualization

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How to Visualize Data using Year-Over-Year Growth Chart?

Some visualization designs are better suited for visualizing year-over-year (YoY) data than others. In other words, they display insights in a clear and easy-to-follow way.

Some of the tested and proven year-over-year growth visualization designs include:

• Waterfall Chart
• Double Axis Line and Graph Chart
• Comparison Bar Chart
• Double Axis Line Chart
• Matrix Chart
• Pareto Bar Chart
• Radar Chart
• Sentiment Trend Chart
• Slope Chart
• Tornado Chart
• Progress Chart

The charts highlighted above are amazingly easy to read and interpret, even for non-technical audiences.

Excel has very basic year-over-year growth charts (highlighted above). Yes, the spreadsheet tool comes with pretty Year-over-year Growth Charts that need more time and effort in editing.

You don’t have to do away with Excel. You can supercharge it by installing third-party add-ins to access ready-to-use and visually appealing year-over-year growth charts.

In this blog, you’ll learn the following:

Table of Content:

• What is Year-Over-year Growth Chart?

How to Calculate Year-Over-Year Growth?

• Best Charts for Presenting Year-Over-Year Growth

How to Create a Year-Over-Year Growth Chart in Excel?

Why do we need a year-over-year growth chart.

Before diving into the how-to guide, we’ll address the following question: What is a Year-Over-Year Growth Chart?

What is a Year-Over-year Growth Chart?

Definition : The year-over-year (YOY) Growth Chart showcases the key performance indicators for comparing growth in a financial year to a previous one.

Unlike standalone monthly metrics, YOY gives you a picture of your performance without seasonal effects, monthly volatility, and other factors. You see a clearer picture of your actual successes and challenges over time. Unsurprisingly, this is a key metric for retail analytics.

One of the key advantages of the year-over-year growth chart is eliminating seasonality from your growth metrics.

Most retailers see a sharp uptick in sales during the holiday season. A single-month basis can give a false indication of massive growth. However, these inflated numbers aren’t truly representative of growth over time if they return to normal levels after the holidays pass.

Comparing similar periods over time gives you a more precise measure of your company’s growth. That’s not to say that YoY metrics are the be-all, end-all of analysis. Focusing on 12 months may also present you with a broader picture.

Combining a longer-term perspective with month-over-month and quarter-over-quarter can help you gain 360-degree insights into your data. Additionally, incorporating a dot plot can provide a clear and concise visualization of individual data points across periods, enhancing your overall analysis.

The year-over-year growth chart is more than just revenue.

You can measure myriad aspects of your growth: conversions, average sale value, and other related metrics.

In the coming section, we’ll cover how to calculate year-over-year growth.

You can calculate year-over-year growth by following a particular formula, which we’ll highlight below.

Follow the steps below to calculate year-over-year growth.

Determine the timeframe you’d like to compare

Before you begin your equation, determine a suitable time. If you’ve just completed a successful season, compare your previous quarter with last year’s.

Also, compare this year’s monthly statistics with last year’s to uncover growth or decline.

Retrieve your company’s numbers from the current and previous years:

Once you’ve established the timeframe, gather data for analysis.

Check your company’s balance sheet if you aren’t sure where these results may be located. This should list your previous performance and financial information for your company.

Subtract last year’s numbers from this year

If you have both numbers, find your growth rate by subtracting last year’s performance numbers from this year’s.

Divide the total by last year’s number

Take the total number from the previous equation and divide it by last year’s number.

Using the earlier example, take the equation’s total, 70, and divide it by 430. The resulting figure is (70 ÷ 430) = 0.1627.

Multiply by 100 to get the final percentage:

The final step of calculating the year-over-year growth chart rate is to convert this total to a percentage.

Multiplying the resulting total number by 100.

Here’s your equation: 0.1627 x 100 = 16.27. (16.3%)

Analyze and evaluate your total:

You can now use this total when showing your success to investors or lenders. This can help them understand how well your business is performing. You can use this number to detail how you plan to perform a higher YOY growth rate next year.

If your total was a negative number and appears as a loss rather than a growth, you can use this to determine the improvements needed to drive a positive result during your next year-over-year growth chart.

In the coming section, we’ll highlight the best charts for presenting year-over-year growth.

The Tested and Proven Best Charts for Presenting Year-Over-Year Growth

A Waterfall Chart for “Year-Over-Year Growth” is a graphical representation that illustrates the change in performance metrics from one year to the next. Each bar in the chart represents a specific metric, such as revenue or profit, for a particular year. The bars are stacked sequentially, with each segment indicating the amount of change from the previous year.

A Comparison Bar Chart  (one of the Year-over-year Growth Charts) uses a bar to represent sections of the same category, and these bars are placed adjacent to each other.

It’s a great way of comparing the data visually. Bar graphs are reliable ways of comparing key data points.

Double Axis Line Graph and Bar Chart

A Dual Axis Bar and Line Graph is one of the best year-over-year growth charts for comparing two data sets for a presentation.

The visualization design uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement.

The relationship between two variables is referred to as correlation. A Dual Axis Bar and Line Chart illustrate plenty of information using limited space, so you can discover trends you may have otherwise missed.

Dual Axis Line Chart

A Dual Axis Line Chart is one of the best graphs for presenting growth year-over-year. The chart has a secondary y-axis to help you display insights into two varying data points.

More so, it uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement.

The visualization design displays data as an arrangement of information in a series of data points called ‘markers’ connected by straight line segments. You can use the chart to visualize a trend in the data over time intervals. In a typical line chart, you have an x and y-axis. The dual axes line chart features one x-axis and two y-axis.

A Matrix Chart can help you identify the presence and strengths of relationships between two or more lists of items. Besides, it provides a compact way of representing many-to-many relationships of varying strengths.

Use this chart to analyze and understand the relationships between data sets.

A Pareto Bar Chart is a graph that indicates the frequency of defects and their cumulative impact.

The chart is useful, especially in hunting for defects to achieve maximum overall improvement. The key goal of a Pareto Diagram (one of the different types of charts for representing data ) is to separate the significant aspects of a problem from the trivial ones.

Pareto Chart is based on the classic 80/20 rule. The rule says that 20% of the causal factors result in 80% of the overall outcomes. For instance, 80% of the world’s total wealth is held by 20% of the population.

This easy-to-read chart prevents you from attacking the causes randomly by uncovering the top 20% of the problems, negatively affecting 80% of your overall performance.

A Radar Chart is a two-dimensional chart showing at least three variables on an axis that starts from the same point.

The chart is straightforward to understand and customize. Furthermore, you can show several metrics across a single dimension. The visualization design is best-suited for showing outliers and commonalities in your data.

You can use radar charts in excel and google sheets to display performance metrics such as clicks, sessions, new users, and page views, among others.

A Sentiment Trend Chart is one of the Year-over-year Growth Charts examples you can use to display the target market’s opinion of your offerings.

The chart is a must-have, especially if your goal is to show the growth and decline of key variables. The line curve in the chart shows the overall pattern and trend of a key variable over a specified period.

A Slope Chart is one of the Year-over-year Growth Charts that show transitions, changes over time, absolute values, and even rankings.

You can use this chart to show the before and after story of variables in your data.

Slope Graphs in Excel & Google Sheets can be useful when you have two time periods or points of comparison and want to show relative increases or decreases quickly across various categories between two data points.

A Progress Bar Chart is a visualization design that displays the progress made in a task or project. You can use the chart to monitor and prioritize your objectives, providing critical data for strategic decision-making.

Besides, it uses filled bars to display how much of the planned activity or goal has been completed.

The chart is significant, especially in tasks that require continuous monitoring and evaluation. It uses green and red bars to show the growth and decline of a variable under study.

In the coming section, we’ll address creating a year-over-year growth chart in Excel.

Excel is one of the go-to data visualization tools for businesses and professionals.

However, this freemium spreadsheet tool comes with a very basic Year-over-year Growth Chart in Excel, such as Progress Graphs.

Well, you don’t have to do away with the spreadsheet app.

You can turn Excel into a reliable data visualization tool loaded with ready-made charts like chord diagrams , Progress and Slope Graphs, by installing third-party apps, such as ChartExpo.

Why ChartExpo?

ChartExpo is a Year-over-year Growth Chart maker that comes as an add-in you can easily install in your Excel.

With different insightful and ready-to-use visualizations, ChartExpo turns your complex, raw data into  compelling visual renderings that tell the story of your data.

This Growth Chart in Excel generator produces simple and clear visualization designs with just a few clicks.

Yes, ChartExpo generates Growth graphs that are amazingly easy to interpret, even for non-technical audiences.

This section will use a Progress Chart to visualize the data below.

To install ChartExpo into your Excel, click this link .

• Open the worksheet and click the Insert button to access the My Apps option.

• Select ChartExpo and click the  Insert button to get started with ChartExpo.

• Once ChartExpo is loaded, you will see a list of charts.

• In this case, look for “ Progress Chart ” in the list as shown below.

• Select the sheet holding your data and click the Create Chart From Selection button, as shown below.

• To edit the chart, click on pencil icon next to chart header.

• Once the Chart Header Properties window shows, click the Line 1 box and fill in the heading. Also, toggle the Show button to the right side.
• Click the Apply button .

• To add a legend, click on pencil icon next to legend.
• Once the Chart Header Properties window shows, toggle the Show button to the right side.
• Complete the process by clicking the Apply button.

In the coming section, we’ll address the following question: why do we need a year-over-year growth chart?

More accurate for seasonal businesses

Some businesses may track their results by comparing them to the previous months’ or quarters’ results.

While this may be resourceful for some, seasonal businesses, such as ski resorts, may not benefit from this method.

Results look smoother

Since your business’s success can vary per month, its performance throughout each month may make the company look unstable, even if it performed well during its on-season.

With the year-over-year growth chart, comparing specific months or quarters can smooth out periods of abysmal performance, which is crucial for effective data storytelling .

Simple to track and calculate

Most businesses use spreadsheet apps, such as Microsoft Excel and Google Sheets, making it possible to create an Area chart or use many other options like a Progress Chart and generate year-over-year growth charts in these apps

Better understand what improvements you can make

Year-over-year Growth Charts can help you view the results of different aspects of your company’s performance for in-depth understanding.

What is the formula for calculating the year-over-year growth?

To calculate YoY, take your current year’s revenue and subtract the previous year’s income.

This gives you a total change in revenue. Then, divide that amount by last year’s total revenue. To get the year-over-year growth value, take that number and multiply it by 100.

Some of the tested and proven year-over-year growth visualization designs include

So, what’s the solution?

We recommend installing third-party apps, such as ChartExpo into your Excel to access a ready-made Year-over-year Growth Chart. Essentially, it’s an add-in you can easily download and install in your Excel app.

ChartExpo comes loaded with all the 10 types of Year-over-year Growth Charts, and many more ready-to-go visualization designs.

Sign up for a 7-day trial to access all the 10 types of YoY Charts. Yes, you’ll enjoy unlimited access to easy-to-interpret and visually appealing charts.

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What Is a Growth Curve?

Understanding a growth curve, the bottom line.

• Business Leaders
• Math and Statistics

Growth Curve: Definition, How It's Used, and Example

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

A growth curve is a graphical representation how how something changes over time. An example of a growth curve might be a chart showing a country's population increase over time.

Growth curves are widely used in statistics to determine patterns of growth over time of a quantity—be it linear, exponential, or cubic. Businesses use growth curves to track or predict many factors, including future sales .

Key Takeaways

• A growth curve shows the direction of some phenomena over time, in the past or into the future, or both.
• Growth curves are typically displayed on a set of axes where the x-axis is time and the y-axis shows an amount of growth.
• Growth curves are used in a variety of applications from population biology and ecology to finance and economics.
• Growth curves allow for the monitoring of change over time and what variables may cause this change. Businesses and investors can adjust strategies depending on the growth curve.

The shape of a growth curve can make a big difference when a business determines whether to launch a new product or enter a new market . Slow growth markets are less likely to be appealing because there is less room for profit. Exponential growth is generally positive but could mean that the market will attract a lot of competitors.

Growth curves were initially used in the physical sciences such as biology. Today, they're a common component of social sciences as well.

Digital Enhancements

Advancements in digital technologies and business models now require analysts to account for growth patterns unique to the modern economy. For example, the winner-take-all phenomenon is a fairly recent development brought on by companies such as Amazon, Google, and Apple . Researchers are scrambling to make sense of growth curves that are unique to new business models and platforms.

Growth curves are often associated with biology, allowing biologists to study organisms and how these organisms behave in a specific environment and the changes to that environment in a controlled setting.

Shifts in demographics, the nature of work, and artificial intelligence will further strain conventional ways of analyzing growth curves or trends.

Analysis of growth curves plays an essential role in determining the future success of products, markets, and societies, both at the micro and macro levels.

Example of a Growth Curve

In the image below, the growth curve displayed represents the growth of a population in millions over a span of decades. The shape of this growth curve indicates exponential growth. That is, the growth curve starts slowly, remains nearly flat for some time, and then curves sharply upwards, appearing almost vertical.

This curve follows the general formula:

V = S * (1 + R) t

The current value, V, of an initial starting point subject to exponential growth, can be determined by multiplying the starting value, S, by the sum of one plus the rate of interest, R, raised to the power of t, or the number of periods that have elapsed.

In finance, exponential growth appears most commonly in the context of compound interest.

The power of compounding is one of the most powerful forces in finance. This concept allows investors to create large sums with little initial capital. Savings accounts that carry a compounding interest rate are common examples.

What Are the 2 Types of Growth Curves?

The two types of growth curves are exponential growth curves and logarithmic growth curves. In an exponential growth curve, the slope grows greater and greater as time moves along. In a logarithmic growth curve, the slope grows sharply, and then over time the slope declines until it becomes flat.

Why Use a Growth Curve?

Growth curves are a helpful visual representation of change over time. Growth curves can be used to understand a variety of changes over time, such as developmental and economic. They allow for the understanding of the effect of policies or treatments.

What Is a Business Growth Model?

A business growth model provides a visual representation for businesses to track various metrics and key drivers, allowing businesses to map out growth and adjust the businesses accordingly to foster these metrics.

A growth curve is a graph that represents the way a phenomenon changes over time. It can show both the past and the future. They typically use two axes, where the x-axis is time and the y-axis is growth.

Growth curves are used in many disciplines, including sciences such as biology and ecology. They are also used in finance and economics. Businesses can use growth curves to see how a specific market is changing over time. This can help them decide whether to enter or leave a certain market or adjust their selling strategy to account for changes.

Curran, Patrick J., Obeidat, Khawla, and Losardo, Diane, via National Library of Medicine. " Twelve Frequently Asked Questions About Growth Curve Modeling: Abstract ." Journal of Cognition and Development , vol. 11, no. 2, 2010.

Sigirli, Deniz and Ercan, Ilker. " Examining Growth with Statistical Shape Analysis and Comparison of Growth Models ." Journal of Modern Applied Statistical Methods , vol. 11, no. 2, November 2012, pp. 1.

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11 Ways to Visualize Changes Over Time – A Guide

Deal with data? No doubt you’ve come across the time-based variety. The visualization you use to explore and display that data changes depending on what you’re after and data types. Maybe you’re looking for increases and decreases, or maybe seasonal patterns.

This is a guide to help you figure out what type of visualization to use to see that stuff.

Let’s start with the basics: the line graph. This will work for most of your time series data. Use it when you have a lot of a points or just a few. Place multiple time series on one graph or place one. Mark the data points with squares, circles, or none at all. Basically, if you’re not sure what to use, the line graph will usually do the trick.

An example: Comparing Roger Clemens to Hall of Fame Pitchers    

Scatterplots work well if you have a lot of data points. Because the dots are small, it doesn’t work well if you only have a few points. Scatterplots also work well when your measurements aren’t nicely structured. For example, if your measurements aren’t equally spaced, a line graph probably wouldn’t work.

An example: Oxygen Concentration Over Time    

Bar charts work best for time series when you’re dealing with distinct points in time (as opposed to more continuous data). They tend to work better when you have data points that are evenly spaced in time.

An example: Who’s Going to Win Nathan’s Hot Dog Eating Contest?    

Use this the same way you would a bar chart when you have multiple categories (hence the stacking). The stacks represent a significance in the sum of the parts. Don’t stack if the parts don’t go together though.

An example: Bad Housing Loans in Forclosure    

The stacked area is the stacked bar’s more versatile sibling. Use this if you’ve got a lot of data points in time and there isn’t enough room for a bunch of bars.

An example: Past 25 Years of Consumer Spending    

The bubble plot is like a scatterplot, but instead of small dots, you size circles by some other metric. This way you can show two measurements at once over time. Hans Rosling’s TED talks made this visualization method especially popular in the past couple of years.

An example: Income per Person and GDP by Gapminder

Color to show changes tends to be underutilized. It’s easier to see differences in height than it is to see differences in shades of gray, but if you’re limited by space or need to show a lot at once, color can be a good solution. The main challenges with color, that should play a role in the design process, are choosing color scale and dealing with the small portion of the population who is colorblind.

An example: Congestion in the Sky

Timelines work for events i.e. you’re most interested in time of occurrence. While they don’t work well if you have a lot of data, you can combine the timeline with any of the above to pretty good effect.

