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

Erica Santiago

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.

Person on laptop researching the types of graphs for data visualization

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

Download Now: Free Excel Graph Generators

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.  

visual representation of growth

Free Excel Graph Templates

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

  • Simple, customizable graph designs.
  • Data visualization tips & instructions.
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You're all set!

Click this link to access this resource at any time.

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. 

Marshall graph

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.

Customer bar graph example

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.

Types of graphs — example of a line graph.

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.

Types of graph — example of a bullet graph.

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. 

bar chart examples

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.

line chart example

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. 

scatter plot examples

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. 

pie chart

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.

Types of charts — example of a column chart.

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.

Types of charts — example of an area chart.

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.

Types of charts — example of a stacked bar chart.

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.

Types of charts — example of a Mekko chart.

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%.

Types of charts — example of a pie chart.

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.

Types of charts — example of a scatter plot chart.

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.

 Types of charts — example of a bubble chart.

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.

Types of charts — example of a waterfall chart.

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.

 Types of charts — example of a heat map.

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.

Gantt Chart - product creation strategy

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

Types of chart — HubSpot tool for making charts.

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visual representation of growth

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.

year-over-year growth chart

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 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.

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.

Waterfall Chart

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.

comparison bar chart in year over year growth chart

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.

double axis line graph and bar chart in year over year growth chart

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.

dual axis line chart in year over year growth chart

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.

matrix chart in year over year growth chart

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.

pareto bar chart in year over year growth chart

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.

radar chart in year over year growth chart

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.

sentiment trend chart in year over year growth chart

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.

slope chart in year over year growth chart

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.

progress chart in year over year growth chart

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. More so, it uses green and red bars to show the growth and decline of a variable under study.

In the coming section, we’ll address how to create 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, such as 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.

visual representation of growth

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.

insert chartexpo in excel

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

open chartexpo in excel

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

list of charts in excel

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

search progress chart in excel

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

create chart in excel

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

edit chart in excel

  • 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 .

edit legend properties in excel

  • 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?

visual representation of growth

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.

Simple to track and calculate

Most businesses use spreadsheets apps, such as Microsoft Excel and Google Sheets. So generating year-over-year growth charts in these apps is possible.

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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  • Open access
  • Published: 19 July 2015

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.


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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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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Visualizations That Really Work

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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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Understanding a growth curve, the bottom line.

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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.

visual representation of growth

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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Excel Tutorial: How To Make A Growth Chart In Excel


Excel is a powerful tool for creating visual representations of data, and a growth chart can be a valuable tool for tracking progress over time. In this tutorial, we will discuss how to create a growth chart in Excel to visually represent data and highlight important trends.

Visualizing data is essential for tracking growth and identifying patterns that may not be immediately obvious when looking at raw numbers. With a growth chart, you can see the trajectory of development and make more informed decisions based on the visual representation of data.

Key Takeaways

  • Visualizing data is essential for tracking growth and identifying patterns over time.
  • Organize your data into columns for consistent and clear input when creating a growth chart in Excel.
  • Customize your line graph by adding titles, labels, and formatting options to make it visually appealing and easy to read.
  • Adding a trendline to your graph can help you interpret the growth trend and identify periods of rapid or slow growth.
  • Using visual tools like charts in Excel can help you make more informed decisions based on the visual representation of data.

Setting up your data

Before creating a growth chart in Excel, it's important to have your data organized in a clear and consistent format. This will make the process of creating the chart much smoother and easier to understand. Here are some key steps to follow when setting up your data:

  • Organize your data into columns for consistent and clear input

Start by organizing your data into columns within your Excel spreadsheet. Each column should represent a different variable or category, such as time periods and corresponding values.

  • Ensure that your data includes a time series and corresponding values for each time point

It's essential that your data includes a time series, such as monthly or yearly intervals, and corresponding values for each time point. This will form the basis for your growth chart and allow you to visually track changes over time.

Creating a line graph

Line graphs are a powerful tool to visualize trends and patterns in your data. Follow these simple steps to create a professional-looking growth chart in Excel.

  • Open your Excel spreadsheet and navigate to the sheet containing your data.
  • Click and drag to select the range of cells that you want to include in your line graph.
  • Once your data is selected, go to the "Insert" tab at the top of the Excel window.
  • Click on the "Charts" group and select "Line" from the dropdown menu of Chart options.
  • After you have inserted the line graph, you can customize it to better represent your data.
  • Add a title to your graph by clicking on the "Chart Title" option under the "Chart Tools" menu.
  • Label your axes by clicking on the "Axis Titles" option and selecting "Primary Horizontal" and "Primary Vertical" Axis Title.
  • Format your line graph by changing the colors, styles, and markers of the lines using the "Format" tab under "Chart Tools".

Adding a trendline

To better visualize the growth trend in your data, you can add a trendline to your chart. Here's how you can do it:

  • A. Select your line graph and go to the "Design" tab
  • B. Click on "Add Chart Element" and select "Trendline"
  • C. Choose the type of trendline that best fits your data (linear, exponential, etc.)

Formatting the chart

Once you have created your growth chart in Excel, it's important to format it to ensure that it is clear, visually appealing, and easy to understand. Here are some key steps to formatting your chart:

A. Adjust the axis labels and titles for clarity and precision

  • Double click on the axis labels to edit them and make sure they are clearly labeled with appropriate units.
  • Ensure that the chart title is descriptive and accurately represents the data being displayed.
  • Adjust the font size and style of the axis labels and titles to improve readability.

B. Customize the colors and styles to make your chart visually appealing and easy to read

  • Click on the chart to select it, and then choose from a variety of pre-designed chart styles and colors under the "Chart Styles" option in the Chart Tools menu.
  • Alternatively, you can customize the colors and styles of individual chart elements by right-clicking on them and selecting "Format" to change the fill color, border color, and other visual properties.
  • Experiment with different color schemes and styles to make your chart visually appealing while ensuring that the data is easy to read and interpret.

Analyzing the growth trend

When creating a growth chart in Excel, it's crucial to analyze the growth trend to understand the patterns and make informed decisions based on the data. There are a few key steps to take when analyzing the growth trend:

One of the most effective ways to analyze the growth trend is by using the trendline equation and R-squared value. The trendline equation helps to determine the mathematical formula that best fits the data points, while the R-squared value indicates the strength of the relationship between the independent and dependent variables. A high R-squared value close to 1 indicates a strong correlation, while a low R-squared value close to 0 indicates a weak correlation.