An example: 10 Largest Data Breaches Since 2000 — Millions Affected    

Again, like the guide to proportions , showing every single data point can work well when you’re interested in the details of every event. This obviously takes up a lot of space, but is sometimes effective when you need to humanize the data.

An example: The Pitching Dominance of Mariano Rivera    

Animation opens up a whole other bag of worms, and it can tricky if you don’t know what you’re doing. It can, however, work really well if you do know what you’re doing. With animation, you can basically take any static graphic, create one for every point in time, and then string them together like a video.

An example: Watch the Giants of Finance Shrink and Then Grow    

Finally, if all else fails, you can always show your data in a basic table. If there aren’t that many data points, a table usually works just fine. Many of the above options will also fit together nicely.

Ready for more? Join as a FlowingData member for access to tutorials on how to do this stuff .

• Alternative Ways to Represent Data
• How to Visualize Anomalies in Time Series Data in R, with ggplot
• I Want to Visualize Aspects of the Data – The Process 162

32 Comments

A radial chart may work for a 12-hour period (yes, it’s also a bag of worms).

What about a time matrix. I was thinking of the one with month on the X axis and years on the Y axis.

It has been used to plot Ozone concentration in the Los Angelas Area : graph available here:

http://www.math.yorku.ca/SCS/Gallery/images/LAoz.gif

@bernard was just about to say the same. They are relatively easy to create using ggplot2 in r. Good to show any regular (e.g. weekly) patterns.

Small multiples, in the Edward Tufte sense, can also get you to “aha.” http://37signals.com/svn/posts/266-using-small-multiples-to-get-to-aha

I would like to add that the bar charts should be used when the individual data sets are more important than the trend. When showing or identifying trends is more important, then the line graphs should be used.

I had written an article on this a couple of months back that could be helpful: http://www.tutorial9.net/web-tutorials/selecting-the-right-chart-type-for-your-data/

To Jorge’s comment, yes, radial bars can work well when the clock analogy of the chart works – here’s an example on my blog from the BBC concerning time and car accidents.

http://blog.datadrivenconsulting.com/2010/01/exploring-crash-statistics-excellent.html

Nice job on the examples…

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In addition to radial bars, what about plotting points using circular coordinates? Or would this be considered a variation of scatter? I used such a device in my flickr search visualizer

http://www.bitstream.ca

I like this listing of basic ways to visualize changes over time very much! There might be some additional information that could be interesting – I’ve held a lecture on the visualization of time-oriented data for a couple of years now and collected quite a wide variety of applicable techniques along with some theory.

The slides can be downloaded here: – Intro/Theory: http://ieg.ifs.tuwien.ac.at/~aigner/presentations/20091214_timevis_intro_1up.pdf – Techniques: http://ieg.ifs.tuwien.ac.at/~aigner/presentations/20091214_timevis_techniques_1up.pdf

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How do I display a comparison between two groups over time with up to ten variables? Trend over time would be a line; comparison a bar; the line graph looks a mess, and the bar/column doesn’t depict the trend. Perhaps it take 2 graphs, but that loses the comparison factor.

interactive with filters:

http://manyeyes.alphaworks.ibm.com/manyeyes/visualizations/michigan-football-home-vs-away-gam

two graphs side-by-side should work okay too. you won’t have the direct comparison, but probably doable.

And if you have 10 variables that are all different units, you probably shouldn’t put all of those on a single graph.

Without knowing more about the data and the goal of representation – a few possible ways:

– Small Multiples ( http://www.infovis-wiki.net/index.php?title=Teaching:TUW_-_UE_InfoVis_WS_2008/09_-_Gruppe_06_-_Aufgabe_1_-_Small_Multiples )

– Using more visual variables (e.g., also color, line thickness, etc.) Here is a list of commonly used visual variables: http://www.infovis-wiki.net/index.php?title=Visual_Variables

– Using “glyphs”: http://www.infovis-wiki.net/index.php?title=Glyph

Very helpful. Thanks for taking the time. John

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The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works

• Maria Evagorou 1 ,
• Sibel Erduran 2 &
• Terhi Mäntylä 3  

International Journal of STEM Education volume  2 , Article number:  11 ( 2015 ) Cite this article

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The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects of scientific practices, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective.

This is a theoretical paper, and in order to argue about the role of visualization, we first present a case study, that of the discovery of the structure of DNA that highlights the epistemic components of visual information in science. The second case study focuses on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of the experimentation. Third, we trace a contemporary account from science focusing on the experimental practices and how reproducibility of experimental procedures can be reinforced through video data.

Conclusions

Our conclusions suggest that in teaching science, the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Furthermore, we suggest that is it essential to design curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication and reflect on these practices. Implications for teacher education include the need for teacher professional development programs to problematize the use of visual representations as epistemic objects that are part of scientific practices.

During the last decades, research and reform documents in science education across the world have been calling for an emphasis not only on the content but also on the processes of science (Bybee 2014 ; Eurydice 2012 ; Duschl and Bybee 2014 ; Osborne 2014 ; Schwartz et al. 2012 ), in order to make science accessible to the students and enable them to understand the epistemic foundation of science. Scientific practices, part of the process of science, are the cognitive and discursive activities that are targeted in science education to develop epistemic understanding and appreciation of the nature of science (Duschl et al. 2008 ) and have been the emphasis of recent reform documents in science education across the world (Achieve 2013 ; Eurydice 2012 ). With the term scientific practices, we refer to the processes that take place during scientific discoveries and include among others: asking questions, developing and using models, engaging in arguments, and constructing and communicating explanations (National Research Council 2012 ). The emphasis on scientific practices aims to move the teaching of science from knowledge to the understanding of the processes and the epistemic aspects of science. Additionally, by placing an emphasis on engaging students in scientific practices, we aim to help students acquire scientific knowledge in meaningful contexts that resemble the reality of scientific discoveries.

Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective. Specifically, the use of visual representations (i.e., photographs, diagrams, tables, charts) has been part of science and over the years has evolved with the new technologies (i.e., from drawings to advanced digital images and three dimensional models). Visualization makes it possible for scientists to interact with complex phenomena (Richards 2003 ), and they might convey important evidence not observable in other ways. Visual representations as a tool to support cognitive understanding in science have been studied extensively (i.e., Gilbert 2010 ; Wu and Shah 2004 ). Studies in science education have explored the use of images in science textbooks (i.e., Dimopoulos et al. 2003 ; Bungum 2008 ), students’ representations or models when doing science (i.e., Gilbert et al. 2008 ; Dori et al. 2003 ; Lehrer and Schauble 2012 ; Schwarz et al. 2009 ), and students’ images of science and scientists (i.e., Chambers 1983 ). Therefore, studies in the field of science education have been using the term visualization as “the formation of an internal representation from an external representation” (Gilbert et al. 2008 , p. 4) or as a tool for conceptual understanding for students.

In this paper, we do not refer to visualization as mental image, model, or presentation only (Gilbert et al. 2008 ; Philips et al. 2010 ) but instead focus on visual representations or visualization as epistemic objects. Specifically, we refer to visualization as a process for knowledge production and growth in science. In this respect, modeling is an aspect of visualization, but what we are focusing on with visualization is not on the use of model as a tool for cognitive understanding (Gilbert 2010 ; Wu and Shah 2004 ) but the on the process of modeling as a scientific practice which includes the construction and use of models, the use of other representations, the communication in the groups with the use of the visual representation, and the appreciation of the difficulties that the science phase in this process. Therefore, the purpose of this paper is to present through the history of science how visualization can be considered not only as a cognitive tool in science education but also as an epistemic object that can potentially support students to understand aspects of the nature of science.

Scientific practices and science education

According to the New Generation Science Standards (Achieve 2013 ), scientific practices refer to: asking questions and defining problems; developing and using models; planning and carrying out investigations; analyzing and interpreting data; using mathematical and computational thinking; constructing explanations and designing solutions; engaging in argument from evidence; and obtaining, evaluating, and communicating information. A significant aspect of scientific practices is that science learning is more than just about learning facts, concepts, theories, and laws. A fuller appreciation of science necessitates the understanding of the science relative to its epistemological grounding and the process that are involved in the production of knowledge (Hogan and Maglienti 2001 ; Wickman 2004 ).

The New Generation Science Standards is, among other changes, shifting away from science inquiry and towards the inclusion of scientific practices (Duschl and Bybee 2014 ; Osborne 2014 ). By comparing the abilities to do scientific inquiry (National Research Council 2000 ) with the set of scientific practices, it is evident that the latter is about engaging in the processes of doing science and experiencing in that way science in a more authentic way. Engaging in scientific practices according to Osborne ( 2014 ) “presents a more authentic picture of the endeavor that is science” (p.183) and also helps the students to develop a deeper understanding of the epistemic aspects of science. Furthermore, as Bybee ( 2014 ) argues, by engaging students in scientific practices, we involve them in an understanding of the nature of science and an understanding on the nature of scientific knowledge.

Science as a practice and scientific practices as a term emerged by the philosopher of science, Kuhn (Osborne 2014 ), refers to the processes in which the scientists engage during knowledge production and communication. The work that is followed by historians, philosophers, and sociologists of science (Latour 2011 ; Longino 2002 ; Nersessian 2008 ) revealed the scientific practices in which the scientists engage in and include among others theory development and specific ways of talking, modeling, and communicating the outcomes of science.

Visualization as an epistemic object

Schematic, pictorial symbols in the design of scientific instruments and analysis of the perceptual and functional information that is being stored in those images have been areas of investigation in philosophy of scientific experimentation (Gooding et al. 1993 ). The nature of visual perception, the relationship between thought and vision, and the role of reproducibility as a norm for experimental research form a central aspect of this domain of research in philosophy of science. For instance, Rothbart ( 1997 ) has argued that visualizations are commonplace in the theoretical sciences even if every scientific theory may not be defined by visualized models.

Visual representations (i.e., photographs, diagrams, tables, charts, models) have been used in science over the years to enable scientists to interact with complex phenomena (Richards 2003 ) and might convey important evidence not observable in other ways (Barber et al. 2006 ). Some authors (e.g., Ruivenkamp and Rip 2010 ) have argued that visualization is as a core activity of some scientific communities of practice (e.g., nanotechnology) while others (e.g., Lynch and Edgerton 1988 ) have differentiated the role of particular visualization techniques (e.g., of digital image processing in astronomy). Visualization in science includes the complex process through which scientists develop or produce imagery, schemes, and graphical representation, and therefore, what is of importance in this process is not only the result but also the methodology employed by the scientists, namely, how this result was produced. Visual representations in science may refer to objects that are believed to have some kind of material or physical existence but equally might refer to purely mental, conceptual, and abstract constructs (Pauwels 2006 ). More specifically, visual representations can be found for: (a) phenomena that are not observable with the eye (i.e., microscopic or macroscopic); (b) phenomena that do not exist as visual representations but can be translated as such (i.e., sound); and (c) in experimental settings to provide visual data representations (i.e., graphs presenting velocity of moving objects). Additionally, since science is not only about replicating reality but also about making it more understandable to people (either to the public or other scientists), visual representations are not only about reproducing the nature but also about: (a) functioning in helping solving a problem, (b) filling gaps in our knowledge, and (c) facilitating knowledge building or transfer (Lynch 2006 ).

Using or developing visual representations in the scientific practice can range from a straightforward to a complicated situation. More specifically, scientists can observe a phenomenon (i.e., mitosis) and represent it visually using a picture or diagram, which is quite straightforward. But they can also use a variety of complicated techniques (i.e., crystallography in the case of DNA studies) that are either available or need to be developed or refined in order to acquire the visual information that can be used in the process of theory development (i.e., Latour and Woolgar 1979 ). Furthermore, some visual representations need decoding, and the scientists need to learn how to read these images (i.e., radiologists); therefore, using visual representations in the process of science requires learning a new language that is specific to the medium/methods that is used (i.e., understanding an X-ray picture is different from understanding an MRI scan) and then communicating that language to other scientists and the public.

There are much intent and purposes of visual representations in scientific practices, as for example to make a diagnosis, compare, describe, and preserve for future study, verify and explore new territory, generate new data (Pauwels 2006 ), or present new methodologies. According to Latour and Woolgar ( 1979 ) and Knorr Cetina ( 1999 ), visual representations can be used either as primary data (i.e., image from a microscope). or can be used to help in concept development (i.e., models of DNA used by Watson and Crick), to uncover relationships and to make the abstract more concrete (graphs of sound waves). Therefore, visual representations and visual practices, in all forms, are an important aspect of the scientific practices in developing, clarifying, and transmitting scientific knowledge (Pauwels 2006 ).

Methods and Results: Merging Visualization and scientific practices in science

In this paper, we present three case studies that embody the working practices of scientists in an effort to present visualization as a scientific practice and present our argument about how visualization is a complex process that could include among others modeling and use of representation but is not only limited to that. The first case study explores the role of visualization in the construction of knowledge about the structure of DNA, using visuals as evidence. The second case study focuses on Faraday’s use of the lines of magnetic force and the visual reasoning leading to the theoretical development that was an inherent part of the experimentation. The third case study focuses on the current practices of scientists in the context of a peer-reviewed journal called the Journal of Visualized Experiments where the methodology is communicated through videotaped procedures. The three case studies represent the research interests of the three authors of this paper and were chosen to present how visualization as a practice can be involved in all stages of doing science, from hypothesizing and evaluating evidence (case study 1) to experimenting and reasoning (case study 2) to communicating the findings and methodology with the research community (case study 3), and represent in this way the three functions of visualization as presented by Lynch ( 2006 ). Furthermore, the last case study showcases how the development of visualization technologies has contributed to the communication of findings and methodologies in science and present in that way an aspect of current scientific practices. In all three cases, our approach is guided by the observation that the visual information is an integral part of scientific practices at the least and furthermore that they are particularly central in the scientific practices of science.

Case study 1: use visual representations as evidence in the discovery of DNA

The focus of the first case study is the discovery of the structure of DNA. The DNA was first isolated in 1869 by Friedrich Miescher, and by the late 1940s, it was known that it contained phosphate, sugar, and four nitrogen-containing chemical bases. However, no one had figured the structure of the DNA until Watson and Crick presented their model of DNA in 1953. Other than the social aspects of the discovery of the DNA, another important aspect was the role of visual evidence that led to knowledge development in the area. More specifically, by studying the personal accounts of Watson ( 1968 ) and Crick ( 1988 ) about the discovery of the structure of the DNA, the following main ideas regarding the role of visual representations in the production of knowledge can be identified: (a) The use of visual representations was an important part of knowledge growth and was often dependent upon the discovery of new technologies (i.e., better microscopes or better techniques in crystallography that would provide better visual representations as evidence of the helical structure of the DNA); and (b) Models (three-dimensional) were used as a way to represent the visual images (X-ray images) and connect them to the evidence provided by other sources to see whether the theory can be supported. Therefore, the model of DNA was built based on the combination of visual evidence and experimental data.

An example showcasing the importance of visual representations in the process of knowledge production in this case is provided by Watson, in his book The Double Helix (1968):

…since the middle of the summer Rosy [Rosalind Franklin] had had evidence for a new three-dimensional form of DNA. It occurred when the DNA 2molecules were surrounded by a large amount of water. When I asked what the pattern was like, Maurice went into the adjacent room to pick up a print of the new form they called the “B” structure. The instant I saw the picture, my mouth fell open and my pulse began to race. The pattern was unbelievably simpler than those previously obtained (A form). Moreover, the black cross of reflections which dominated the picture could arise only from a helical structure. With the A form the argument for the helix was never straightforward, and considerable ambiguity existed as to exactly which type of helical symmetry was present. With the B form however, mere inspection of its X-ray picture gave several of the vital helical parameters. (p. 167-169)

As suggested by Watson’s personal account of the discovery of the DNA, the photo taken by Rosalind Franklin (Fig.  1 ) convinced him that the DNA molecule must consist of two chains arranged in a paired helix, which resembles a spiral staircase or ladder, and on March 7, 1953, Watson and Crick finished and presented their model of the structure of DNA (Watson and Berry 2004 ; Watson 1968 ) which was based on the visual information provided by the X-ray image and their knowledge of chemistry.

X-ray chrystallography of DNA

In analyzing the visualization practice in this case study, we observe the following instances that highlight how the visual information played a role:

Asking questions and defining problems: The real world in the model of science can at some points only be observed through visual representations or representations, i.e., if we are using DNA as an example, the structure of DNA was only observable through the crystallography images produced by Rosalind Franklin in the laboratory. There was no other way to observe the structure of DNA, therefore the real world.

Analyzing and interpreting data: The images that resulted from crystallography as well as their interpretations served as the data for the scientists studying the structure of DNA.

Experimenting: The data in the form of visual information were used to predict the possible structure of the DNA.

Modeling: Based on the prediction, an actual three-dimensional model was prepared by Watson and Crick. The first model did not fit with the real world (refuted by Rosalind Franklin and her research group from King’s College) and Watson and Crick had to go through the same process again to find better visual evidence (better crystallography images) and create an improved visual model.

Example excerpts from Watson’s biography provide further evidence for how visualization practices were applied in the context of the discovery of DNA (Table  1 ).

In summary, by examining the history of the discovery of DNA, we showcased how visual data is used as scientific evidence in science, identifying in that way an aspect of the nature of science that is still unexplored in the history of science and an aspect that has been ignored in the teaching of science. Visual representations are used in many ways: as images, as models, as evidence to support or rebut a model, and as interpretations of reality.

Case study 2: applying visual reasoning in knowledge production, the example of the lines of magnetic force

The focus of this case study is on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of this experimentation (Gooding 2006 ). Faraday’s articles or notebooks do not include mathematical formulations; instead, they include images and illustrations from experimental devices and setups to the recapping of his theoretical ideas (Nersessian 2008 ). According to Gooding ( 2006 ), “Faraday’s visual method was designed not to copy apparent features of the world, but to analyse and replicate them” (2006, p. 46).

The lines of force played a central role in Faraday’s research on electricity and magnetism and in the development of his “field theory” (Faraday 1852a ; Nersessian 1984 ). Before Faraday, the experiments with iron filings around magnets were known and the term “magnetic curves” was used for the iron filing patterns and also for the geometrical constructs derived from the mathematical theory of magnetism (Gooding et al. 1993 ). However, Faraday used the lines of force for explaining his experimental observations and in constructing the theory of forces in magnetism and electricity. Examples of Faraday’s different illustrations of lines of magnetic force are given in Fig.  2 . Faraday gave the following experiment-based definition for the lines of magnetic forces:

a Iron filing pattern in case of bar magnet drawn by Faraday (Faraday 1852b , Plate IX, p. 158, Fig. 1), b Faraday’s drawing of lines of magnetic force in case of cylinder magnet, where the experimental procedure, knife blade showing the direction of lines, is combined into drawing (Faraday, 1855, vol. 1, plate 1)

A line of magnetic force may be defined as that line which is described by a very small magnetic needle, when it is so moved in either direction correspondent to its length, that the needle is constantly a tangent to the line of motion; or it is that line along which, if a transverse wire be moved in either direction, there is no tendency to the formation of any current in the wire, whilst if moved in any other direction there is such a tendency; or it is that line which coincides with the direction of the magnecrystallic axis of a crystal of bismuth, which is carried in either direction along it. The direction of these lines about and amongst magnets and electric currents, is easily represented and understood, in a general manner, by the ordinary use of iron filings. (Faraday 1852a , p. 25 (3071))

The definition describes the connection between the experiments and the visual representation of the results. Initially, the lines of force were just geometric representations, but later, Faraday treated them as physical objects (Nersessian 1984 ; Pocovi and Finlay 2002 ):

I have sometimes used the term lines of force so vaguely, as to leave the reader doubtful whether I intended it as a merely representative idea of the forces, or as the description of the path along which the power was continuously exerted. … wherever the expression line of force is taken simply to represent the disposition of forces, it shall have the fullness of that meaning; but that wherever it may seem to represent the idea of the physical mode of transmission of the force, it expresses in that respect the opinion to which I incline at present. The opinion may be erroneous, and yet all that relates or refers to the disposition of the force will remain the same. (Faraday, 1852a , p. 55-56 (3075))

He also felt that the lines of force had greater explanatory power than the dominant theory of action-at-a-distance:

Now it appears to me that these lines may be employed with great advantage to represent nature, condition, direction and comparative amount of the magnetic forces; and that in many cases they have, to the physical reasoned at least, a superiority over that method which represents the forces as concentrated in centres of action… (Faraday, 1852a , p. 26 (3074))

For giving some insight to Faraday’s visual reasoning as an epistemic practice, the following examples of Faraday’s studies of the lines of magnetic force (Faraday 1852a , 1852b ) are presented:

(a) Asking questions and defining problems: The iron filing patterns formed the empirical basis for the visual model: 2D visualization of lines of magnetic force as presented in Fig.  2 . According to Faraday, these iron filing patterns were suitable for illustrating the direction and form of the magnetic lines of force (emphasis added):

It must be well understood that these forms give no indication by their appearance of the relative strength of the magnetic force at different places, inasmuch as the appearance of the lines depends greatly upon the quantity of filings and the amount of tapping; but the direction and forms of these lines are well given, and these indicate, in a considerable degree, the direction in which the forces increase and diminish . (Faraday 1852b , p.158 (3237))

Despite being static and two dimensional on paper, the lines of magnetic force were dynamical (Nersessian 1992 , 2008 ) and three dimensional for Faraday (see Fig.  2 b). For instance, Faraday described the lines of force “expanding”, “bending,” and “being cut” (Nersessian 1992 ). In Fig.  2 b, Faraday has summarized his experiment (bar magnet and knife blade) and its results (lines of force) in one picture.

(b) Analyzing and interpreting data: The model was so powerful for Faraday that he ended up thinking them as physical objects (e.g., Nersessian 1984 ), i.e., making interpretations of the way forces act. Of course, he made a lot of experiments for showing the physical existence of the lines of force, but he did not succeed in it (Nersessian 1984 ). The following quote illuminates Faraday’s use of the lines of force in different situations:

The study of these lines has, at different times, been greatly influential in leading me to various results, which I think prove their utility as well as fertility. Thus, the law of magneto-electric induction; the earth’s inductive action; the relation of magnetism and light; diamagnetic action and its law, and magnetocrystallic action, are the cases of this kind… (Faraday 1852a , p. 55 (3174))

(c) Experimenting: In Faraday's case, he used a lot of exploratory experiments; in case of lines of magnetic force, he used, e.g., iron filings, magnetic needles, or current carrying wires (see the quote above). The magnetic field is not directly observable and the representation of lines of force was a visual model, which includes the direction, form, and magnitude of field.

(d) Modeling: There is no denying that the lines of magnetic force are visual by nature. Faraday’s views of lines of force developed gradually during the years, and he applied and developed them in different contexts such as electromagnetic, electrostatic, and magnetic induction (Nersessian 1984 ). An example of Faraday’s explanation of the effect of the wire b’s position to experiment is given in Fig.  3 . In Fig.  3 , few magnetic lines of force are drawn, and in the quote below, Faraday is explaining the effect using these magnetic lines of force (emphasis added):

Picture of an experiment with different arrangements of wires ( a , b’ , b” ), magnet, and galvanometer. Note the lines of force drawn around the magnet. (Faraday 1852a , p. 34)

It will be evident by inspection of Fig. 3 , that, however the wires are carried away, the general result will, according to the assumed principles of action, be the same; for if a be the axial wire, and b’, b”, b”’ the equatorial wire, represented in three different positions, whatever magnetic lines of force pass across the latter wire in one position, will also pass it in the other, or in any other position which can be given to it. The distance of the wire at the place of intersection with the lines of force, has been shown, by the experiments (3093.), to be unimportant. (Faraday 1852a , p. 34 (3099))

In summary, by examining the history of Faraday’s use of lines of force, we showed how visual imagery and reasoning played an important part in Faraday’s construction and representation of his “field theory”. As Gooding has stated, “many of Faraday’s sketches are far more that depictions of observation, they are tools for reasoning with and about phenomena” (2006, p. 59).

Case study 3: visualizing scientific methods, the case of a journal

The focus of the third case study is the Journal of Visualized Experiments (JoVE) , a peer-reviewed publication indexed in PubMed. The journal devoted to the publication of biological, medical, chemical, and physical research in a video format. The journal describes its history as follows:

JoVE was established as a new tool in life science publication and communication, with participation of scientists from leading research institutions. JoVE takes advantage of video technology to capture and transmit the multiple facets and intricacies of life science research. Visualization greatly facilitates the understanding and efficient reproduction of both basic and complex experimental techniques, thereby addressing two of the biggest challenges faced by today's life science research community: i) low transparency and poor reproducibility of biological experiments and ii) time and labor-intensive nature of learning new experimental techniques. ( http://www.jove.com/ )

By examining the journal content, we generate a set of categories that can be considered as indicators of relevance and significance in terms of epistemic practices of science that have relevance for science education. For example, the quote above illustrates how scientists view some norms of scientific practice including the norms of “transparency” and “reproducibility” of experimental methods and results, and how the visual format of the journal facilitates the implementation of these norms. “Reproducibility” can be considered as an epistemic criterion that sits at the heart of what counts as an experimental procedure in science:

Investigating what should be reproducible and by whom leads to different types of experimental reproducibility, which can be observed to play different roles in experimental practice. A successful application of the strategy of reproducing an experiment is an achievement that may depend on certain isiosyncratic aspects of a local situation. Yet a purely local experiment that cannot be carried out by other experimenters and in other experimental contexts will, in the end be unproductive in science. (Sarkar and Pfeifer 2006 , p.270)

We now turn to an article on “Elevated Plus Maze for Mice” that is available for free on the journal website ( http://www.jove.com/video/1088/elevated-plus-maze-for-mice ). The purpose of this experiment was to investigate anxiety levels in mice through behavioral analysis. The journal article consists of a 9-min video accompanied by text. The video illustrates the handling of the mice in soundproof location with less light, worksheets with characteristics of mice, computer software, apparatus, resources, setting up the computer software, and the video recording of mouse behavior on the computer. The authors describe the apparatus that is used in the experiment and state how procedural differences exist between research groups that lead to difficulties in the interpretation of results:

The apparatus consists of open arms and closed arms, crossed in the middle perpendicularly to each other, and a center area. Mice are given access to all of the arms and are allowed to move freely between them. The number of entries into the open arms and the time spent in the open arms are used as indices of open space-induced anxiety in mice. Unfortunately, the procedural differences that exist between laboratories make it difficult to duplicate and compare results among laboratories.

The authors’ emphasis on the particularity of procedural context echoes in the observations of some philosophers of science:

It is not just the knowledge of experimental objects and phenomena but also their actual existence and occurrence that prove to be dependent on specific, productive interventions by the experimenters” (Sarkar and Pfeifer 2006 , pp. 270-271)

The inclusion of a video of the experimental procedure specifies what the apparatus looks like (Fig.  4 ) and how the behavior of the mice is captured through video recording that feeds into a computer (Fig.  5 ). Subsequently, a computer software which captures different variables such as the distance traveled, the number of entries, and the time spent on each arm of the apparatus. Here, there is visual information at different levels of representation ranging from reconfiguration of raw video data to representations that analyze the data around the variables in question (Fig.  6 ). The practice of levels of visual representations is not particular to the biological sciences. For instance, they are commonplace in nanotechnological practices:

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

In the visualization processes, instruments are needed that can register the nanoscale and provide raw data, which needs to be transformed into images. Some Imaging Techniques have software incorporated already where this transformation automatically takes place, providing raw images. Raw data must be translated through the use of Graphic Software and software is also used for the further manipulation of images to highlight what is of interest to capture the (inferred) phenomena -- and to capture the reader. There are two levels of choice: Scientists have to choose which imaging technique and embedded software to use for the job at hand, and they will then have to follow the structure of the software. Within such software, there are explicit choices for the scientists, e.g. about colour coding, and ways of sharpening images. (Ruivenkamp and Rip 2010 , pp.14–15)

On the text that accompanies the video, the authors highlight the role of visualization in their experiment:

Visualization of the protocol will promote better understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used in different laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The software that takes the video data and transforms it into various representations allows the researchers to collect data on mouse behavior more reliably. For instance, the distance traveled across the arms of the apparatus or the time spent on each arm would have been difficult to observe and record precisely. A further aspect to note is how the visualization of the experiment facilitates control of bias. The authors illustrate how the olfactory bias between experimental procedures carried on mice in sequence is avoided by cleaning the equipment.

Our discussion highlights the role of visualization in science, particularly with respect to presenting visualization as part of the scientific practices. We have used case studies from the history of science highlighting a scientist’s account of how visualization played a role in the discovery of DNA and the magnetic field and from a contemporary illustration of a science journal’s practices in incorporating visualization as a way to communicate new findings and methodologies. Our implicit aim in drawing from these case studies was the need to align science education with scientific practices, particularly in terms of how visual representations, stable or dynamic, can engage students in the processes of science and not only to be used as tools for cognitive development in science. Our approach was guided by the notion of “knowledge-as-practice” as advanced by Knorr Cetina ( 1999 ) who studied scientists and characterized their knowledge as practice, a characterization which shifts focus away from ideas inside scientists’ minds to practices that are cultural and deeply contextualized within fields of science. She suggests that people working together can be examined as epistemic cultures whose collective knowledge exists as practice.

It is important to stress, however, that visual representations are not used in isolation, but are supported by other types of evidence as well, or other theories (i.e., in order to understand the helical form of DNA, or the structure, chemistry knowledge was needed). More importantly, this finding can also have implications when teaching science as argument (e.g., Erduran and Jimenez-Aleixandre 2008 ), since the verbal evidence used in the science classroom to maintain an argument could be supported by visual evidence (either a model, representation, image, graph, etc.). For example, in a group of students discussing the outcomes of an introduced species in an ecosystem, pictures of the species and the ecosystem over time, and videos showing the changes in the ecosystem, and the special characteristics of the different species could serve as visual evidence to help the students support their arguments (Evagorou et al. 2012 ). Therefore, an important implication for the teaching of science is the use of visual representations as evidence in the science curriculum as part of knowledge production. Even though studies in the area of science education have focused on the use of models and modeling as a way to support students in the learning of science (Dori et al. 2003 ; Lehrer and Schauble 2012 ; Mendonça and Justi 2013 ; Papaevripidou et al. 2007 ) or on the use of images (i.e., Korfiatis et al. 2003 ), with the term using visuals as evidence, we refer to the collection of all forms of visuals and the processes involved.

Another aspect that was identified through the case studies is that of the visual reasoning (an integral part of Faraday’s investigations). Both the verbalization and visualization were part of the process of generating new knowledge (Gooding 2006 ). Even today, most of the textbooks use the lines of force (or just field lines) as a geometrical representation of field, and the number of field lines is connected to the quantity of flux. Often, the textbooks use the same kind of visual imagery than in what is used by scientists. However, when using images, only certain aspects or features of the phenomena or data are captured or highlighted, and often in tacit ways. Especially in textbooks, the process of producing the image is not presented and instead only the product—image—is left. This could easily lead to an idea of images (i.e., photos, graphs, visual model) being just representations of knowledge and, in the worse case, misinterpreted representations of knowledge as the results of Pocovi and Finlay ( 2002 ) in case of electric field lines show. In order to avoid this, the teachers should be able to explain how the images are produced (what features of phenomena or data the images captures, on what ground the features are chosen to that image, and what features are omitted); in this way, the role of visualization in knowledge production can be made “visible” to students by engaging them in the process of visualization.

The implication of these norms for science teaching and learning is numerous. The classroom contexts can model the generation, sharing and evaluation of evidence, and experimental procedures carried out by students, thereby promoting not only some contemporary cultural norms in scientific practice but also enabling the learning of criteria, standards, and heuristics that scientists use in making decisions on scientific methods. As we have demonstrated with the three case studies, visual representations are part of the process of knowledge growth and communication in science, as demonstrated with two examples from the history of science and an example from current scientific practices. Additionally, visual information, especially with the use of technology is a part of students’ everyday lives. Therefore, we suggest making use of students’ knowledge and technological skills (i.e., how to produce their own videos showing their experimental method or how to identify or provide appropriate visual evidence for a given topic), in order to teach them the aspects of the nature of science that are often neglected both in the history of science and the design of curriculum. Specifically, what we suggest in this paper is that students should actively engage in visualization processes in order to appreciate the diverse nature of doing science and engage in authentic scientific practices.

However, as a word of caution, we need to distinguish the products and processes involved in visualization practices in science:

If one considers scientific representations and the ways in which they can foster or thwart our understanding, it is clear that a mere object approach, which would devote all attention to the representation as a free-standing product of scientific labor, is inadequate. What is needed is a process approach: each visual representation should be linked with its context of production (Pauwels 2006 , p.21).

The aforementioned suggests that the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Therefore, an implication for the teaching of science includes designing curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication (as presented in the three case studies) and reflect on these practices (Ryu et al. 2015 ).

Finally, a question that arises from including visualization in science education, as well as from including scientific practices in science education is whether teachers themselves are prepared to include them as part of their teaching (Bybee 2014 ). Teacher preparation programs and teacher education have been critiqued, studied, and rethought since the time they emerged (Cochran-Smith 2004 ). Despite the years of history in teacher training and teacher education, the debate about initial teacher training and its content still pertains in our community and in policy circles (Cochran-Smith 2004 ; Conway et al. 2009 ). In the last decades, the debate has shifted from a behavioral view of learning and teaching to a learning problem—focusing on that way not only on teachers’ knowledge, skills, and beliefs but also on making the connection of the aforementioned with how and if pupils learn (Cochran-Smith 2004 ). The Science Education in Europe report recommended that “Good quality teachers, with up-to-date knowledge and skills, are the foundation of any system of formal science education” (Osborne and Dillon 2008 , p.9).

However, questions such as what should be the emphasis on pre-service and in-service science teacher training, especially with the new emphasis on scientific practices, still remain unanswered. As Bybee ( 2014 ) argues, starting from the new emphasis on scientific practices in the NGSS, we should consider teacher preparation programs “that would provide undergraduates opportunities to learn the science content and practices in contexts that would be aligned with their future work as teachers” (p.218). Therefore, engaging pre- and in-service teachers in visualization as a scientific practice should be one of the purposes of teacher preparation programs.