By comparing the growth rate at different time points, you can identify periods of rapid or slow growth. This can provide valuable insights into the underlying factors influencing the growth trend. For example, if there is a sudden spike in the growth rate at a specific time point, it may indicate a successful marketing campaign or a new product launch. On the other hand, a gradual decline in the growth rate may signal market saturation or changing consumer preferences.

Creating a growth chart in Excel is a simple and effective way to visually represent your data. First, organize your data into columns and rows. Then, select the data and insert a line chart. Customize the chart to fit your needs and add titles and labels. Finally, analyze the chart to track growth trends over time.

Using visual tools like charts is essential for tracking and analyzing growth data. It allows for quick and easy interpretation of complex data, making it easier to spot trends and make informed decisions. So, next time you need to track growth data, consider creating a growth chart in Excel to gain valuable insights.

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17 Data Visualization Techniques All Professionals Should Know

Data Visualizations on a Page

  • 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 Chart Example

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

Bar Chart Example

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

Histogram Example

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 Chart Example

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

Heat Map Example

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

Box and Whisker Plot Example

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

Waterfall Chart Example

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

Area Chart Example

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

Scatter Plot Example

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 Example

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

Timeline Example

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

Highlight Table Example

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

Bullet Graph Example

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

Choropleth Map Example

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

Word Cloud Example

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 Diagram Example

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

Correlation Matrix Example

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

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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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30 Examples Of Financial Graphs And Charts You Can Use For Your Business

Financial graphs and charts blog by datapine

Table of Contents

1) What Are Financial Graphs?

2) Why You Need Financial Analysis Graphs?

3) The Role Of Financial Data Visualizations

4) Financial Business Graph Examples

5) Best Types Of Financial Graphs

6) Financial Graphs Best Practices

The financial health, flow, and fluidity of your business will ultimately dictate its long-term success, which is why monitoring your money matters carefully, comprehensively, and accurately is absolutely essential.

In our data-driven digital age, 'business intelligent' organizations with the ability to collate, organize, and leverage the insights that are most valuable to their ongoing commercial goals are the ones that are destined to thrive in the long term. Online data visualization takes precedence in business operations, creating more efficient and faster workspaces.

That said, in a time wherein less than two years, around 1.7 megabytes of new information will be generated per second for every single person on the planet, businesses looking to keep their financial affairs fluid need access to KPI dashboards equipped with graphs and charts that are digestible, accurate, and deliver the level of insight required to increase efficiency and stop potential pitfalls before they occur.

In this article, we will present the basic definition of financial graphs, explain why you need them, and answer the most basic of questions: what graphs to include in financial analysis? By presenting data graphically, you will not only make the most out of your monetary information, but simple visuals will do half of the explaining for you. That said, let's get started.

What Are Financial Graphs?

Financial graphs and charts depicted on a dashboard

**click to enlarge**

Financial graphs and charts are visual tools that allow companies to monitor various performance metrics in areas such as liquidity, budgets, expenses, cash flow, and others.  By doing so, they can successfully manage risks to ensure healthy finances and steady growth.

To ensure the best possible performance for a company, conducting regular financial analytics and ensuring the highest quality of data management must be the top priorities of companies, no matter the size. If the finance department raises an alarm, everyone must carefully listen because it concerns the most crucial information and can lead to serious damages if ignored. That's why financial charts must be created with the utmost care and attention. Let's see this in more detail.

Why Do You Need Financial Analysis Graphs?

As humans, we respond to and process visual data better than anything else. That said, when it comes to digesting and taking action upon vital financial metrics and insights, well-designed finance graphs and charts offer the best solution. According to Illinois State University, when it comes to visual aids of this kind, three standards apply: graphs and charts should display unambiguous information, meaningful data, and presently said insights in the most efficient way possible.

Fundamentally, you need them because:

  • You will be able to track your liquidity, cash flow, budgets, and expenses accurately with ease, visually, and automate processes that were oftentimes done manually and with higher risks of errors.
  • By setting the right financial KPIs for your organization, you can set valuable goals that result in growth and success. While there are numerous charts out there, we will explain the invaluable ones for any company.
  • You will be able to make sense of all the financial information and metrics as they will be split into actionable categories and presented intuitively and scannable, no matter the metric you need to include and analyze.
  • Pen and paper or static data will no longer cut it in today’s fast-paced, competitive commercial landscape. As mentioned, manual work is prone to mistakes you can easily avoid by using self-service analytics software .

“Every second of every day, our senses bring in way too much data than we can possibly process in our brains.” – Peter Diamandis , Chairman/CEO, X-Prize Foundation .

Based on this quote alone, it’s clear that by leveraging the power of robust charts that deliver accurate, reliable, and clear-cut financial insights, busy fiscal departments will be able to make sense of the insights before them, resulting in success and evolution, rather than getting bogged down with droves of meaningless and convoluted data.

You can start by creating a simple income vs. expenses graph, add additional charts relevant to your organization's story and finally create a dashboard that will present all your information on a single screen. Let's see this in more detail.

Your Chance: Want to create interactive financial charts and graphs? Explore our 14 day free trial & benefit from great finance management!

Which Role Does Financial Data Visualization Play?

Financial data visualization example of a performance dashboard

Financial data visualizations such as interactive dashboards are complete with charts and graphs that assist in the tracking of all of your core KPIs on one navigable platform. For optimizing reports and detailed analysis, you can check our blog article about financial report examples.

These dashboards give time-stretched finance departments the power to remain on top of the economic performance of the business, resulting in more efficient cash management, accurate expense tracking, comprehensive insights on sales, and additional visual insights geared toward reaching valuable financial goals .

A financial dashboard offers all of the metrics and insights needed to ensure the success of your overall performance, cash flow, cash management, and profit and loss, among others. The business dashboard above not only makes extracting key data swift but is developed in a way that makes communicating your findings to important stakeholders within the business far more simple. And in contrast to a traditional Excel chart, these ones serve real-time data that will prove invaluable to the financial future of your company.