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Terhi Mäntylä

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ME carried out the introductory literature review, the analysis of the first case study, and drafted the manuscript. SE carried out the analysis of the third case study and contributed towards the “Conclusions” section of the manuscript. TM carried out the second case study. All authors read and approved the final manuscript.

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Evagorou, M., Erduran, S. & Mäntylä, T. The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. IJ STEM Ed 2 , 11 (2015). https://doi.org/10.1186/s40594-015-0024-x

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The 30 Best Data Visualizations of 2024 [Examples]

Written by: Anna Glivinska

Data is beautiful; it can inspire, improve lives and bring out the best in people. To keep you inspired, we’ve gathered the best data visualizations of 2024.

The chosen works cover a variety of topics from NASA asteroids in space to environmental issue statistics and futuristic LIDAR data graphs.

With over 4.54 billion people using the Internet in 2020, we’re sure to witness even more amazing data visualizations every year. For now, get ready to dive into 2024’s best data visualization examples. Enjoy your flight of imagination!

• NASA's Eyes on Asteroids is a good data visualization example that provides a great user experience. The design is simple and intuitive, making it easy for users to navigate the site and find what they're looking for.
• The History of Pandemics is an infographic that presents a visual timeline of every known pandemic and includes information on how many people were affected, where it spread and what caused it.
• Void of the Memories is the rarest data visualization on this list. It's a great combination of calligraphy and data visualization that tells the story of human memory and experience.
• The search for dark matter is one of the most important scientific questions in physics today, and this infographic, “The Search for Dark Matter,” serves as a great introduction to the subject.
• Enhance your data storytelling skills and creatively showcase your data by signing up for Visme's data visualization tools .

1 Nasa’s Eyes on Asteroids

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If you are interested in exploring data visualization topics in space exploration, check out this striking data visualization created by NASA.

NASA's Eyes on Asteroids is one of the best data visualizations due to its exceptional design and functionality. This interactive visualization allows users to explore the asteroid belt and see the real-time positions of asteroids in our solar system.

The design of this visualization is highly engaging and visually stunning, with a sleek and modern interface that is easy to use. The visualization features a 3D solar system model, allowing users to zoom in and out to explore asteroids and other celestial bodies.

One of the key features of NASA's Eyes on Asteroids visualization is its real-time data feed, which provides up-to-date information on the positions and trajectories of asteroids. This feature makes the visualization highly informative and relevant to current events, allowing users to track potentially hazardous asteroids and see their projected paths over time.

Design your own space exploration infographic using Visme. Allowing you to create data visualizations easier and faster.

Get inspired by one of our loyal Visme users, MacKenzie Stonis , Economic Research Analyst at Greater Memphis Chamber, who said:

"I have enough complications in life; I don’t need my report-building tool to add any fuel to the fire,” she laughs.  “I personally had experience with similar applications before Visme and found their tools weren’t as user-friendly as Visme, and their tools didn’t handle data very well. They didn’t provide the solution I really wanted."

2 Selfiecity – The Science of Selfies

Selfiecity is an innovative and engaging data visualization project exploring the selfies world. It uses a variety of visualizations to analyze selfies from five cities around the world.

They collected over 120,000 selfies from the five cities and selected nearly 1,000 photos from each town. After collecting the images, they analyzed various metrics such as demographics, poses, moods and features.

The project then revealed exciting insights into the culture and social behavior of the people who take selfies. For example, the project shows that women take more selfies than men and that people tend to take selfies in public places rather than private spaces.

The study was quite complex and yielded valuable insights, which presented a challenge when it came to sharing the results . However, the team did an excellent job creating visually appealing data visualizations to present the information.

3 The Ancient Seven Wonders of the World

The civil engineering feats of humankind have reached the highest peaks of the mountains and deep into the ocean, and we have built pyramids, temples and statues that are still standing today.

The seven wonders of the ancient world are a collection of man-made structures that are considered to be remarkable feats at the time they were built.

Pranav Gavali, a Data Scientist, created this graphic using data from Encyclopedia Britannica and Wikipedia to visualize the world's seven ancient wonders along with their features and modern-day locations.

The graphic perfectly illustrates how the seven wonders were built and why they are considered a wonder of the world. The Great Pyramid of Giza is the only one of the seven wonders that still stands today.

Design an infographic like this one using Visme’s pre-designed content blocks and infographic templates . Include live data visualizations by connecting to your Google or Excel spreadsheets. When connecting your Visme charts to Excel Online, select full sheets or only a specific range. Plus, when values change in your linked sheet, the chart is This is a prime example of how creative design can bring data to life

4 The World’s Population at 8 Billion

On November 15, 2022, the world’s population reached 8 billion. This is the first time in history that there have been this many people on Earth. And there can't be a more straightforward and visually appealing way to present this data than this visualization.

What makes this big data visualization stand out is its simplicity and effectiveness in conveying the message. Using a circle to represent the earth is a powerful symbol that makes the visualization easy to understand and remember.

By using colors to represent continents and lines to separate countries, the visualization effectively conveys the complexity of the world's population in a simple and visually appealing way.

5 The Top 10 Largest Nuclear Explosions

This is a prime example of how creative design can bring data to life. Beyond the interesting data visualization, it uses a unique approach, similar to an infographic, to showcase the impact and size of the largest nuclear explosions ever detonated.

It features a series of explosion image examples that help visualize each explosion's scale and impact. The use of images effectively conveys the destructive power of each blast in a way that is easy to understand and remember.

The data is presented clearly and concisely, with each explosion listed along with its country of origin.

6 Visualizing the History of Pandemics

This is an informative graphic named Visualizing the History of Pandemics by Nicholas LePan. It tells the story of all the known pandemics in the history of mankind, including the name of the disease, death toll and the approximate date the pandemic occurred.

While the exact number of victims of every disease is still under question, we can still learn from this graphic that super-spreading infections happened across all history of mankind. Statistical data of this infographic shows some diseases scaling with the growth of the population.

Striking 3D illustrations of diseases are combined with the research data from CDC, WHO, BBC, Wikipedia, Historical records, Encyclopedia Britannica and John Hopkins University. The illustrations scale according to the recorded death toll to allow scanning and recognizing data easily. 

7 It Fell From the Sky

Created by a UK-based designer, this infographic highlights beautiful data visualization of 34,000 meteorites that have fallen on the Earth. You will discover the map and timeline of the impacts per year, wrapped up in clean, stylish graphics. The visualization also shows spikes on the records and comparing the size of the biggest meteorites recorded. 

Meteorites hit almost all of Earth’s surface, but some areas seem untouched; this phenomenon could be connected with Earth’s magnetic fields. And who knows –  the future may bring us even more meteorites to explore! 

If you’re a fan of space and astronomy, you can learn more about meteorites from NASA website or check out this database of the Meteoritical Society.

Try Visme, our all-in-one design for creating stunning visualizations on meteorites in space or other research topics you’re working on.

Get the most out of Visme’s seamless integration with Google Sheets to create visualizations of live, easy-to-update data.

Link to your Google Sheets account or import through a link. Select the page and data range and connect them to your Visme chart. When the data changes in the Google Sheet, it automatically applies to the live project. Simply press the refresh button.

Sign up to Visme for free.

8 Mars Mission 2024 Promo Reel

Vivid, rich in details. This 3D graphic uses beautiful data visualizations to share the vision of the future. Space missions and sending people into space are shown in an eye-catching red-grey palette.

The complicated animation of terrain exploration, space module flight and surface graphics are breathtaking. For a moment, you feel like a Mars mission crew member with your eyes on the stars.

9 Void of the Memories

These mesmerizing circles were brought to you by one of the best-in-class street art and calligraphy authors, Pokras Lampas. Whether you would like to decipher this canvas or refer to it as a pure visual object, the unique gothic Calligrafuturism style is an eye magnet for anyone.

The project is focused on the human consciousness and the theme of dreams in the context of human memory and experience. According to the author, the future is for global unity and harmony of cultures – and it’s visible in the fusion of styles, techniques and systems used in the project graphics. 

10 Plastic Waste Pollution 

Based on data on the distribution of total plastic waste generation by continent, Jamie Kettle created this personal project to estimate the percentage of plastic waste that was inadequately disposed of. 

The infographic provides a clear and precise picture of current surface plastic mass by ocean, measuring it in a creative way. We can see plastic waste management for every country in a colored bar chart. The names of the countries that report 100% of all their plastic waste handled properly are highlighted in bold. 

One of the major findings here is that the country's GDP and efficient plastic waste management aren’t always correlated—you can see this by the irregular patterns shown in the infographic.

If you are curious about plastic waste, here are some resources for you: a guide on plastic waste, detailed info on plastic waste pollution from the UN Environment Program and Impacts of Mismanaged Trash by the United States Environmental Protection Agency.

If you’re working on a research topic like waste management, use Visme’s charts and graphs templates to highlight your findings and statistical analysis. Incorporate vertical bar graphs and align the values to the left, right or center to match your overall design.

11 Fossil Fuels

This profound and complex visualization tells us about one of the most pressing environmental issues – the increasing amount of carbon dioxide in the Earth's atmosphere.

While CO 2 buildup is responsible for climate change, the trend is projected to continue, and the infographic provides insight into when this could happen. It’s easy to notice a steady increase in fossil fuel emissions since the Industrial Revolution and the projected sharp rise in the concentration of carbon dioxide until 2100.

Find more data on CO 2 emissions in the Our World in Data research, EPA website and Worldometer stats.

12 Price of a Pandemic: Poverty Spreads Around the Globe

In this classic data visualization by National Geographic, data is placed against the dark background for better contrast and readability. Simple, comprehensive charts show us the effect of the pandemic on the income of people in various countries.

The authors distributed three levels of income range for countries with low and middle class income to provide a clear picture of the current situation. Core findings of the report were that the pandemic pushed a tremendous amount of people to extreme poverty – projected data is 100 million of people living on $1.90 per person/day.

Based on the World Bank data, the infographic provides a wide view of the exact factors influencing people’s wellbeing – from travel restrictions and job loss to wars, displacements and higher food costs. Highlights at the beginning reveal rapid shrinking of income in examined countries across all continents on a mass scale.

13 Water Consumption 

Hidden food production costs involve a great amount of freshwater. This stunning example of visualization created by Chesca Kirkland unfolds a story of water consumption required to produce certain kinds of food. 

From chocolate to cheese, coffee and beer, every product requires a certain amount of freshwater to grow or be produced. The second part of the infographic is centered on the water resources available, including the map of the water footprint per capita per year and general availability of clean water to people. 

Nominated for two C-Change Environmental and Sustainability Awards, the project won First Class Honours in Final Design Futures. Raising awareness about water sustainability is vital as we move forward to a more intelligent, AI-driven future.

We at Visme are inviting you to take up the challenge and create informative infographics that can invite change to various industry branches. Use our amazing free infographic library to create graphics for your personal projects as well as corporate or brand presentations. 

For more detailed info on the infographic creation, watch this video on the 13 major types of infographics .

14 Icebergs and Climate Change

Dedicated to “travel adventures” of this 4,200-square-kilometer iceberg, this infographic alerts people to climate change. A giant chunk of ice the length of Puerto Rico broke off the Antarctic peninsula coast to wander into the wild – and dangerously close to South Georgia Island, packed with wildlife.

The graphic compares the size of the berg with 66 countries or territories and cites that the ice mass is so large that it cannot be captured in one photograph. Besides, we can also see impressive geodata on the wildlife from the IUCN Red List of Threatened Species inhabiting the endangered South Georgia Island.

15  Cell Towers Map of the World

This stunning, elegant and creative visualization of 40 million cell towers is surely an unforgettable view. Based on OpenCelliD, the world's largest open database of cell towers, this interactive map is so far one of the most precise publicly available data sources for telecom-related projects.

We can see how the cell tower network lights up Europe and other big cities of the world; simultaneously, vast areas of “wilderness” are still present on the map. Harsh climate and low population density in the northern regions of Russia and Canada, along with central areas of Africa and Mongolia result in low quantity of cell towers in these areas.

Closeup view of this cell tower map resembles the brain structure. Similar to the neurons, axons and dendrites that create the communication network of the human body – cell towers keep humanity connected.

16  Active Satellites in Space

Created for Scientific American, this colorful and bright data visualization displays satellites in an original way. Neat and stylish satellite cluster grids sort them by country, orbit and class – business/commercial, civil, amateur/academic or defense.

The graphic details the mass of the satellites (100 kgs - 5,000 kgs), category (Test and Training, Communications, Images, Surveillance and Meteorology, Navigation and Research) and the launch date, from Nov 1974 till Aug 2020.

According to the graphic, six countries of the world control the largest amount of the satellites in orbit, and the US owns the largest share so far.

17  Covid Vaccination Tracker

Updated until July 15, 2022, this animated Covid vaccination tracker shows the percentage of people in the world given at least one dose. The infographic and data illustration displays data on the vaccination rollout plan in over 80 countries and 50 US states.

Data presented in this data visualization is sourced from the Our World in Data project at the University of Oxford. Uncluttered, simple graphs show the 7-day Covid vaccination rolling average as well. The interactive charts allow you to sort the percent of population given at least one dose by country or income.

At the bottom of the page we can see the detailed, in-depth Covid-19 vaccination statistics, with type of vaccines offered (Pfizer-BioNTech, Moderna, Sinopharm, CanSino, Oxford-AstraZeneca, Johnson & Johnson, Covishield, Sputnik V, etc.) and vaccination priority groups for various countries separately.

If you’re working on an infographic that includes map data, like this example, try Visme’s map data visualization tool . It comes equipped with a handy hover tooltip that labels country names and square footage. If you don’t need to show this data, you can hide it in the Map settings.

Create demographic visualization easily with Visme’s map templates . If you need to edit your map infographic on the go, you can do so from the mobile app on Android and iOS.

18  Blindsight

It took 4 years to create this non-commercial self-funded project. Based on the eponymous sci-fi novel by Peter Watts, this visualization row includes breathtaking renders of the solar system, four-dimensional objects as a system of data visualization and manipulation, spacesuit interface renders, cryo capsule graphics and nonhuman species concepts.

The visualization received over a dozen awards and nominations such as Best VFX Screen Power Film Festival 2020, Outstanding Achievement Award (Sci-fi Short) Indie Short Fest LA 2020, Winner Best Sound & Music Fantasy/Sci-fi film Festival 2021, Award Winner Flickfair 2020, Official selection Miami International Sci-fi Film Festival 2021 and so on.

Space mysteries have always tempted mankind. With the outstanding talent of the team behind the project, we hope to enjoy the related movie one day.

19  Gravitational Waves

Introducing to you another captivating space-themed project – the interactive visualization of gravitational wave events. Created for Science News, this space-time ripples design is amazingly minimalistic, slick and informative.

This enchanting spiral animation is saturated with useful data about black hole mergers or cosmic smashups. You can learn about the original and final mass of the mergers, total merger size and other details of gravitational wave events. 

20  Map of the Lighthouses of Ireland

Updated my map of the lighthouses of Ireland from the #30DayMapChallenge - now with the correct timings/flash patterns etc. Thanks to @IrishLights for providing additional information pic.twitter.com/eLlicP8fw5 — Neil Southall (@neilcfd1) December 8, 2020

This great animation was created as a part of 30 Day Map Challenge and it depicts all lighthouses in Ireland according to their timing and flash patterns. Here, the author visualizes data from the IrishLights – the maritime organization delivering the safety service around the coast of Ireland. 

Aside from being a vital part of the water safety of coastal waterways, lighthouses are a symbol of hope and undying light even through the toughest circumstances. That’s one of the reasons why this minimalistic graphic is so appealing.

21  Together [Hierarchical Positions of Employees in a Corporation]

Good data visualizations are essential for conveying complex information in an easily understandable way. Look at this creative way of displaying the hierarchical organization structure in a large corporation with a presence in over 100 countries. This creative data visualization example looks fun and a bit otherworldly, with muffled but contrasting colors.

Linking C-level executives to their subordinates in every branch revealed an intricate and complex corporation structure. It’s suggested that in most cases, flat patterns would fail to represent company structures correctly because of the flexibility of human relations.

22  The Search for Dark Matter

The search for the ever elusive and intriguing dark matter continues. The problem isn’t likely to get solved any time soon – but here is a striking infographic for you to follow the lead.

Quanta Magazine created this interesting data visualization to represent the types of particles that dark matter could be made of. Axions, WIMPs, ultralight dark matter or primordial black holes – any of these could be a star candidate. 

Distributing every particle type along the scale according to their mass, the visualization also provides clear, concise descriptions for every type. Additionally, you can dive into the experiments’ data. Are you the one to solve the new puzzle in particle physics?

23  2020 Autonomous Vehicle Technology Report

Concise and lean, this comprehensive report draws focus to autonomous vehicle technology and provides an insight into the hardware & software market for self-driving vehicles. 

The report starts from the visualization explaining levels of autonomous vehicle capabilities in context of the environment. We learn that the greatest challenge for Google (Waymo), Uber and other companies building self-driving vehicles is to enable the vehicle to adjust to all driving scenarios.

Sensory technology is an essential part of autonomous vehicles, and they’re designed to build an environment map and localize themselves inside that map at the same time. This requires huge computational technologies – maps created by AI systems and humans are of great help here.

Further in the report, we see the visualization of the electromagnetic spectrum and its usage for perception sensors, graphics of the time-of-flight (ToF) principle of environment sensing and various object detection sensor types such as radars, cameras, LIDARs, MEMS, etc. The next visualization covers different sets of sensors used for autonomy by Tesla, Volvo-Uber and Waymo. 

Short, clean-cut schemes of the AI architecture of autonomous vehicles, the computation/decision making environment of an autonomous vehicle and the concept of vehicle-to-everything (V2X) communication complete the report.

24  The U.S. Election Twitter Network Graph Tool

These cutting edge visuals from the U.S. Election Twitter Network Graph Tool enables a viewer to analyze social media interactions that define the online political landscape. In this case, we’re tracking the influence and connections between various political figures.

It’s clearly visible which accounts the target account is most likely to mention or reply to. The network graphs clearly show the potential of certain accounts to generate new connections and influence their followers.

You can search for specific nodes in the interactive map. All information flow between nodes is reflected in the color of the node edges. Working together with other open-source investigation tools, this graph is meant to increase transparency and help fight misinformation in social networks.

25  Map of a Fly Brain

The high-resolution nervous system map represented in the above graphic is a part of the fruit-fly’s brain – yet the complexity and harmony of the structure is astounding. 

Millions of connections between 25,000 neurons create a wiring diagram, or connectome, of connections in various parts of a fruit fly’s brain.

It’s estimated that tracking all neuron connections in the fruit fly’s brain manually would need 250 people working for 20 years at least. Google’s computational power has helped to speed up this research, and scientists are aiming to create a full fruit fly brain visualization by 2022.

26  Freight Rail Works

Our next interesting visualization highlights the advanced layers of technology Freight Rail Works uses across its infrastructure. Talented Danil Krivoruchko & Aggressive/Loop teams produced a futuristic and dynamic animation of the data-world around a train in motion.

Magnificent waves of data light up outlines of the objects and then vanish in waves as the train moves forward to the smart city. Graphics of the giant city cluster zoom out to reveal the continent routes and the beauty of a simple railway communications network. 

In the era of semi-autonomous aircrafts and drones, the simple, down-to-earth railway system looks stable but innovative in this graphic.

27  The Korean Clusters

Korean hospitals and churches experienced a burst of Covid infections among their visitors in January 2020. Having linked connections between the confirmed cases, scientists were able to trace back the first case and build a tree of contacts between the affected people.

Tracking the timeline of the first patient’s actions revealed that this person caused thousands of infections. Wandering sick for a few days resulted in over 30 more people infected. Subsequently, the Shincheonji Church cluster with 5,016 infected people accounted for at least 60% of all cases in South Korea at that time.

28  2020’s Biggest Tech Mergers and Acquisitions

Despite the fact that for most businesses 2020 was a devastating year with grim outcomes, this data visualization shows that Big Tech experienced a growth boost. It’s not surprising that people working remotely increasingly need digital services of all kinds.

The graphic shows the biggest tech mergers and acquisitions closed in 2020, together with the short description of the acquired company, acquiring company, deal amount and deal date. While the chart is visually busy, it’s also innovative and visually appealing.

If you need a market report from your industry area, grab the data from Crunchbase and build your own custom branded infographic via our data visualization tool quickly and easily. Sign up free .

29  Stolen Paintings

This wonderful visualization was created for Visual Data, a column on "La Lettura," the cultural supplement of "Corriere Della Sera."

From 1900 to the present day, the infographic reveals the details of 40 stolen paintings. Neutral, minimalistic visuals highlight the painting’s artist, the year when the painting was created and the year of theft. 

It was shocking to find out that the majority of thefts took place during the last 20 years (2000-2020) – and most of the art works have never been recovered.

30  House Of Cards LIDAR

House of Cards from Brendan Dawes on Vimeo .

Take a look at the last cool data visualization in this list – the rework of Radiohead's House of Cards video. This astonishing art was created on the basis of around one minute of the LIDAR data.

Motion graphics of particles scattered around a person’s face create an unforgettable image. The hero of the story in the video is clearly emotional – but we can’t tell anymore whether this person is even human. 

AI generated data can be beautiful, but how can you take control?

Data Visualization FAQs

What is the most popular form of data visualization.

Bar graphs, bar charts or column charts are the most popular type of data visualization.

Bar charts are best for comparing numerical values across categories using rectangles (or bars) of equal width and variable height. You can use bar graphs to compare items between different groups, measure changes over time and identify patterns or trends.

Other popular forms of data visualization include pie charts , line graphs , area charts , histograms , pivot tables, boxplots, scatter plots , radar charts and choropleth maps.

What Are the Benefits of Data Visualization?

Here’s how data visualization helps users to make the most of their data.

• Data visualizations make data clear, concise and easy to understand. Users can easily unlock key values from massive data sets, interpret them and draw conclusions.
• Visualization allows business users to identify relationships, patterns and trends between data, giving it greater meaning. You can easily uncover fresh insights and focus areas that require more attention.
• Creative data visualization is about creating compelling narratives through the use of graphics, diagrams and visual analytics. Visualizing data helps users tell better stories and convey messages in an engaging manner.
• Data visualization can significantly increase the pace of decision-making processes since it makes it simple for us to understand visual data. It’s no surprise, as The Wharton School of Business says that data visualization can cut down on meeting time by up to 24% .

Visualizing data helps quickly spot any errors so they can be removed. If you still doubt the importance of data visualization, this article about 50 data visualization statistics might change your thought process.

What are the Best Practices of Data Visualization?

Below are data visualization best practices to help you present data in an engaging and appealing way.

• Specify the audience and their unique needs. Your data visualization should be crafted to communicate, provide real value and meet the needs of the target audience.
• Define a Clear Purpose. Specify what questions you want your data visualizations to answer or the problems you want them to solve.
• Keep your data clean. Before visualizing your data, make sure to fix or remove incomplete, duplicate, incorrect, corrupted and incorrectly formatted data within your dataset.
• Use the right visuals. With so many charts available, identify the best type for presenting the particular data type you’re working on.
• Keep your data organized. At a glance, your audience should be able to view and digest information quickly.
• Use the right color combination.

Read our article to learn more about data visualization best practices.

Create Your Own Data Visualizations

If you are feeling inspired by these cool data visualizations, use our data visualization software to convert disparate data into clean, comprehensive visuals using the best data visualization techniques . You'll find an extensive library of customizable charts and graphs including bubble charts, bar graphs , line charts , scatter plots, and much more. 

Wondering if Visme's data visualization tools are right for you? Take a look at what one of our satisfied customers, Cassandra C. | Owner, has to say:

“I also appreciate the wide range of features, including charts, graphs, and other visuals that can be used to present data in a clear and concise way. Overall, I'm very happy with Visme and would highly recommend it to anyone looking for a fun, user-friendly tool to create visuals.”

To learn more about creating your own data visualizations, check out our detailed guide on data visualization types and the introduction to data viz on our blog.

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Mindfulness

How visualization can benefit your well-being, visualization can help you reach a range of goals..

Updated November 20, 2023 | Reviewed by Gary Drevitch

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Co-written by Kelsey Schultz and Tchiki Davis

Visualization, also called mental imagery , is essentially seeing with the mind’s eye or hearing with the mind’s ear. That is, when visualizing you are having a visual sensory experience without the use of your eyes. In fact, research has shown that visualization recruits the same brain areas that actual seeing does (Pearson et al., 2015).

Humans have evolved to rely heavily on our eyesight, making us highly visually-oriented creatures. Because our brains are adapted to easily process and comprehend visual information, visualization can be a powerful tool for influencing our thoughts, emotions , and behaviors. In fact, research has shown that processing emotions using visualization is more powerful than processing verbally (Blackwell et al., 2019). For example, when research participants listen to descriptions of emotionally valenced situations (i.e., “your boss telling you that they are disappointed with your work”), participants who are instructed to imagine themselves in the situation demonstrate a greater change in mood than those that are instructed only to think about the situation verbally (Blackwell et al., 2019).

There appear to be a number of emotional, cognitive, and behavioral benefits to practicing visualization.

​Emotional. Some forms of visualization have been shown to increase optimism and other positive emotions (Murphy et al., 2015). It has also been shown to be a useful method for regulating negative emotions such as anxiety or overwhelm (Blackwell et al., 2019).

Cognitive. Visualization techniques can be used to facilitate some kinds of decision-making and problem-solving (Blackwell et al., 2019). For example, visualization might be helpful when planning the best route to take on your upcoming road trip. Visualization techniques, such as the mind palace, are also an effective means of improving memory . The mind palace technique involves using a place you are very familiar with, such as your bedroom, and using different locations within that space as mnemonic devices associated with a particular piece of information you are trying to store.

Behavioral. Visualization can also help us achieve our goals by allowing us to determine the appropriate sequences of actions needed to reach our goal and identify any potential obstacles we might encounter as we proceed toward a goal. In other words, we can use visualization as a sort of rough draft for our plans by imagining each step we need to take to reach our goal, what each step might include, what might go wrong, and the ways in which we might need to prepare.

Visualization Tools

Music. Visualization music is music that is specifically intended to facilitate visualization and similar meditative processes. This kind of music can also be described as atmospheric or ambient, as the purpose is not to occupy your attention , but rather to help you focus attention on your visualizations.

Boards. Visualization boards, also called vision boards , are visual representations of your goals , intentions, and desires. Vision boards are typically poster-sized and include a collage-type arrangement of images that symbolize different facets of your goals and intentions. Vision boards are useful for ensuring that your goals remain salient. That is, by creating a visual representation of your goals, you can easily look back at your vision board and remind yourself of the intentions you set. When your intentions are at the forefront of your mind, you are more likely to act in accordance with them.

Guided Imagery

Guided imagery is a visualization exercise in which you engage all of your senses as you imagine yourself in a positive, peaceful environment.

• To begin, find a comfortable position, close your eyes, and begin breathing slowly and deeply as you start to relax.
• Next, visualize a place where you feel calm and content. This can be a place you’ve been before, a place you would like to go, or a place that is wholly the product of your imagination . Engage all of your senses to add depth and detail to the place you are visualizing. Can you feel a soft breeze? Do you hear birds or the sound of water lapping on the shore?
• Reflect on the calm that emerges as you move deeper into your vision.
• As you inhale, imagine peace washing over you and filling your body.
• As you exhale, imagine exhaustion, tension , and stress being washed away.
• Stay in your vision for as long as you like.

Visualization is a simple yet powerful technique that we can use to improve many facets of our lives. We can use visualization to improve our mood, help us remember important information, facilitate problem-solving and decision-making , and boost progress toward our goals. Depending on the purpose, there are many forms of visualization we can practice. For example, if we are trying to regulate our mood we might try visualization meditation , whereas if we are trying to solidify our goals for the new year we might use a vision board or a mind map .

Adapted from a post on visualization published by The Berkeley Well-Being Institute.

Blackwell, S. E. (2019). Mental imagery: From basic research to clinical practice. Journal of Psychotherapy Integration, 29(3), 235.

Murphy, S. E., O’Donoghue, M. C., Drazich, E. H., Blackwell, S. E., Nobre, A. C., & Holmes, E. A. (2015). Imagining a brighter future: the effect of positive imagery training on mood, prospective mental imagery and emotional bias in older adults. Psychiatry Research, 230(1), 36-43.

Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental imagery: functional mechanisms and clinical applications. Trends in cognitive sciences, 19(10), 590-602.​

Tchiki Davis, Ph.D. , is a consultant, writer, and expert on well-being technology.

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

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

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

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

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What Is Data Visualization?

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

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

Data Visualization Techniques

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

Here are some important data visualization techniques to know:

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

1. Pie Chart

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

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

2. Bar Chart

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

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

3. Histogram

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

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

4. Gantt Chart

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

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

5. Heat Map

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

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

6. A Box and Whisker Plot

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

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

7. Waterfall Chart

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

8. Area Chart

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

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

9. Scatter Plot

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

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

10. Pictogram Chart

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

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

11. Timeline

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

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

12. Highlight Table

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

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

13. Bullet Graph

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

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

14. Choropleth Maps

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

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

15. Word Cloud

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

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

16. Network Diagram

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

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

17. Correlation Matrix

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

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

Other Data Visualization Options

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

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

Tips For Creating Effective Visualizations

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

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

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

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

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

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

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

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

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

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

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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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18 Types of Diagrams You Can Use to Visualize Data (Templates Included)

Have you ever found yourself stuck while trying to explain a complex concept to someone? Or struggling to put your idea into words?

This is where diagrams come in.

While simple text is best for highlighting figures or information, diagrams are handy for conveying complex ideas and loads of information without overwhelming your audience. They can visualize almost anything, from numerical data to qualitative relationships, making them versatile tools in numerous fields.

Whether you’re in the academe or enterprise setting, this guide is for you. We’ll explore the different types of diagrams with a brief explanation for each type, the best time to use a diagram type, and how you can use them to be a better visual storyteller and communicator. You’ll also find examples and templates for each type of diagram.

Let’s get on with it.

You can also follow along by creating a free account . Select a template to get started.

What exactly is a diagram? 

A diagram is a visual snapshot of information. Think of diagrams as visual representations of data or information that communicate a concept, idea, or process in a simplified and easily understandable way. You can also use them to illustrate relationships, hierarchies, cycles, or workflows. 

Diagrams aren’t just used to show quantitative data, such as sales earnings or satisfaction ratings with a diagram. They’re equally helpful if you want to share qualitative data. For example, a diagram could be used to illustrate the life cycle of a butterfly, showcasing each transformation stage. 

Now, let’s jump into the various types of diagrams, ranging from simple flow charts to the more complex Unified Modeling Language (UML) diagrams.

18 diagram types and when to use each type 

Whether you’re doing data analysis or need a simple visual representation of data, there is a wide array of diagrams at your fingertips. If you’re having a hard time choosing the right diagram for your data visualization needs, use the list below as a quick guide. 

1. Flowchart 

A flowchart is a type of diagram that acts as a roadmap for a process or workflow. It uses shapes and arrows to guide you through each step, making complex procedures simple to understand.

Flowcharts are best for : Simplifying complex processes into understandable stages, making it easier for your readers to follow along and see the ‘big picture”. 

2. Line graph

Line graphs , sometimes called line charts, visualizes numerical data points connected by straight lines. In a line graph or line chart, data points representing different time periods are plotted and connected by a line. This helps with easy visualization of trends and patterns.

Line graphs are best for: Representing the change of one or more quantities over time, making them excellent for tracking the progression of data points.

3. Bar chart 

A bar chart , often interchangeable with bar graphs, is a type of diagram used primarily to display and compare data. For this diagram type, rectangular bars of varying lengths represent data of different categories or groups. Each bar represents a category, and the length or height of the bar corresponds to the numeric data or quantity.

Variations of bar charts include stacked bar charts, grouped bar charts, and horizontal bar charts. 

Bar charts are best for : Comparing the frequency, count, or other measures (such as average) for different categories or groups. A bar chart is particularly useful if you want to display data sets that can be grouped into categories.

4. Circle diagram or pie chart

A pie chart is a circular diagram that represents data in slices. Each slice of the pie chart represents a different category and its proportion to the whole.

Pie charts are best for: Displaying categorical data where you want to highlight each category’s percentage of the total.

5.Venn diagrams

A Venn diagram compares the differences and similarities of groups of things. As a diagram based on overlapping circles, each circle in a Venn diagram represents a different set, and their overlap represents the intersection of the data sets. 

Venn diagrams are best for : Visualizing the relationships between different groups of things. They are helpful when you want to show areas of overlap between elements. A good example is if you want to compare the features of different products or two overlapping concepts, like in the Ikigai Venn diagram template below. Easily create your Venn diagram with Piktochart’s online Venn diagram maker .

6. Tree diagrams

A tree diagram is a diagram that starts with one central idea and expands with branching lines to show multiple paths, all possible outcomes, decisions, or steps. Each ‘branch’ represents a possible outcome or decision in a tree diagram, moving from left to right. Tree diagrams are best for : Representing hierarchy like organizational roles, evolutionary relationships, or possible outcomes of events like when a company launches a product. 

7. Organizational chart 

Organizational charts are diagrams used to display the structure of an organization. In an organizational chart, each box or node represents a different role or department, and lines connecting the boxes illustrate the lines of authority, communication, and responsibility. The chart typically starts with the highest-ranking individual or body (like a CEO or Board of Directors) at the top and branches downwards to various levels of management and individual employees.

Organizational charts are best for : Showing relationships between different members and departments in a company or organization. 

8. Gantt charts 

Gantt charts are typically used in project management to represent the timeline of a project. They consist of horizontal bars, with each bar representing a task or activity.

For this type of diagram, each chart is represented by a horizontal bar spanning from its start date to its end date. The length of the bar corresponds to the duration of the task. Tasks are listed vertically, often in the order they need to be completed. In some projects, tasks are grouped under larger, overarching activities or phases.

Gantt charts are best for : Projects where you need to manage multiple tasks that occur over time, often in a specific sequence, and may depend on each other.