Not only will your company have the opportunity to explore, monitor, and access real-time data, but the interactivity levels are an invaluable resource for managing enormous amounts of information, especially in the financial sector where a small mistake can lead to millions of damages. That's why interaction with the finance charts and graphs is of utmost importance: a single KPI can be viewed in numerous useful ways and angles that static presentations could never offer.

Finally, we cannot avoid mentioning collaboration as one of the top roles of modern financial data visualization tools. As we said before, finances are arguably the most important aspect of any business. If something is wrong with them, most likely, the entire company will suffer. By using BI dashboard tools such as datapine, you will be able to share your financial insights live with the rest of the departments in your company and enhance a collaborative, data-driven work methodology that will optimize your business performance as a whole.

Graph use in financial reports is already a business standard in today's environment. When you add up intelligent tools, automation, stunning visuals, and interactivity for your data visualization process, your finance department will significantly increase productivity and decrease costs. Let's see this through our top 30 financial chart templates.

See Our 30 Financial Business Graph Examples

To put the importance of a dashboard-based financial business graph into perspective, here are 30 templates that cover the most critical money-centric aspects of the ambitious modern business.

1. Gross Profit Margin

Financial chart example: gross profit margin expressed in euros and percentage on a gauge chart

As a key component of our profit & loss dashboard , this indicator has been developed in the form of a traditional pie-style chart but with a more navigable design. The gross profit chart showcases your overall revenue minus the cost of goods sold, divided by your total sales revenue.

Offering a visual representation of your gross profit as well as clearly defined metrics, this chart will allow you to measure your organization’s production efficiency and ultimately enable you to enjoy a greater level of income from each dollar of your sales.

2. Operating Profit Margin

A CFO metric example showing the operating profit margin and its development over time

As another profit and loss-centric financial charting example, this visual is split into an easy-to-digest percentage gauge in addition to a detailed bar chart and will enable you to accurately calculate your Earnings Before Interest and Tax (EBIT).

The higher your operating income, the more profitable your business will potentially be, and this chart will help this metric from dipping through a mix of historical data and priceless real-time insights.

3. Operating Expense Ratio

A financial graph of the operating expenses ratio showing the value of 40%

The operating expense ratio (OER) is also strongly related to the profit and loss area of your finance department's key activities, and this color-coded health gauge will allow you to access the information you need, even at a quick glance.

The OER will give you the power to understand the operational efficiency of your business by comparing your operating expenses to your overall revenue. This is the best visual to show profit and loss, but you do need to connect it with other charts to create a proper financial data story. By monitoring this information regularly, you will be able to decide whether your venture is scalable and make necessary changes to your commercial strategy if you feel it isn't.

4. Current Ratio

Current ratio financial graph closely tied to the management dashboard

Closely tied to the cash management dashboard , this financial graph example is essentially a liquidity ratio that will give you the ability to understand how equipped the business is to pay your most critical obligations in the short term, often within a 6 or 12-month period.

Presented in the form of two visual ratio calculations for swift access to your overall liquidity health or performance as well as a column chart to help you compare data and spot trends, this chart will ensure that you will be able to meet obligations, commit to payments, and quash detrimental roadblocks before they unfold.

5. Net Profit Margin

Financial graph explaining net profit margin

Presented in a similar format to the operational expenses graph, this particular profit graph makes it easy for busy teams to obtain and analyze the information they need to delve deeper into the health of your bottom line, as a result gaining the level of insight required to boost your overall net profits.

As one of the most vital financial KPIs a business can track, this graph is invaluable - and by using this robust, reliable, and intuitive chart, you will be able to iron out any inefficiencies and boost your company’s net profit over time.

6. Accounts Payable Turnover Ratio

Accounts payable turnover ratio financial graph

Regarding the smooth and responsible handling of your company's cash management activities, the accounts payable turnover is another liquidity calculation that will ensure that you are able to pay all of your important expenses within the required deadlines or set timeframes.

The ratio itself changes according to real-time shifts and is displayed in a bold numbered format, while historical or chronological information is presented in the form of a column graph that showcases turnover percentages split into different periods of time. A higher ratio gives suppliers and creditors the assurance that your business pays its bills frequently and is a pivotal metric when negotiating a credit line with a supplier, so it's a chart your company cannot afford to live without.

7. Accounts Receivable Turnover Ratio

Financial chart essential for accounts receivable turnover

Presented as a scannable pie chart, accompanied by vital turnover metrics, this is one of the financial graphs templates that quantifies how swiftly your organization collects your payments owed, thus showcasing your effectiveness in extending credits.

The quicker your business can transform credit sales into cash, the better your liquidity, ultimately translating to a greater ability to handle your short-term liabilities.

8. Return On Assets (ROA)

Return on assets business graph example

This particular example is incredibly useful as it's a financial performance graph that will allow you to understand how well your business can leverage its assets to gain more profit.

Displayed in an easy-to-follow column chart and trend line format, this graph offers an exceptional visual representation of how profitable your organization is concerning your overall asset. The bottom line here is the higher your ROA, the better, particularly when you compare this metric to your direct industry competitors - so this chart is essential to your ongoing financial progress.

9. Return On Equity (ROE)

Financial graph return on equity example

This color-keyed visual offers a distinct measurement of the level of profit you are able to generate for your various shareholders. This particular metric is calculated by dividing your business’s net income (minus the dividends to preferred stocks) by the equity of your shareholders (excluding preferred shares) - not only does this provide an excellent gauge of financial performance, but it’s also effective for comparison with other competitors within your sector.

The better your Return on Equity, the more value you are offering to your shareholders, which will translate to tangible long-term commercial success.

10. Gross Margin Return On Investement (GMROI)

Financial graph example on retail displaying the gross margin return on investment (GMROI)

A great retail KPI is the gross margin return on investment (GMROI). It is an inventory profitability indicator, and it measures the ability of an organization to turn its inventory into cash (after subtracting the inventory costs). The GMROI is calculated by dividing the gross profit by the average inventory costs. The result will tell you how much money you made from the inventory you invested in. An industry standard for this metric is a ratio higher than 1. However, experts recommend that a successful retail store should have a GMROI of around 3. This means the company is making money from its investment. On the contrary, a ratio below 1 means something needs to be done to improve profitability. 

A good practice when it comes to measuring the GMROI is to do it by product category. This way, you can understand which products return more and focus your efforts on those. 