9. Unified Modeling Language (UML) diagram

Software engineers use Unified Modeling Language (UML) diagrams to create standardized diagrams that illustrate the building blocks of a software system.

UML diagrams, such as class diagrams, sequence diagrams, and state diagrams, provide different perspectives on complex systems. Class diagrams depict a system’s static structure, displaying classes, attributes, and relationships. Meanwhile, sequence diagrams illustrate interactions and communication between system entities, providing insight into system functionality. 

UML diagrams are best for : Visualizing a software system’s architecture in software engineering.

10. SWOT analysis diagrams 

A SWOT analysis diagram is used in business strategy for evaluating internal and external factors affecting the organization. The acronym stands for Strengths, Weaknesses, Opportunities, and Threats. Each category is represented in a quadrant chart, providing a comprehensive view of the business landscape.

SWOT diagrams are best for : Strategic planning and decision-making. They represent data that can help identify areas of competitive advantage and inform strategy development.

Piktochart offers professionally-designed templates to create diagrams , reports , presentations , brochures , and more. Sign up for a free account today to create impressive visuals within minutes.

11. Fishbone diagram 

Fishbone diagrams, sometimes called cause-and-effect diagrams,  are used to represent the causes of a problem. They consist of a central idea, with different diagrams or branches representing the factors contributing to the problem.

Fishbone diagrams are best for : Brainstorming and problem-solving sessions.

12. Funnel chart

A funnel chart is a type of diagram used to represent stages or progress. In a funnel chart, each stage is represented by a horizontal bar, and the length of the bar corresponds to the quantity or value at that stage. The chart is widest at the top, where the quantity or value is greatest, and narrows down to represent the decrease at each subsequent stage.

Funnel charts are best for: Visual representation of the sales pipeline or data visualization of how a broad market is narrowed down into potential leads and a select group of customers.

13. SIPOC diagrams

A SIPOC diagram is used in process improvement to represent the different components of a process. The acronym stands for Suppliers, Inputs, Process, Outputs, and Customers.

SIPOC diagrams are best for: Providing a high-level view of a process which helps visualize the sequence of events and their interconnections.

14. Swimlane diagrams

Swimlane diagrams are best for mapping out complex processes that involve multiple participants or groups.

Keep in mind that each lane (which can be either horizontal or vertical) in a swimlane diagram represents a different participant or group involved in the process. The steps or activities carried out by each participant are plotted within their respective lanes. This helps clarify roles and responsibilities as well as the sequence of events and points of interaction.

Swimlane diagrams are best for : Visualizing how different roles or departments interact and collaborate throughout a workflow or process.

15. Mind maps

A mind map starts with a central idea and expands outward to include supporting ideas, related subtopics, concepts, or tasks, which can be further subdivided as needed. The branches radiating out from the central idea represent hierarchical relationships and connections between the different pieces of information in a mind map.

Mind maps are best for : Brainstorming, taking notes, organizing information, and visualizing complex concepts in a digestible format.

16. Scatter Plots

Scatter plots are used to compare data and represent the relationship between two variables. In a scatter plot, each dot represents a data point with its position along the x and y axes representing the values of two variables.

Scatter plots are best for : Observing relationships and trends between the two variables. These scatter plots are useful for regression analysis, hypothesis testing, and data exploration in various fields such as statistics, economics, and natural sciences.

17. PERT chart

PERT (Project Evaluation Review Technique) charts are project management tools used to schedule tasks. Each node or arrow represents each task, while lines represent dependencies between tasks. The chart includes task duration and earliest/latest start/end times.

Construction project managers often use PERT charts to schedule tasks like design, site prep, construction, and inspection. Identifying the critical path helps focus resources on tasks that impact the project timeline.

PERT charts are best for : Visualizing the sequence of tasks, the time required for each task, and project timelines.

18. Network diagrams

A network diagram visually represents the relationships between elements in a system or project. In network diagrams, each node represents an element, such as a device in a computer network or a task in a project. The lines or arrows connecting the nodes represent the relationships or interactions between these elements.

Network diagrams are best for: Visually representing the relationships or connections between different elements in a system or a project. They are often used in telecommunications, computer networking, project management, and organization planning.

Choosing the right diagram starts with a good understanding of your audience

Understanding your audience’s needs, expectations, and context is necessary before designing diagrams. The best diagram is not the one that looks the most impressive but the one that communicates complex information most clearly and effectively to your intended audience.

Make professional diagrams for free with no design experience with Piktochart’s online diagram maker . Sign up for free .

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What Sales Data Visualization can do for Your Sales Team and How to Execute it Like a Pro

By Pohan Lin, Senior Web Marketing and Localizations Manager at Databricks

As profoundly significant as our raw data is, it’s not very good at telling us a story. Spreadsheets often leave us confused and unenlightened, overwhelming us with static data that blinds us to critical insights.

Sales data visualization brings numerical and textual data to life. Visual representation communicates raw data clearly and concisely, enabling sales teams to extract meaning from large volumes of data.

What is data visualization? 

Data visualization is the graphical representation of data using visual elements such as graphs, charts, maps, etc. It transforms your complex raw data into an easily digestible visualization designed to identify trends, patterns, relationships, outliers and more. 

Data visualizations tap into the fact that we respond to visual information quicker than textual information. Presenting data in a visual format can empower sales teams to make faster interpretations and deliver timely results. 

It also facilitates effective communication with stakeholders and customers, who will likely engage better with your charts than your spreadsheets. 

What type of data visualization should I use? 

From bar charts to bubble graphs, there are many techniques you can use to visualize your data. The specific visualization you use for any given dataset will depend on the story you’re trying to tell.  

Here are some of the most popular sales data visualization techniques. 

Bar charts 

Bar charts are the most popular type of data visualization tool because they’re simple and easy to interpret. They’re used to compare quantities within related categories, such as the number of sales across years or customers by age. The x-axis highlights the categories, and the y-axis represents the value.

If you want to compare two or more values or identify your highest/lowest-performing activities, bar charts are an effective solution. Using stacked bar charts or grouped bar charts give deeper exploration, like the example below.

Pie charts and donut charts

Pie charts illustrate percentages as part of a whole. The chart is divided into segments or “slices” that represent numerical proportions, with the total sum of all slices always equal to 100%.

Pie charts work best for visually representing simple percentage data. For sales data visualization, this might be something such as monthly sales across your different channels or revenue generated from specific products or product types.  

Line graphs 

Line graphs are good for charting continuous data sets to uncover trends or relationships between variables over time. The data points are represented as dots joined by straight lines, aiding the visualization of historical and present patterns within datasets. You can see this easily in the visualization example below:

Line graphs can track the changes of multiple different groups in one visualization. For example, you might document the yearly sales of different product types within the same period or track your monthly revenue with one of your startup growth strategies . Just remember to use different colors for each category.  

Scatter plots

Often used when datasets contain different data points, scatter plot charts show relationships between two variables. They often act as a visual aid for representing and identifying data patterns, trends, correlations and anomalies.

You might use scatter plot charts to visualize the relationships between average sales and the time of day or e-commerce sales and inflation.

Funnel charts

We’re all familiar with the marketing funnel and sales funnel, but did you know you can use the funnel method to visualize any sequential step? For example, you can use it to visualize your unique buyer journey, identify drop-off points and bottlenecks, or improve your sales pipeline .  

Heat maps 

Heat maps use color shading, gradients and saturations to identify differences in values. Usually, the darker/warmer the color, the higher or more intense the result. For example, the heatmap below makes it clear from a sales perspective that in 2016 a business’ Brooklyn store achieved the most sales, while their Staten Island store resulted in consistently low sales.

Heat maps are good for spotting intensities and trends as well as analyzing performance. They can be used for everything from rating values on a scale (i.e. customer sentiments) to location assessments. 

How sales data visualization benefits your sales team

Big data is everywhere. And despite its increasing accessibility, raw data is still time-consuming to sift through. This is problematic for sales teams which should be spending as little time on analytical interpretation, and as much time on analytical application, as possible. 

Data visualizations mitigate this problem. Here are some of the ways visualizations can benefit your team. 

Improves metric and goal-tracking capabilities

You might only do yearly, quarterly or monthly reports, but that doesn’t mean you can’t consult your KPIs consistently. Data visualizations help you keep track of your goals and enable you to make swift goal-oriented business decisions, such as redesigning a poorly-performing landing page or contacting your growing number of idle customers as part of your customer retention strategy . 

Identifies trends and hidden patterns 

Performance analysis, pipeline analysis, market research analysis, forecasting–these critical activities depend on identifying trends and patterns. Data visualization makes spotting trends, patterns and outliers easier, increasing the speed and accuracy of business decisions. 

Improves productivity

People process images much faster than text. Up to 600,000 times faster, in fact. Transforming your raw data into a visual format makes it easier to interpret. Complex data patterns, trends, relationships, etc., can be understood at a glance. This means teams can spend less time wading through data and more time applying it to real-world business decisions.

Improves sales reporting 

Your sales reports need to be engaging, informational and actionable. Your team should be able to extract valuable insights from your report without trawling through a bunch of text. Data visualizations communicate your findings in a snackable fashion to improve report engagement.

If you’re using a Google Data Studio alternative , your sales reports can also benefit from perks such as direct data integration channels. This enables you to access and utilize channel-wide campaign data for your visualizations–right inside your reporting platform.

Top tips for executing sales data visualization like a pro

Before you get stuck into creating your first data visualization, here are a couple of fundamental best practices. 

Choose the right type of visual

There’s no one-size-fits-all approach when it comes to data visualization. If you choose the wrong visual format, you risk confusing your audience and skewing their interpretations. So, when you’re considering which visual to use, ask yourself what story you’re trying to tell. For example, are you trying to:

• Show comparisons between two or more values? 
• Document values over a length of time?
• Analyze trends or patterns?
• Highlight a composition?

All of these questions–and more–will dictate the type of visualization you choose. In your sales reports, you’ll probably use multiple visualization types to showcase different things.

Tell data stories through colors 

Colors don’t just make your visualizations more aesthetically eye-catching. They also help accentuate specific information and distinguish different categories. This makes for a more intuitive viewing experience.

Pairing two complementary colors with a neutral color like gray or white is an effective way to create a clean-looking visual that is easy to interpret. Using intuitive colors (like red for warm and blue for cold) helps viewers process your data’s story faster. Take a look at the image below: the colors are so intuitive that we immediately know what the map represents without having to scrutinize the key. 

While using the right colors can enhance your visualization, using the wrong colors can ruin it completely. Beware of using too many different colors to encode data and too many shades of one color, contrasting colors, or arbitrary colors. Remember, the colors you use should aid the telling of your data’s story; they’re more than mere aesthetic tools!

Keep it clear and simple 

The whole point of data visualization is to take raw data–which is usually presented in a complex, unengaging way–and transform it into an easy-to-digest visual aid. If you stuff too much data into one graph or chart, you risk defeating the purpose of your visualization goals.

Less is more. Limit the number of categories you use in charts (between four and seven is a good rule of thumb). Omit information that isn’t essential to the immediate story you’re trying to tell. Don’t be afraid to use single-value charts to communicate straightforward data or to split your data into two separate graphs to improve clarity.

Strive to communicate your data’s story as succinctly as possible. 

The takeaway

Sales data visualization empowers sales teams to easily decipher raw data and identify the trends, patterns, opportunities and threats that govern sales processes and goals. With the ability to quickly and intuitively understand complex data, sales teams can execute time-sensitive business initiatives and take advantage of sales-boosting opportunities. 

Of course, for your graphs and charts to be impactful within your sales reports, they still need to be supported by text. However, for the swift delivery of actionable insights, visualizations are powerful solutions. 

This article is part of the Crunchbase Community Contributor Series. The author is an expert in their field and we are honored to feature and promote their contribution on the Crunchbase blog.

Please note that the author is not employed by Crunchbase and the opinions expressed in this article do not necessarily reflect official views or opinions of Crunchbase, Inc .

Pohan Lin is the senior web marketing and localizations manager at Databricks, a global data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture and azure data lake tutorial . With over 18 years of experience in web marketing, online SaaS business, and e-commerce growth, Lin is passionate about innovation and dedicated to communicating the significant impact data has in marketing.

• Sales Leadership
• Originally published August 30, 2022, updated July 9, 2024

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The Power of Visualization in Math

Creating visual representations for math students can open up understanding. We have resources you can use in class tomorrow.

When do you know it’s time to try something different in your math lesson?

For me, I knew the moment I read this word problem to my fifth-grade summer school students: “On average, the sun’s energy density reaching Earth’s upper atmosphere is 1,350 watts per square meter. Assume the incident, monochromatic light has a wavelength of 800 nanometers (each photon has an energy of 2.48 × 10 -19 joules at this wavelength). How many photons are incident on the Earth’s upper atmosphere in one second?”

My students couldn’t get past the language, the sizes of the different numbers, or the science concepts addressed in the question. In short, I had effectively shut them down, and I needed a new approach to bring them back to their learning. So I started drawing on the whiteboard and created something with a little whimsy, a cartoon photon asking how much energy a photon has.

Immediately, students started yelling out, “2.48 × 10 -19 joules,” and they could even cite the text where they had learned the information. I knew I was on to something, so the next thing I drew was a series of boxes with our friend the photon.

If all of the photons in the image below were to hit in one second, how much energy is represented in the drawing?

Students realized that we were just adding up all the individual energy from each photon and then quickly realized that this was multiplication. And then they knew that the question we were trying to answer was just figuring out the number of photons, and since we knew the total energy in one second, we could compute the number of photons by division.

The point being, we reached a place where my students were able to process the learning. The power of the visual representation made all the difference for these students, and being able to sequence through the problem using the visual supports completely changed the interactions they were having with the problem.

If you’re like me, you’re thinking, “So the visual representations worked with this problem, but what about other types of problems? Surely there isn’t a visual model for every problem!”

The power of this moment, the change in the learning environment, and the excitement of my fifth graders as they could not only understand but explain to others what the problem was about convinced me it was worth the effort to pursue visualization and try to answer these questions: Is there a process to unlock visualizations in math? And are there resources already available to help make mathematics visual?

I realized that the first step in unlocking visualization as a scaffold for students was to change the kind of question I was asking myself. A powerful question to start with is: “How might I represent this learning target in a visual way?” This reframing opens a world of possible representations that we might not otherwise have considered. Thinking about many possible visual representations is the first step in creating a good one for students.

The Progressions published in tandem with the Common Core State Standards for mathematics are one resource for finding specific visual models based on grade level and standard. In my fifth-grade example, what I constructed was a sequenced process to develop a tape diagram—a type of visual model that uses rectangles to represent the parts of a ratio. I didn’t realize it, but to unlock my thinking I had to commit to finding a way to represent the problem in a visual way. Asking yourself a very simple series of questions leads you down a variety of learning paths, and primes you for the next step in the sequence—finding the right resources to complete your visualization journey.

Posing the question of visualization readies your brain to identify the right tool for the desired learning target and your students. That is, you’ll more readily know when you’ve identified the right tool for the job for your students. There are many, many resources available to help make this process even easier, and I’ve created a matrix of clickable tools, articles, and resources .

The process to visualize your math instruction is summarized at the top of my Visualizing Math graphic; below that is a mix of visualization strategies and resources you can use tomorrow in your classroom.

Our job as educators is to set a stage that maximizes the amount of learning done by our students, and teaching students mathematics in this visual way provides a powerful pathway for us to do our job well. The process of visualizing mathematics tests your abilities at first, and you’ll find that it makes both you and your students learn.

To revisit this article, visit My Profile, then View saved stories .

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A Trippy Visualization Charts the Internet's Growth Since 1997

In November 2003, security researcher Barrett Lyon was finishing college at California State University, Sacramento, while working full time as a penetration tester—a hacker companies hire to find weaknesses in their own digital systems. At the beginning of each job, Lyon would do some basic reconnaissance of the customer's infrastructure: “case the joint,” as he puts it. He realized he was essentially refining and repeating a formula to map what the new target network looked like. “That formula ended up being an easy piece of software to write, so I just started having this software do all the work for me,” Lyon says.

At lunch with his colleagues one day, Lyon suggested that he could use his network mapper to sketch the entire internet. “They thought that was pretty funny, so they bet me 50 bucks I couldn't do it," he says. So he did.

What followed was a vast, celestial jumble of thin, overlapping lines, starbursts, and branches in a static image that depicted the global internet of the early 2000s. Lyon called the piece Opte, and while his betting colleagues were skeptical of the visual rats nests he produced at first, the final product immediately started attracting fans on Slashdot and beyond .

Lyon's original Opte Internet Map from 2003.

Now Opte is back in an entirely new and updated form . The original version used “traceroutes,” diagnostic commands that scout different paths through a network, to visualize the internet in all of its enormous complexity. But traceroutes can be blocked, spoofed, or have other inaccuracies. So in a 2010 exhibit of the original Opte at the Museum of Modern Art in New York, Lyon explored an alternative. Instead of basing the map on traceroutes, Lyon used Border Gateway Protocol routing tables, the subway maps of the internet, to get a more accurate view. Now he's carried that approach into this next generation.