11. IT Cost Break Down

IT costs break down is one of the financial graphs that focuses on the IT department

This financial graph template focuses especially on the IT department, but you can easily adjust it for any other function in a company. We can see how the allocation of costs behaves in designated units (software, hardware, SP, and personnel) while depicting the cost percentage of each of their elements (for instance, administration, development, operations, and support). It's crucial to monitor the expenses graph to identify the main cost drivers on the one hand and possibilities on the other so that the company can adjust its strategies.

If you see that one unit spends significant amounts of resources, it would make sense to investigate further and check if the costs are justified or need more attention. By using relevant online business intelligence software , you can directly interact with all of the values presented in this visual and dig deeper as much as you need. Not only will you cut time into exporting, importing, scrolling, and searching for the right information, but your comprehension will be much quicker since humans are visual creatures, as stated earlier.

12. Cost Avoidance

This financial graph example shows how much costs were saved in a procurement department by the supplier category

Our list of financial data visualization examples wouldn't be complete without cost avoidance. This is one of the graphs that are important to take care of since it tracks how much costs, in this case, of a procurement department, have been saved in a specific time frame. You can also depict a 5-year trend like in our template above and organize it by supplier category. This metric is not as tangible as direct cost savings, for example, but it does bring value to the whole procurement department.

The goal of every procurement professional is to reduce costs in the future (as well as the present), and this chart can easily depict how much these efforts have brought in a company and had a direct impact on the savings processes. For instance, a procurement professional or manager can lock the price of a contract with a vendor to avoid a future price increase. To see more details on procurement operations and management, you can explore our set of procurement metrics .

13. Cash Conversion Cycle

A financial graph depicting the cash conversion cycle in a specific time frame

The cash conversion cycle (CCC) is a metric that helps companies in tracking how much time a company needs to convert their resources into cash from sales. In our example, the formula is also simply depicted so that it can easily be followed: you need to add the day's sales outstanding to the days of inventory outstanding and deduct the days payable outstanding to calculate the cash conversion cycle. If you use a finance graph that you can interact with and calculates the data automatically based on your input, the possibility of making a human error is minimized. You don't have to calculate each time you need a report manually, but you can monitor your data in real time with just a few clicks.

In the end, the goal is always to decrease the cycle as much as possible since an increment can mean that the organization is not fully efficient in its management and operations. It's simple: if the company sells what consumers want to buy, the cycle is quick and healthy. If not, additional corrections need to be performed so that the company doesn't fall into even more serious difficulties.

14. Vendor Payment Error Rate

The vendor payment error rate is depicted with line graphs and in percentage during the last 12 months

Paying invoices and issuing them to vendors, suppliers, or other stakeholders is essential to analyze since it can show how many errors are made and if the accounts payable department is healthy. Of course, mistakes do happen, but sometimes they can be dangerous, so they should be kept at a minimum. Errors may include payment to the wrong entity, overpayments, or double invoicing, and each accounts payable manager usually strives to reduce those errors as much as possible.

A proper financial and analytical report can assist in this process. When you automate and digitalize your analytics process with the help of modern software tools, you don't have to worry that your error rate will increase any time soon. In our example above, we can see that our average error rate is 1.3%, but it has started to decrease in the last few months. The goal should be to have the lowest rate possible and avoid any possible business disputes.

15. Operating Cash Flow

The operating cash flow graph is depicted annually by the last 5 years, with an average annual growth

This cash flow graph gives a clear picture of the business operation's performance. The example presented above shows how much cash a company generated over the course of 5 years. It doesn't include investments and/or non-sales-related income, which basically means it focuses on main cash activities (for example, selling/buying inventory or paying salaries). This graph is important to track since it clearly depicts if a company can sustain its operations and eventually grow. It should be monitored closely and regularly to avoid any potential difficulties.

To create such a chart, there are some data visualization techniques that are useful to study and follow. That way, your analysis and presentation of vital information will yield the best possible value and ensure the most profitable results.

16. Fixed Operating Expenses

Financial graph example tracking fixed operating expenses

As its name suggests, the fixed operating expenses KPI tracks all expenses that need to be mandatorily paid in a specific time period and that will not vary depending on the volume of production or sales. These include salaries, rent and utilities, office supplies, marketing, and insurance, just to name a few. While these expenses are very hard to lower as they are not influenced by the production of the company, it is still fundamental to keep a close eye on them to make sure that they don’t go up too much as they account for a big percentage of revenue.

17. Variable Operating Expenses

Financial graph example tracking variable operating expenses

On the other hand, variable operating expenses are all expenses that can vary depending on the production level of the company. We are talking about raw materials, distribution and costs, sales commissions, packaging, and many more, depending on the industry. They are easier to control and manipulate than fixed ones because they follow a simple rule: the more you produce, the higher the variable expenses. The more you sell from what you produced, the less impact from these costs. Companies also use their variable expenses to define pricing, plan their budgeting strategies, and track their profitability (together with fixed expenses), among other things. 

18. Actual vs. Forecast Income

Actual vs. forecast income as a financial graph template

Forecasting is the process of using historical and current data to generate accurate predictions about the future. In finances, forecasting has become an increasingly important practice that enables managers to generate strategies based on realistic scenarios. Our next example is a table displaying the actual vs. forecast income with insights into the actual value, the forecast value, and the absolute difference between the two. Here, we can observe a difference of $-33,237 in the net profit. This can shine a light on some issues that need to be addressed to prevent the business from having profitability problems in the future. However, it is important to note that the difference between the forecasted and the actual value is not necessarily a negative thing. It will depend on the way the business approaches forecasting.

19. Actual vs. Forecast Expenses

Actual vs. forecast expenses as a financial chart example

Following with another forecasting example, we have the actual vs. forecast expenses. This time, displayed in a financial bar chart instead of a table. As we mentioned in previous examples, keeping expenses at a minimum while maintaining profitability is one of the biggest challenges for organizations of all sizes. Here, we can see the actual costs compared to the forecasted value and an absolute difference between the two. Overall, we can say that this business was successful at keeping costs low as their absolute value is on the lower side. That said, there is still room for improvement. For instance, we can see that marketing costs are almost $50.000 higher than the forecast. This is something that is worth exploring in more detail to find the causes and determine if it is a critical issue or not.

20. Working Capital

Working capital depicting details of current assets and current liabilities as one of the financial graph templates for showing short-term financial health

Moving on with our list of financial chart examples, we have the working capital. This is a straightforward graph that gives you a glance overview of the financial health of your company. It doesn't include any ratios or proportions but solely numbers that represent the state of your current liabilities, current assets, and total working capital. If the working capital is high, you might want to consider investing the excess cash, as higher values don't necessarily mean your company is performing well.

21. Income Before Tax 

Income before tax financial graph example

Our next financial chart template shows a summary of an income statement. We have mentioned the value of an income statement and discussed many of the KPIs present in it throughout this post. However, there is one missing that we will focus on right now: the income before tax, also known as EBIT. As its name suggests, the income before tax is a KPI that tracks the amount of income generated by a company before subtracting all tax-related expenses. It is used by managers and investors as a way to analyze the performance of a company’s core operations without considering tax costs, as they can cloud the actual operating values.

22. Berry Ratio

A financial chart example tracking the berry ratio of a business

The Berry Ratio compares the gross profit of a company with its operating expenses to understand the amount of profit from a specific time period. In the chart above, we see that 1,0 is the reference coefficient to measure this metric. If your company’s Berry Ratio is below 1,0, it means that you are losing money. On the other hand, if it’s higher, it means that you are making a profit above all variable expenses.

This business graph is a fundamental part of a CFO dashboard , if you track it regularly, you can understand which exact period your profit dropped or increased and draw conclusions to improve your business finances.

23. Economic Value Added

Economic value added business chart example

This interactive gauge chart aims to track the Economic Added Value (EVA) of a company, the colors red, gray, and green make it easier to understand if the number is positive or negative visually. This metric is obtained by deducting the costs of capital from the operating profit and adjusting it for taxes on a cash basis. In order to calculate your company’s Economic Added Value, you can use a simple formula consisting of: net operating profit after taxes (NOPAT) - invested capital * weighted average cost of capital (WACC).

The EVA is a fundamental financial metric to understand if a company’s investment is returning any value. If a business has a negative EVA, it means that it’s not generating any profit from its investments. By measuring this metric on a regular basis, you’ll have a bigger picture of your company's wealth and make better managerial decisions in the long run.

24. Payroll Headcount Ratio

Financial chart example showing payroll headcount ratio

Next, in our financial data visualization examples, we have the Payroll Headcount Ratio. This metric consists of dividing all the HR full-time positions by the total number of employees based on various aspects such as their associated costs or revenues. You can include full-time and part-time employees as well as freelancers or contractors in the calculation. The overall aim of the Payroll Headcount Ratio is to understand how well your company is managing its workforce costs. 

By tracking HR metrics like the Payroll Headcount Ratio, you can make sure that your labor costs are well invested and bringing positive financial gain to your company, as well as help you understand if your overhead costs for payroll are too high, this way you can take action quickly and avoid any difficulties.

25. Procurement Cost Reduction

Financial graph displaying cost reduction

Cost reduction is an important KPI that you will find in any procurement dashboard . This metric's aim is to track the tangible savings you have made in terms of cost management over the years. The image above displays two charts to understand cost reduction, the first one is a 5-year trend so you can compare your performance with other years, and the second one gives a detailed view of the savings by supplier category; this way, you can learn exactly on what area you saved money.

By currently monitoring your cost reduction, you can streamline your supplier lifecycle management, increase efficiency by leveraging supply chain analytics or train your staff on how to save costs. All of this will certainly increase your numbers in the long term.

26. Cost Per Hire

The Cost per hire measures all the costs involved in the hiring process of one candidate

This straightforward metric aims to track the number of resources you invest in each new employee you need to hire. In the pie chart above, we can see the yearly expenses divided by seniority level: Junior, Mid-level, and Senior. The chart covers all expenses that come from the recruiting process, such as marketing, time cost that the recruiter spends reviewing CVs and conducting interviews, as well as training and cost materials associated with it.

Although it might not seem like it, the recruitment process usually costs businesses a lot of money. By keeping track of this metric, you can optimize investments and extract all the potential out of your talent acquisition budget. In the end, investing in new talents is what will bring more value back to your company.  

27. P/E Ratio

Financial management graph tracking the price earning ratio (P/E)

Moving on with our list of financial graphics, we have the price-earning ratio (P/E). This indicator, displayed in an intuitive area chart, is used to measure the value of a company compared to its competitors. It does this by relating a company’s share price to its earnings per share. It gives potential investors an idea of how much money they would pay for stock shares for each dollar of earnings. The P/E calculations should always consider competitors from the same industry, as the values will considerably vary depending on the nature of each industry.   

28. Quick Ratio/Acid Test

Business graph example tracking the quick ratio

Ensuring liquidity is one of the greatest financial aims of any organization. The quick ratio, or acid test, aims at helping companies understand their liquidity’s health in a short-term period. It measures the ability of a business to turn its near-cash assets (assets that can be turned quickly into cash) to pay down its current liabilities. The higher your quick ratio, the better. Your goal should be to keep it at a minimum of 1,0. This means your business has the capacity to pay all of its current liabilities quickly. 

An important note when it comes to monitoring this metric is to understand that, when comparing it to the current ratio, the acid test will always be smaller due to the fact that it only includes near-cash assets. 

29. Budget Variance

Example of a financial graph displaying the budget variance in a table

Next, we have the budget variance displayed in a table chart. This straightforward metric expresses the difference between budgeted and actual figures in different accounting categories. The values can be favorable or unfavorable and are clearly depicted with the colors red for negative and green for positive. This way, you get a glance notion of what is working and what is not. Negative budget variances can indicate that the company was not able to forecast costs and revenues accurately. However, some negative variances can also happen due to external factors that are outside the control of the organization. This can be changing business conditions, changes in the overall economic environment, or an increase in the costs of raw materials, just to name a few. 

30. MRR Growth 

MRR growth rate being shown as a financial chart example in customer service

To start explaining the MRR growth, we first need to understand what MRR even stands for. The monthly recurring revenue is the income that a business can expect to generate every single month. It is a fundamental metric that serves as a foundation for calculating other relevant indicators, such as the customer lifetime value or the average selling price. Tracking the MRR growth for longer periods of time can tell you how sustainable is your business model and how fast you are growing. 