The original Opte was a still image, but the 2021 version is a 10K video with extensive companion stills, using BGP data from University of Oregon's Route Views project to map the global internet from 1997 to today. Lyon worked on the visualization for months and relied on a number of applications, tools, and scripts to produce it. One is a software package called Large Graph Layout, originally designed to render images of proteins, that attempts hundreds and hundreds of different visual layouts until it finds the most efficient, representative solution. Think of it as a sort of web of best fit, depicting all of the internet's sprawling, interconnected data routes. The closer to the center a network is, the bigger and more interconnected it is.

Present day, from Opte The Internet: 1997 - 2021.

While the concept—to map and visualize the whole internet—remains the same, animating its evolution and expansion over almost 25 years allows the new version of Opte to be more interactive. The materials are all free for non-commercial use and Lyon hopes the piece will be particularly valuable to educators and engaging for students. Viewers can see details about the different network regions, and Lyon made some diagrams and videos that call out specific points of interest. One shows China's network space, for example, with its two heavily controlled connections in and out. Lyon also highlights much of the United States military's internet presence, including NIPRNET, the Department of Defense's Non-Classified Internet Protocol Network, and SIPRNET, the Secret Internet Protocol Network.

Zooming in on China's internet, present day.

By moving through time, Opte also makes major internet events tangible, like Iran's national 2019 internet shutdown and Myanmar's recent internet blackout in the last few weeks. Lyon says he's still collecting data to give a robust picture of recent events. Opte even shows BGP route leaks , incidents where data meant to flow on a certain path was accidentally or maliciously redirected to travel over other parts of the network. The new project is constructed to be easily updatable so Lyon can revise it as time passes.

While Opte is a striking and powerful visualization of the internet's size and impact, Lyon says the piece also ultimately depicts the internet's failure to become truly decentralized and insuppressible in its current form, particularly in countries and geographic regions that have limited points of connectivity to the global internet.

“When I look at it, each one of those little squiggles and wiggles is human beings doing something,” Lyon says. “People actually using the network, building the network, literally going across oceans and mountains with fiber optic cables and digging ditches. All of that work is reflected in one snapshot. But some countries are not actually very connected and that enables control.”

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Visual Representation

What is visual representation.

Visual Representation refers to the principles by which markings on a surface are made and interpreted. Designers use representations like typography and illustrations to communicate information, emotions and concepts. Color, imagery, typography and layout are crucial in this communication.

Alan Blackwell, cognition scientist and professor, gives a brief introduction to visual representation:

• Transcript loading…

We can see visual representation throughout human history, from cave drawings to data visualization :

Art uses visual representation to express emotions and abstract ideas.

Financial forecasting graphs condense data and research into a more straightforward format.

Icons on user interfaces (UI) represent different actions users can take.

The color of a notification indicates its nature and meaning.

Van Gogh's "The Starry Night" uses visuals to evoke deep emotions, representing an abstract, dreamy night sky. It exemplifies how art can communicate complex feelings and ideas.

© Public domain

Importance of Visual Representation in Design

Designers use visual representation for internal and external use throughout the design process . For example:

Storyboards are illustrations that outline users’ actions and where they perform them.

Sitemaps are diagrams that show the hierarchy and navigation structure of a website.

Wireframes are sketches that bring together elements of a user interface's structure.

Usability reports use graphs and charts to communicate data gathered from usability testing.

User interfaces visually represent information contained in applications and computerized devices.

This usability report is straightforward to understand. Yet, the data behind the visualizations could come from thousands of answered surveys.

© Interaction Design Foundation, CC BY-SA 4.0

Visual representation simplifies complex ideas and data and makes them easy to understand. Without these visual aids, designers would struggle to communicate their ideas, findings and products . For example, it would be easier to create a mockup of an e-commerce website interface than to describe it with words.

Visual representation simplifies the communication of designs. Without mockups, it would be difficult for developers to reproduce designs using words alone.

Types of Visual Representation

Below are some of the most common forms of visual representation designers use.

Text and Typography

Text represents language and ideas through written characters and symbols. Readers visually perceive and interpret these characters. Typography turns text into a visual form, influencing its perception and interpretation.

We have developed the conventions of typography over centuries , for example, in documents, newspapers and magazines. These conventions include:

Text arranged on a grid brings clarity and structure. Gridded text makes complex information easier to navigate and understand. Tables, columns and other formats help organize content logically and enhance readability.

Contrasting text sizes create a visual hierarchy and draw attention to critical areas. For example, headings use larger text while body copy uses smaller text. This contrast helps readers distinguish between primary and secondary information.

Adequate spacing and paragraphing improve the readability and appearance of the text. These conventions prevent the content from appearing cluttered. Spacing and paragraphing make it easier for the eye to follow and for the brain to process the information.

Balanced image-to-text ratios create engaging layouts. Images break the monotony of text, provide visual relief and illustrate or emphasize points made in the text. A well-planned ratio ensures neither text nor images overwhelm each other. Effective ratios make designs more effective and appealing.

Designers use these conventions because people are familiar with them and better understand text presented in this manner.

This table of funerals from the plague in London in 1665 uses typographic conventions still used today. For example, the author arranged the information in a table and used contrasting text styling to highlight information in the header.

Illustrations and Drawings

Designers use illustrations and drawings independently or alongside text. An example of illustration used to communicate information is the assembly instructions created by furniture retailer IKEA. If IKEA used text instead of illustrations in their instructions, people would find it harder to assemble the furniture.

IKEA assembly instructions use illustrations to inform customers how to build their furniture. The only text used is numeric to denote step and part numbers. IKEA communicates this information visually to: 1. Enable simple communication, 2. Ensure their instructions are easy to follow, regardless of the customer’s language.

© IKEA, Fair use

Illustrations and drawings can often convey the core message of a visual representation more effectively than a photograph. They focus on the core message , while a photograph might distract a viewer with additional details (such as who this person is, where they are from, etc.)

For example, in IKEA’s case, photographing a person building a piece of furniture might be complicated. Further, photographs may not be easy to understand in a black-and-white print, leading to higher printing costs. To be useful, the pictures would also need to be larger and would occupy more space on a printed manual, further adding to the costs.

But imagine a girl winking—this is something we can easily photograph. 

Ivan Sutherland, creator of the first graphical user interface, used his computer program Sketchpad to draw a winking girl. While not realistic, Sutherland's representation effectively portrays a winking girl. The drawing's abstract, generic elements contrast with the distinct winking eye. The graphical conventions of lines and shapes represent the eyes and mouth. The simplicity of the drawing does not draw attention away from the winking.

A photo might distract from the focused message compared to Sutherland's representation. In the photo, the other aspects of the image (i.e., the particular person) distract the viewer from this message.

© Ivan Sutherland, CC BY-SA 3.0 and Amina Filkins, Pexels License

Information and Data Visualization

Designers and other stakeholders use data and information visualization across many industries.

Data visualization uses charts and graphs to show raw data in a graphic form. Information visualization goes further, including more context and complex data sets. Information visualization often uses interactive elements to share a deeper understanding.

For example, most computerized devices have a battery level indicator. This is a type of data visualization. IV takes this further by allowing you to click on the battery indicator for further insights. These insights may include the apps that use the most battery and the last time you charged your device.

macOS displays a battery icon in the menu bar that visualizes your device’s battery level. This is an example of data visualization. Meanwhile, macOS’s settings tell you battery level over time, screen-on-usage and when you last charged your device. These insights are actionable; users may notice their battery drains at a specific time. This is an example of information visualization.

© Low Battery by Jemis Mali, CC BY-NC-ND 4.0, and Apple, Fair use

Information visualization is not exclusive to numeric data. It encompasses representations like diagrams and maps. For example, Google Maps collates various types of data and information into one interface:

Data Representation: Google Maps transforms complex geographical data into an easily understandable and navigable visual map.

Interactivity: Users can interactively customize views that show traffic, satellite imagery and more in real-time.

Layered Information: Google Maps layers multiple data types (e.g., traffic, weather) over geographical maps for comprehensive visualization.

User-Centered Design : The interface is intuitive and user-friendly, with symbols and colors for straightforward data interpretation.

The volume of data contained in one screenshot of Google Maps is massive. However, this information is presented clearly to the user. Google Maps highlights different terrains with colors and local places and businesses with icons and colors. The panel on the left lists the selected location’s profile, which includes an image, rating and contact information.

© Google, Fair use

Symbolic Correspondence

Symbolic correspondence uses universally recognized symbols and signs to convey specific meanings . This method employs widely recognized visual cues for immediate understanding. Symbolic correspondence removes the need for textual explanation.

For instance, a magnifying glass icon in UI design signifies the search function. Similarly, in environmental design, symbols for restrooms, parking and amenities guide visitors effectively.

The Interaction Design Foundation (IxDF) website uses the universal magnifying glass symbol to signify the search function. Similarly, the play icon draws attention to a link to watch a video.

How Designers Create Visual Representations

Visual language.

Designers use elements like color , shape and texture to create a communicative visual experience. Designers use these 8 principles:

Size – Larger elements tend to capture users' attention readily.

Color – Users are typically drawn to bright colors over muted shades.

Contrast – Colors with stark contrasts catch the eye more effectively.

Alignment – Unaligned elements are more noticeable than those aligned ones.

Repetition – Similar styles repeated imply a relationship in content.

Proximity – Elements placed near each other appear to be connected.

Whitespace – Elements surrounded by ample space attract the eye.

Texture and Style – Users often notice richer textures before flat designs.

The 8 visual design principles.

In web design , visual hierarchy uses color and repetition to direct the user's attention. Color choice is crucial as it creates contrast between different elements. Repetition helps to organize the design—it uses recurring elements to establish consistency and familiarity.

In this video, Alan Dix, Professor and Expert in Human-Computer Interaction, explains how visual alignment affects how we read and absorb information:

Correspondence Techniques

Designers use correspondence techniques to align visual elements with their conceptual meanings. These techniques include color coding, spatial arrangement and specific imagery. In information visualization, different colors can represent various data sets. This correspondence aids users in quickly identifying trends and relationships .

Color coding enables the stakeholder to see the relationship and trend between the two pie charts easily.

In user interface design, correspondence techniques link elements with meaning. An example is color-coding notifications to state their nature. For instance, red for warnings and green for confirmation. These techniques are informative and intuitive and enhance the user experience.

The IxDF website uses blue for call-to-actions (CTAs) and red for warnings. These colors inform the user of the nature of the action of buttons and other interactive elements.

Perception and Interpretation

If visual language is how designers create representations, then visual perception and interpretation are how users receive those representations. Consider a painting—the viewer’s eyes take in colors, shapes and lines, and the brain perceives these visual elements as a painting.

In this video, Alan Dix explains how the interplay of sensation, perception and culture is crucial to understanding visual experiences in design:

Copyright holder: Michael Murphy _ Appearance time: 07:19 - 07:37 _ Link: https://www.youtube.com/watch?v=C67JuZnBBDc

Visual perception principles are essential for creating compelling, engaging visual representations. For example, Gestalt principles explain how we perceive visual information. These rules describe how we group similar items, spot patterns and simplify complex images. Designers apply Gestalt principles to arrange content on websites and other interfaces. This application creates visually appealing and easily understood designs.

In this video, design expert and teacher Mia Cinelli discusses the significance of Gestalt principles in visual design . She introduces fundamental principles, like figure/ground relationships, similarity and proximity.

Interpretation

Everyone's experiences, culture and physical abilities dictate how they interpret visual representations. For this reason, designers carefully consider how users interpret their visual representations. They employ user research and testing to ensure their designs are attractive and functional.

Leonardo da Vinci's "Mona Lisa", is one of the most famous paintings in the world. The piece is renowned for its subject's enigmatic expression. Some interpret her smile as content and serene, while others see it as sad or mischievous. Not everyone interprets this visual representation in the same way.

Color is an excellent example of how one person, compared to another, may interpret a visual element. Take the color red:

In Chinese culture, red symbolizes luck, while in some parts of Africa, it can mean death or illness.

A personal experience may mean a user has a negative or positive connotation with red.

People with protanopia and deuteranopia color blindness cannot distinguish between red and green.

In this video, Joann and Arielle Eckstut, leading color consultants and authors, explain how many factors influence how we perceive and interpret color:

Learn More about Visual Representation

Read Alan Blackwell’s chapter on visual representation from The Encyclopedia of Human-Computer Interaction.

Learn about the F-Shaped Pattern For Reading Web Content from Jakob Nielsen.

Read Smashing Magazine’s article, Visual Design Language: The Building Blocks Of Design .

Take the IxDF’s course, Perception and Memory in HCI and UX .

Questions related to Visual Representation

Some highly cited research on visual representation and related topics includes:

Roland, P. E., & Gulyás, B. (1994). Visual imagery and visual representation. Trends in Neurosciences, 17(7), 281-287. Roland and Gulyás' study explores how the brain creates visual imagination. They look at whether imagining things like objects and scenes uses the same parts of the brain as seeing them does. Their research shows the brain uses certain areas specifically for imagination. These areas are different from the areas used for seeing. This research is essential for understanding how our brain works with vision.

Lurie, N. H., & Mason, C. H. (2007). Visual Representation: Implications for Decision Making. Journal of Marketing, 71(1), 160-177.

This article looks at how visualization tools help in understanding complicated marketing data. It discusses how these tools affect decision-making in marketing. The article gives a detailed method to assess the impact of visuals on the study and combination of vast quantities of marketing data. It explores the benefits and possible biases visuals can bring to marketing choices. These factors make the article an essential resource for researchers and marketing experts. The article suggests using visual tools and detailed analysis together for the best results.

Lohse, G. L., Biolsi, K., Walker, N., & Rueter, H. H. (1994, December). A classification of visual representations. Communications of the ACM, 37(12), 36+.

This publication looks at how visuals help communicate and make information easier to understand. It divides these visuals into six types: graphs, tables, maps, diagrams, networks and icons. The article also looks at different ways these visuals share information effectively.

​​If you’d like to cite content from the IxDF website , click the ‘cite this article’ button near the top of your screen.

Some recommended books on visual representation and related topics include:

Chaplin, E. (1994). Sociology and Visual Representation (1st ed.) . Routledge.

Chaplin's book describes how visual art analysis has changed from ancient times to today. It shows how photography, post-modernism and feminism have changed how we see art. The book combines words and images in its analysis and looks into real-life social sciences studies.

Mitchell, W. J. T. (1994). Picture Theory. The University of Chicago Press.

Mitchell's book explores the important role and meaning of pictures in the late twentieth century. It discusses the change from focusing on language to focusing on images in cultural studies. The book deeply examines the interaction between images and text in different cultural forms like literature, art and media. This detailed study of how we see and read visual representations has become an essential reference for scholars and professionals.

Koffka, K. (1935). Principles of Gestalt Psychology. Harcourt, Brace & World.

"Principles of Gestalt Psychology" by Koffka, released in 1935, is a critical book in its field. It's known as a foundational work in Gestalt psychology, laying out the basic ideas of the theory and how they apply to how we see and think. Koffka's thorough study of Gestalt psychology's principles has profoundly influenced how we understand human perception. This book has been a significant reference in later research and writings.

A visual representation, like an infographic or chart, uses visual elements to show information or data. These types of visuals make complicated information easier to understand and more user-friendly.

Designers harness visual representations in design and communication. Infographics and charts, for instance, distill data for easier audience comprehension and retention.

For an introduction to designing basic information visualizations, take our course, Information Visualization .

Text is a crucial design and communication element, transforming language visually. Designers use font style, size, color and layout to convey emotions and messages effectively.

Designers utilize text for both literal communication and aesthetic enhancement. Their typography choices significantly impact design aesthetics, user experience and readability.

Designers should always consider text's visual impact in their designs. This consideration includes font choice, placement, color and interaction with other design elements.

In this video, design expert and teacher Mia Cinelli teaches how Gestalt principles apply to typography:

Designers use visual elements in projects to convey information, ideas, and messages. Designers use images, colors, shapes and typography for impactful designs.

In UI/UX design, visual representation is vital. Icons, buttons and colors provide contrast for intuitive, user-friendly website and app interfaces.

Graphic design leverages visual representation to create attention-grabbing marketing materials. Careful color, imagery and layout choices create an emotional connection.

Product design relies on visual representation for prototyping and idea presentation. Designers and stakeholders use visual representations to envision functional, aesthetically pleasing products.

Our brains process visuals 60,000 times faster than text. This fact highlights the crucial role of visual representation in design.

Our course, Visual Design: The Ultimate Guide , teaches you how to use visual design elements and principles in your work effectively.

Visual representation, crucial in UX, facilitates interaction, comprehension and emotion. It combines elements like images and typography for better interfaces.

Effective visuals guide users, highlight features and improve navigation. Icons and color schemes communicate functions and set interaction tones.

UX design research shows visual elements significantly impact emotions. 90% of brain-transmitted information is visual.

To create functional, accessible visuals, designers use color contrast and consistent iconography. These elements improve readability and inclusivity.

An excellent example of visual representation in UX is Apple's iOS interface. iOS combines a clean, minimalist design with intuitive navigation. As a result, the operating system is both visually appealing and user-friendly.

Michal Malewicz, Creative Director and CEO at Hype4, explains why visual skills are important in design:

Learn more about UI design from Michal in our Master Class, Beyond Interfaces: The UI Design Skills You Need to Know .