This metric proves to be specifically useful for companies working with subscription-based models, as predicting recurring revenue is easier for them. Monitoring your MRR growth with a line chart is the most effective way to do it, as it can easily indicate how the values increased or decreased during the observed period. 

Which Chart Type Is Best For Visualizing Your Financial Data?

We couldn't finish this article before mentioning a very important aspect to consider when analyzing or presenting your financial data: charts and graph types. Choosing the right business graph to display your information is just like taking a picture of something and showing it to others. You want it to be understandable and focused on what you need in order to support a discussion. Here we show you some of the most common charts types to visualize your financial insights:

  • Line chart: This type of finance chart is ideal for displaying multiple series of closely related data over a period of time; like this, you can find trends, accelerations, decelerations, or volatility in your data. Its minimalistic design consisting of thin lines makes this type of chart very easy to understand. In order to maintain it like this, you should always keep your axes scales close to your highest data point. This way, you avoid wasting valuable space in the chart. It is also important to consider only displaying the relevant metrics for your analytical process since too many variables can overcrowd the chart and make it hard to decipher. You can use line charts to track financial KPIs such as the return on equity, working capital ratio, or earnings before Interest and Taxes.
  • Number chart: A number chart is one of the most basic types of business graphs, as it is essentially a ticker that gives you an immediate notion of how a specific KPI is performing. You just need to choose the period you want to track and if you want to compare it to a trend or a fixed goal, depending on the aim of your analysis. In finances, you can use it to measure metrics like the total cash balance, your current assets and liabilities, or some sales KPIs like the total revenue. Keeping track of these live numbers will help you catch any anomalies in time.
  • Tables: Tables are a classic way of displaying information, and they can prove to be really useful for working with your raw data. You can use a table to display many precise measures and dimensions, always having the grand total to compare or support it. They can also be useful if several people need to access the data for different reasons, as they can filter it and work only with what they need. It is important to consider that due to its complexity, you should always try to make your tables as visually appealing as possible regarding colors and shapes. You can accomplish this with the help of a dashboard tool . In finances, you can use tables to display your profit and loss statements (P&L) to drive advanced insights into your company's revenues.
  • Gauge chart : The gauge chart is a straightforward and simple type of visualization often used to display the performance of a single metric with a quantitative context. With the help of colors and needles, this type of chart aims to track the progress of a KPI in comparison to a set target or to other time periods. It is important to consider that because gauge charts are most effective for displaying one single metric, it is not the best chart to use if you want to drive actionable insights from your analysis. You can go back to our list of financial graph templates to see the economic value-added and the net profit margin illustrated with colorful gauge charts.
  • Progress chart: As its name suggests, a progress chart aims to track how much percentage of a specific goal you have accomplished and how much you have left to complete it fully. The data can be expressed in circles or bar charts, and you can also add reference numbers to indicate where you should be in a specific time period and compare if you are late or advanced to accomplish your final goal. If you want a more detailed view, you can also break down your progress in different areas and track each of them separately to understand if any step-backs are happening and where. In finances, you can use it to keep track of your budget spending or the development of a big project where your company placed a big investment.
  • Waterfall chart : This type of visualization helps understand the cumulative effect between positive or negative values to reach a final value. For instance, if a company wants to illustrate its yearly profit, the waterfall would display all sources of revenue and then add or deduct all costs to reach the total profit of the year. The additions and subtractions can be both time-based and category-based. In the use case we just mentioned, they are divided by category of revenue and costs. Our example on return on assets at the top uses a monthly division.
  • Area chart : This type of graphic typically combines a line and bar chart to show how one or more numeric values change based on a second variable. The area chart differs from these two others by adding shading between the lines and the baseline. It is typically used to show trends between associated attributes over time. In finances, area charts are usually used to represent stock changes over time, as seen in our P/E ratio example above. 
  • Bar chart : This type uses horizontal bars in a rectangular shape to display categorical data. They are mostly used to compare values based on a specific category, with the categories represented on the y-axis and the values on the x-axis (horizontal). They are used in finances when summarizing large data sets, as the horizontal orientation allows you to fit multiple values and categories without overcrowding the chart. For instance, when you want to visualize revenue by top 15 products.  
  • Column chart : In many places, column and bar charts are considered the same. However, they do serve different purposes depending on the goal and the analytical context. Column charts display categorical data in rectangular columns that have a vertical orientation. This means they can fit fewer values before they get overcrowded. However, this doesn’t mean they are not extremely valuable. For instance, you can use them in finances to analyze your net profit by each quarter of the year. The sizes of the vertical columns can help you spot any under or over-performing quarters at a glance. Plus, they can be mixed with other types of charts, such as line charts, to provide an even deeper look into the data, as we saw in our MRR Growth example. 

 Although these might be considered the best charts for financial analysis, you should always consider what your analytical aim is and what questions you are trying to answer when picking your visualizations. Here we give you a useful overview to help you choose the right type of business chart depending on your goals.

Overview to use the right financial data visualization types for comparisons, compositions, relationships and distributions

Financial Graphs And Charts Best Practices

As you have seen throughout this insightful post and our list of 30 interactive examples, charts have the capacity to turn the most complex data points into understandable values that can significantly enhance the decision-making process and drive business growth. That said, financial data is not easy to deal with. While it might sound easy just to build a chart to display your most important performance indicators, there are still a few best practices you need to follow in order to make your visualizations successful. Here we tell you a few of them. 

  • Think of your goals and audience 

The first step to generating successful finance-related visuals is to think about your audience and goals. This is a best practice that you should apply to any analytics-related task or process, especially when it comes to generating data visualizations for finances, as the language and data being used are complex and critical to the correct functioning of the organization.  

In that sense, there are two things you should consider. For starters, what are the company’s financial goals? Thinking about this question will help you plan your visuals to tell a compelling story that will allow management and any other stakeholder that needs to use those charts to answer questions and inform their most important strategic decisions. You can also think outside of the box and include some graphics that provide context or deeper insights. 

Paired with the general goals, you should also think about your audience. What matters to them, what is their level of knowledge, and what are they expecting to get from these visuals? By putting yourself on the audience shows, you’ll be able to generate visuals that are compelling and engaging. Plus, it will help users that are not very technical with finances to understand the message on each graph easily. We’ll discuss this last point as a separate best practice a bit later in the post. 

  • Avoid unnecessary elements and be smart

The first best practice for financial data presentation is to avoid cluttering your graphs with unnecessary elements. To avoid this, you should first define a clear goal for the visual you are building. This way, you will be able to clearly distinguish which elements are needed and which ones are not. If you are using more than one axes, make sure that each of them provides value to the point you are trying to show. Otherwise, it can lead to a misleading interpretation of the data. 

Another important note here is to be smart about the way you present your insights. For instance, if you use a bar chart to show revenue growth over the past 12 months, it is only natural to order the values by month to see the progression. On the other side, if you are showing revenue growth by the department, it could be a good idea to order them from largest to smallest growth. This allows the audience to understand at a glance the highest and lowest categories. 

  • Keep a consistent visual identity 

Charts and graphs are integral in communicating complex financial information in an intuitive way. That said, when building them, the colors you use can significantly affect how the data is perceived. A carefully selected color palette can help your audience understand the values better, as well as keep them focused during the analysis process. On the contrary, a poor color palette can make the visualization process less effective and harder to understand. 

A few good practices for this is to define specific colors for specific topics. For instance, you can use orange every time you will display revenue-related charts and play with the different shades of the color to show different values of revenue. That way, your audience will automatically understand you are talking about revenue when they see the color orange. Another good practice is to keep the colors consistent with the business's visual identity. This makes them more friendly-looking to the audience as well as more professional in general.

  • Use understandable language

It is very likely that your financial goals will also affect the rest of the departments in your organization. If you want to increase sales in your online channels, then you need to connect with the marketing department to think of initiatives that can help achieve this objective. That same scenario can happen with several other departments. Hence, the need to make financial data understandable for every user level. 

That said, when building your finance statement charts, it is of utmost importance to use friendly language. If you are including acronyms in your axes, make sure you explain what they refer to. The same rule applies to any other type of technical language you include in your representations. You should always keep your audience in mind when building your charts.  

  • Use interactive elements 

Financial data visualizations have been a part of businesses' regular operations for decades now. That said, the practice of generating visuals for the finance department has mutated with the years, shifting from static graphs and charts displayed in a PowerPoint presentation to modern online dashboards containing a mix of interactive graphics that allow users to navigate the data and extract deeper insights.  How, you might be wondering? The answer is interactive capabilities provided by modern data visualization software. 

Financial analytics tools such as datapine provide users with multiple interactivity options to give users the power to bring their data to life and uncover critical insights. Some of these interactivity features include:

  • Drill down : This filter enables you to go into lower levels of hierarchical data all in one chart. For instance, imagine you are looking at revenue per product category and want to look deeper into a specific category. All you need to do is click on that category, and the chart will adapt to show the best-selling products in that category. That way, you’ll be able to find the reasons for certain trends and patterns without going through infinite charts. 
  • Drill through: Similar to drill-downs, drill throughs also provide extra information from a particular chart, but instead of just going into lower levels of data, it shows the extra data in a popup. For instance, say you have a number chart displaying the total revenue of the year. A drill through would enable the user to click on that chart to see a pop-up displaying revenue by department. 
  • Time interval widget: This filter lets you visualize different time periods in specific KPIs. For example, you might be visualizing revenue for the past 5 years and realize that year 3 had a huge spike. You can click on that year for monthly or weekly revenue.

These are just a few of the many interactivity options you can include when generating your financial graphics. If you want to know more about this topic, check out our guide on the top 14 interactive dashboard features. 

6. Tell a cohesive data story 

Expanding on the point above, it is no secret that finance users are acquainted with numbers and formulas, probably more than any other department. That said, in order to achieve a collaborative environment with other relevant business players, the data needs to be displayed in a way that tells a cohesive understandable story. Data visualizations allow non-technical users to identify trends and patterns in the data. However, this is not possible without a correct organization of the different graphs and charts. Modern dashboard software assists you with this task by providing a centralized view of your most important financial indicators. 

The image below is a financial dashboard displaying relevant metrics related to profit and loss. Being able to quickly see how the numbers fluctuated over time and how each indicator affected the other allows users to get a complete picture and make informed decisions.  

Visual of a financial business dashboard example for top-management

7. Gather internal feedback and adapt 

As you’ve learned from this list of best practices, building successful financial data visualizations is a task that requires thoughtful consideration of the design but also of the audience and final use case. That means there’s probably always room for improvement, and you should see that as an opportunity. 

After you generate your graphs and charts and present them to the finance team, you should gather feedback from all users and find improvement opportunities to make the process as efficient and personalized as possible. This may sound like an exaggeration, but the way you choose to chart your financial KPIs is going to set the groundwork for future strategic decisions. Therefore, it should not be taken lightly.

Key Takeaways From Financial Charts & Graphs

We have expounded on what graphs to include in financial analysis and explained in detail each of them. We hope these graphs and charts templates have given you the inspiration you need to optimize your overall financial reporting and analysis . If you would like more data-driven, business-based pearls of wisdom, explore these sales report examples that you can use for daily, weekly, monthly, or annual reporting.

To get a more in-depth knowledge of the financial statement graphs essential for your business, you can test datapine for a 14-day free trial !


Explained visually.

By Victor Powell

We seem primed to understand linear growth, but lots of processes in finance, ecology, physics and other fields depend on exponential growth.

Here's a simple visualization to help conceptualize this type of growth. It starts off {{opts.rateLabel}} with every step. Adjust the rate to see {{opts.otherRateLabels.join(' and ')}}.

Linear growth

Most of us already have an intuition for linear growth. In the example below, each step adds a fixed amount to the total.

Exponential growth

With exponential growth, on the other hand, each step multiplies the total by a fixed amount. So, exponentiation is repeated multiplication in the same way that linear growth is repeated addition.

The following is a naive model of the spread of a virus in a population. The number of infected individuals grows exponentially up until the virus runs out of people to infect.

For more explanations, visit the Explained Visually project homepage.

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Shift mindset & attitude with visualization

Visualization can help you to shift your mindset and bring it into your day. Experience improved motivation and focus, and set yourself for success whilst showing up as your best self.