The fundamental principles of effective visual representation are:

Clarity : Designers convey messages clearly, avoiding clutter.

Simplicity : Embrace simple designs for ease and recall.

Emphasis : Designers highlight key elements distinctively.

Balance : Balance ensures design stability and structure.

Alignment : Designers enhance coherence through alignment.

Contrast : Use contrast for dynamic, distinct designs.

Repetition : Repeating elements unify and guide designs.

Designers practice these principles in their projects. They also analyze successful designs and seek feedback to improve their skills.

Read our topic description of Gestalt principles to learn more about creating effective visual designs. The Gestalt principles explain how humans group elements, recognize patterns and simplify object perception.

Color theory is vital in design, helping designers craft visually appealing and compelling works. Designers understand color interactions, psychological impacts and symbolism. These elements help designers enhance communication and guide attention.

Designers use complementary , analogous and triadic colors for contrast, harmony and balance. Understanding color temperature also plays a crucial role in design perception.

Color symbolism is crucial, as different colors can represent specific emotions and messages. For instance, blue can symbolize trust and calmness, while red can indicate energy and urgency.

Cultural variations significantly influence color perception and symbolism. Designers consider these differences to ensure their designs resonate with diverse audiences.

For actionable insights, designers should:

Experiment with color schemes for effective messaging. 

Assess colors' psychological impact on the audience. 

Use color contrast to highlight critical elements. 

Ensure color choices are accessible to all.

In this video, Joann and Arielle Eckstut, leading color consultants and authors, give their six tips for choosing color:

Learn more about color from Joann and Arielle in our Master Class, How To Use Color Theory To Enhance Your Designs .

Typography and font choice are crucial in design, impacting readability and mood. Designers utilize them for effective communication and expression.

Designers' perception of information varies with font type. Serif fonts can imply formality, while sans-serifs can give a more modern look.

Typography choices by designers influence readability and user experience. Well-spaced, distinct fonts enhance readability, whereas decorative fonts may hinder it.

Designers use typography to evoke emotions and set a design's tone. Choices in font size, style and color affect the emotional impact and message clarity.

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Designers create a balance between simplicity and complexity in their work. They focus on the main messages and highlight important parts. Designers use the principles of visual hierarchy, like size, color and spacing. They also use empty space to make their designs clear and understandable.

The Gestalt law of Prägnanz suggests people naturally simplify complex images. This principle aids in making even intricate information accessible and engaging.

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Michal Malewicz explains how to master hierarchy in UI design using the Gestalt rule of proximity:

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How does all of this fit with interaction design and user experience? The simple answer is that most of our understanding of human experience comes from our own experiences and just being ourselves. That might extend to people like us, but it gives us no real grasp of the whole range of human experience and abilities. By considering more closely how humans perceive and interact with our world, we can gain real insights into what designs will work for a broader audience: those younger or older than us, more or less capable, more or less skilled and so on.

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All open-source articles on Visual Representation

Data visualization for human perception.

The Key Elements & Principles of Visual Design

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Guidelines for Good Visual Information Representations

• 5 years ago

Philosophy of Interaction

Information visualization – an introduction to multivariate analysis.

• 8 years ago

Aesthetic Computing

How to represent linear data visually for information visualization.

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Top 20 project management charts to visualize project progress

A project management chart is a visual representation of the tasks and objectives involved in a project or process. From Gantt charts to bar charts, view the top 20 project management charts and find out how they can help you become a better project manager.

Whether or not it’s your job title, being a project manager means finding ways to execute work more effectively. For teams engaged in sprints or iterative work, visualizing tasks can help streamline communication and create transparency. 

We’ve put together 20 of the most effective project management charts in each of these categories and outlined how each can benefit your team and help you achieve your goals.

Types of project management charts

Project management involves a range of tasks and responsibilities, and as such, it makes use of various chart types to meet different requirements and stages within a project. Each type of project management chart provides a distinct perspective to assist project managers in visualizing, planning, and communicating key aspects of their projects.

Choosing the best project management chart depends on factors like the project's scope, the stage of its lifecycle, and the kind of information that needs to be communicated to stakeholders. 

Top project management charts for planning and resourcing

A common misconception in project management is that project charts are only useful for reporting. In fact, some of the most valuable project charts are those that help you plan projects and set your team up for success. 

Using project charts for planning and resourcing can benefit your team at all levels: 

Individual contributors have a clear way to visualize upcoming work.

Team leads can ensure they have enough resources to hit their goals on time.

Stakeholders get a bird’s-eye view of the work to come, which increases engagement and buy-in.

Take a look at the top nine project management charts for planning and resourcing: 

1. Gantt charts

A Gantt chart is a horizontal bar chart used to illustrate a project’s schedule by visualizing tasks over time. In this chart, each bar represents a task or initiative, and the length of the bar determines how long the task or initiative should take. Use Gantt charts to visualize the timeline, tasks, and goals within a given project. 

While not the original inventor, Gantt charts became popular thanks to Henry Gantt in the 1910s. Gantt charts have come a long way since their original use of logging factory hours. Today, they’re used to track real-time project progress, visualize task dependencies , and represent important milestones. 

Best for: Teams looking to visually map out their project plan so they can coordinate dependent tasks and hit their deadlines on time. Gantt charts are helpful for planning and scheduling projects from start to finish. 

2. Work breakdown structures (WBS)

A work breakdown structure is a method used to visually break down project activities into smaller units. You and your project team can use a WBS to visualize required deliverables and dependencies while streamlining communication.

There are three levels within a WBS, which include parent tasks, dependencies, and sub-dependencies. These levels break tasks down into their most simple form, showing the work required to complete the parent task.

Best for: Teams working on complex projects looking to break down tasks into small pieces. A WBS helps to visualize dependencies, connecting tasks to larger goals. 

3. Critical path method (CPM)

A critical path is the longest sequence of activities your team needs to finish on time in order for the entire project to be complete. The critical path method is a technique used to identify the amount of time each of these activities requires. 

Since delays in critical tasks can affect the entire project, the critical path method helps to facilitate better resource allocation and avoid bottlenecks. Once you identify the critical path, you can schedule tasks with enough time to ensure your team can complete the project deliverables on schedule. 

Best for: Teams looking to complete a project in the most efficient timeline possible. This type of project management chart helps to schedule deliverables and project due dates. 

4. PERT charts

PERT stands for P rogram E valuation and R eview T echnique. A PERT chart is a tool used to schedule, organize, and map out tasks within a project. You can use it to gain a visual representation of a project's timeline that breaks down individual tasks.

The purpose of a PERT chart is to better understand how to connect tasks to one another, giving a clear visual of dependencies. You can use this project management chart chart to evaluate required resources and estimate task duration and team allocation.

Best for: Teams working on a project with complex sub-dependencies. A PERT chart helps to accurately allocate resources, keeping deadlines on track. 

5. Flowcharts

A flowchart is a diagram that illustrates the steps, sequences, and decisions of a workflow. You can use a flowchart to plan, visualize, and document important steps in a process. 

A flowchart may incorporate different visualization tools such as a PERT chart or swimlane diagram. You can use a flowchart for a variety of purposes, including to simplify complex workflows , organize tasks, and identify bottlenecks. 

Best for: Teams who struggle to solve bottlenecks and keep tasks organized. A flowchart makes it easy to visualize project issues and solutions. 

6. Network diagrams

A network diagram consists of boxes and arrows to depict tasks and visualize whether they are critical or not. It is one of many resource leveling tools used to adjust project dates and gauge available resources. 

Use a network diagram to map the chronology and schedule of project tasks.  Plan the duration of projects and track progress along the way with the help of a network diagram.  

Best for: Teams who struggle to keep projects on track and prioritize deliverables. A network diagram helps to prioritize critical tasks. 

7. Matrix diagrams

A matrix diagram helps you understand the relationship between data sets, functions, and project elements. You can use this diagram to identify problems, allocate resources, and assess areas of opportunity within a project. 

There are a variety of different matrix diagrams that include L-shaped, Y-shaped, C-shaped, T-shaped, and X-shaped. Analyze the goal and data points of your project to determine the right diagram for your team. 

Best for: Teams who work on data-focused projects and need help connecting tasks to goals. A matrix diagram helps your team understand the relationship between data and goals. 

8. Cause and effect chart

The cause-and-effect chart is instrumental in brainstorming sessions, particularly when teams are identifying and dissecting potential causes of a specific problem or issue within a project. This project management chart helps identify underlying factors that may not be immediately apparent.

Cause-and-effect charts let team members conduct in-depth analysis by visually highlighting project variables and their potential effects. It's a useful tool for breaking complicated problems into more manageable parts, enabling a rigorous look at each component and how it affects the project.

Best for: Teams that are dealing with complex problems, especially in projects where pinpointing and understanding root causes are critical for successful resolutions and project success.

9. SWOT analysis chart

A SWOT analysis chart assesses a project's strengths, weaknesses, opportunities, and threats. When working on agile projects , this kind of overview is especially important for coming up with good strategies and smart choices.

This project management chart enables teams to align their objectives with internal strengths and weaknesses, as well as external opportunities and threats. This helps them understand their position in the face of project challenges and changes in the market.

Best for: Agile teams looking to adapt and pivot their strategies in response to changing project dynamics and external factors.

Project management charts best for executing

Once you’ve finished the planning process, use project charts to execute your work. Track exactly who’s doing what, by when, and why. Keep your team on track with a central source of information and update them on any changes in real-time. 

Let’s take a look at the six best project management charts for executing work: 

10. Kanban boards

Kanban boards are a way to visualize work that needs to get done, especially as work moves through stages. You may have seen these created out of sticky notes or on a whiteboard.

Virtual Kanban board tools are more dynamic task management tools—they allow you to track the progress of work across different stages in real time. While these stages vary from team to team, yours may include New, Ready, In Progress, Drafting, Hold, and Complete.

Best for: Teams that embrace continuous improvement and prefer to work in successive stages with clear deliverables. This project management chart helps to visualize the stages within a project. 

11. Pareto charts

The Pareto principle states that roughly 80% of consequences come from 20% of causes. Use a Pareto chart to visualize project tasks based on this 80/20 rule. It’s an excellent data visualization tool that typically combines elements of a bar graph and a line graph, where individual tasks are represented as bars on the vertical axis and their cumulative impact is shown by a line graph on the horizontal axis. 

You can use this type of project management chart to identify priorities and make the best decisions for your team. Consider which tasks will have the biggest impact with just 20% of your team’s time. This will help align smaller tasks with larger goals and give your team clear direction. 

Best for: Teams who have a heavy workload and need help prioritizing projects. A Pareto chart helps to organize tasks by priority, so you can make the biggest impact. 

12. RACI Chart

By outlining roles and duties within project teams, the RACI chart encourages accountability and openness. It categorizes each team member as r esponsible, a ccountable, c onsulted, or i nformed for every task. By outlining job ownership, a RACI chart makes sure each team member is aware of their individual responsibilities and the demands made of them.

This project management chart is particularly effective in minimizing misunderstandings and ensuring that all stakeholders are on the same page throughout the project lifecycle.

Best for: Multi-stakeholder projects benefit from this approach because it simplifies communication, clarifies roles and responsibilities, and lowers the possibility of duplicate or overlooked tasks.

13. Project timelines

A project timeline helps you stay on track so you can hit your deadlines. Map out project progress and connect smaller tasks to larger business goals with a project timeline. 

To get started, create a project timeline by listing out your to-dos, estimating the duration of each initiative, and mapping out dependencies. Once your timeline is in place, share it with all of your project stakeholders so they have real-time insight into your initiatives and deadlines, as well as any changes you make along the way. 

Best for: Teams looking to stay on track with tight deadlines. Project timelines help visualize the work needed to reach goals. 

14. Fishbone diagrams

Use a fishbone (Ishikawa) diagram to represent issues or bottlenecks within a project. This type of diagram is also commonly referred to as a cause-and-effect chart. In a fishbone diagram, the head of the fish represents the issue or bottleneck you’re trying to resolve, while the ribs represent different categories and associated tasks. 

You can use a fishbone diagram to solve solutions for root cause issues with the help of your team members. Examples of issues include a lack of resources and incorrect project data. 

Best for: Teams who struggle to solve project issues in real time. The fishbone diagram helps connect issues to potential solutions so you can identify the next best steps. 

15. Control charts

A control chart is a way to visualize project changes. You can use a control chart to understand how long tasks take to complete compared to the allocated resources. This will give you a true picture of the project’s progress over time. 

To create a control chart, start by determining an upper and lower limit—such as task duration or number of resources—to represent set milestones. Erratic changes that exceed these limits symbolize drastic fluctuations. Once you identify those fluctuations with your control chart, you can quickly address and resolve them.

Best for: Teams that struggle with solving issues that derail or postpone projects. This type of project management chart helps you stay on track with allotted resources.

Project management charts to use for reporting

A critical—but sometimes overlooked—part of project management is reporting on work once it’s finished. It’s amazing if you’re able to hit your project goals, but without a way to report on your work, your team can’t learn from your successes—or mistakes. 

Effective project charts for reporting give you an opportunity to l earn valuable lessons from your project and apply those lessons moving forward. 

To get started, check out these five types of project management charts for reporting:

16. Bar charts

A bar chart is a traditional approach to visualizing project data. The purpose of a bar chart is to measure project variables based on milestones. With its simple format and versatile components, it’s no wonder so many teams use bar charts. 

You can create a bar chart by plotting the variables of your choosing, such as task hours or project cost, on the X and Y axes. This design allows you to quickly digest project data and share it with key stakeholders. 

While you can create bar charts by hand, the best way to generate this type of chart is to create it with a universal reporting tool . When your bar chart is directly connected to your team's work, you can reduce manual work and duplicative tasks and dedicate more time to high-impact initiatives.

Best for: Teams looking for a simple way to visualize project components. A bar chart helps to analyze various project variables against goals 

17. Burndown charts

A burndown chart is a visual representation of the remaining work vs. the time required to complete it. You can use a burndown chart to estimate task duration, analyze issues, and determine your project completion date.

The purpose of this project management chart is to accurately plan for future resources based on data. To create a burndown chart, plot the estimated task duration against the actual time on the chart. This will give you a visualization of the ideal vs. actual work duration. 

Best for: Teams looking to analyze the estimated work time vs. the actual. A burndown chart helps to determine project due dates. 

18. Burn up charts

A burn up chart differs from a burndown chart in that it represents the amount of work left to complete, rather than the duration. In short, it tracks project progress as opposed to time. 

To create a burn up chart, plot the ideal tasks remaining against the actual number of tasks remaining. This will give you a clear understanding of where you need additional resources. You can use both a burndown chart and a burn up chart together to understand the full picture of team efficiency.

Best for: Teams looking to analyze the estimated vs. actual amount of work. A burn up chart helps determine resource allocation . 

19. Pie charts

A pie chart is a traditional design similar to a bar chart, though it differs in visual layout. A pie chart breaks down different components within a project. For example, if you anticipate the research phase to account for 10% of the project and it exceeds 20%, you know where to begin analyzing areas for improvement.

You can use a pie chart to track significant components within a large project to better understand resource allocation and important metrics and insights. 

Best for: Teams looking to understand the breakdown of a given project. This project management chart helps your team visualize multiple components against each other to determine where you’re spending the most time or resources. 

20. Status report chart

The status report chart, also referred to as a dashboard, offers detailed updates and overviews of a project's progress. By integrating timeline views and data visualization, a status report chart provides a clear and concise representation of the project's current status, milestones, and potential challenges.

This comprehensive project management chart serves as a visual summary, showing key aspects such as progress, resource allocation, budget status, and upcoming deadlines. It's a valuable tool for keeping stakeholders informed and engaged while also facilitating data-driven decision-making processes.

Best for: projects that need to keep stakeholders up to date on progress and status on a regular basis.

Advantages of project management charts

Effective project management is key to the success of any venture, large or small. You can improve the effectiveness and clarity of your project planning and execution by implementing different types of project management charts. These charts are not just tools for organizations; they are vital in steering a project towards success. Here are some of the key advantages:

Visualization of complex data. Project management charts turn complex project data into clear, digestible visual formats. For all team members to comprehend and interact with the information, this simplification is essential. It will enable better decision-making and a successful project conclusion.

Improved project planning: Project management charts are indispensable in creating detailed plans and schedules. They provide a roadmap for the project, highlighting key milestones and deadlines, which are essential for keeping the project on track and ensuring timely completion.

Streamlined team communication: One of the biggest challenges in any project is maintaining clear and consistent communication . Charts offer a visual representation of project status and updates, making it easier for team members to stay informed and aligned.

Tracking and reporting progress: Monitoring the ongoing progress of a project is important. Project management charts provide a visual tool for tracking development against planned objectives and make it easier to report on the project's status to stakeholders and make adjustments as needed.

Visualize your work and increase clarity

A project management chart can help your team better digest project information through simple visuals. This can help you streamline project planning by setting clear expectations up front. 

While there are many different types of project charts to choose from, project management software makes building diagrams easy. From shuffling between projects and tasks to keeping feedback and team communication in one place, a project management tool can help you accomplish your goals. 

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