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Learn how visualization can support your health and fitness journey

Learn how visualization can help to change your mindset and attitude.

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Visualization is a success practice used by people who want to grow and achieve their goals. It is not just daydreaming or wishful thinking. When used correctly, it is a scientific process that allows you to shift your state and tap into more of your inner resources. By state we mean our mental, emotional and physical state. Said another way, it’s our mindset and how we’re showing up to things. When we use effective visualization to shift our state into an empowered one, we can better focus on what’s important, take more dynamic action and more easily overcome challenges. It also allows us to mentally prepare, so that we can show up at our best at the most critical moments.

Visualization can help us with stress in two significant ways. It can help to create a state of deep relaxation after the body is triggered by stress. And it can help us to prevent stress by preventing us from triggering the stress response in the first place. An example of this is swimmer Missy Franklin.

Franklin, who won four gold medals at the London Games, uses visualization as a way of reducing anxiety of the unknown. Says Franklin, “When I get there, I’ve already pictured what’s going to happen a million times so I don’t actually have to think about it.” Like that, visualization can reduce stress and help us relax more. It provides not only a method of creating relaxation after stress has occurred, but it can also help us prevent it. And that makes it an invaluable tool. Visualization is an Effective Way to Create a Positive Mindset and Attitude Objective: Answer the question, “What is visualization?”

When most people think of visualization, they think of a mental practice. Visualization with EnVision goes way beyond that. We recognize that the mind affects the emotions and the body, and all of these are components of setting a winning attitude. In other words, the most effective approach to visualization has a profound effect on how we feel. It changes our attitude and helps us to create a positive mindset. And it is our daily mindset and attitude that propel us rapidly towards our goals. They accelerate our progress and efficiency. They can make all the difference. And visualization can be the key that unlocks the winning mindset and attitude.

Visualization is a versatile tool. We can use it to set attitude and develop a positive mindset. We can use it to rehearse important steps towards our success. One of the most effective uses of visualization is to create a vision for our lives that is so inspiring it compels us to act. At EnVision we call this your compelling future. We help you to define your compelling future and to represent that future visually with a vision board. We help you to know how to best visualize this future so that you feel motivated and inspired. And we help you to know what actions to take in order to achieve it.

What is the value of being able to show up as your best self, each and every day? Visualization is a tool that can be used to shift our internal mental/emotional state. Why? Because when we visualize the subconscious mind reacts as if what we are visualizing is real. That means we can utilize visualization to prime ourselves for the day so that we can show up as the version of ourselves that we want to be. This is the power of visualization. Being able to create our lives in a way that matches our vision. And this is the power of using a visualization app. It can guide you to have the most effective state shift so that you show up as your best self. So why not give it a try?

One of the things that using a visualization app can help with is creating the best habits. This is especially true when it comes to the habit of visualization. While you might be able to find guided visualizations elsewhere, when it comes to practicing visualization for success, EnVision has got everything organized for you. Connecting to your vision, using visualization to set your mindset and attitude for the day, setting powerful intentions and getting key actions done — these are all part of the EnVision daily habit. We help you to create the best future possible.

Success is built one day at a time. Showing up to each day with a positive mindset and attitude makes all the difference in having a productive day moves you closer to your goals. Visualization is a powerful tool for shifting mindset. That is one of the things we focus on with EnVision. Shifting your mindset, your attitude, or what we call “state,” is effectively done with the style of visualization we offer. If you want to create the mindset for success, then check out the EnVision app.

How do you create an unstoppable mindset? With effective visualization. You see, effective visualization goes beyond just picturing what you want. It is about creating that deeply felt emotional state that can inspire you. It is also about rehearsing for the obstacles that you will encounter along your way. When you see yourself overcoming these and coming up with creative solutions to problems, you begin to know that you will succeed. You feel the passion and know that you are unstoppable. And that is part of the benefit of using a visualization app.

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  • Review article
  • Open access
  • Published: 11 July 2018

Decision making with visualizations: a cognitive framework across disciplines

  • Lace M. Padilla   ORCID: orcid.org/0000-0001-9251-5279 1 , 2 ,
  • Sarah H. Creem-Regehr 2 ,
  • Mary Hegarty 3 &
  • Jeanine K. Stefanucci 2  

Cognitive Research: Principles and Implications volume  3 , Article number:  29 ( 2018 ) Cite this article

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Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.


People use visualizations to make large-scale decisions, such as whether to evacuate a town before a hurricane strike, and more personal decisions, such as which medical treatment to undergo. Given their widespread use and social impact, researchers in many domains, including cognitive psychology, information visualization, and medical decision making, study how we make decisions with visualizations. Even though researchers continue to develop a wealth of knowledge on decision making with visualizations, there are obstacles for scientists interested in integrating findings from other domains—including the lack of a cognitive model that accurately describes decision making with visualizations. Research that does not capitalize on all relevant findings progresses slower, lacks generalizability, and may miss novel solutions and insights. Considering the importance and impact of decisions made with visualizations, it is critical that researchers have the resources to utilize cross-domain findings on this topic. This review provides a cognitive model of decision making with visualizations that can be used to synthesize multiple approaches to visualization research. Further, it offers practical recommendations for visualization designers based on the reviewed studies while deepening our understanding of the cognitive processes involved when making decisions with visualizations.


Every day we make numerous decisions with the aid of visualizations , including selecting a driving route, deciding whether to undergo a medical treatment, and comparing figures in a research paper. Visualizations are external visual representations that are systematically related to the information that they represent (Bertin, 1983 ; Stenning & Oberlander, 1995 ). The information represented might be about objects, events, or more abstract information (Hegarty, 2011 ). The scope of the previously mentioned examples illustrates the diversity of disciplines that have a vested interest in the influence of visualizations on decision making. While the term decision has a range of meanings in everyday language, here decision making is defined as a choice between two or more competing courses of action (Balleine, 2007 ).

We argue that for visualizations to be most effective, researchers need to integrate decision-making frameworks into visualization cognition research. Reviews of decision making with visual-spatial uncertainty also agree there has been a general lack of emphasis on mental processes within the visualization decision-making literature (Kinkeldey, MacEachren, Riveiro, & Schiewe, 2017 ; Kinkeldey, MacEachren, & Schiewe, 2014 ). The framework that has dominated applied decision-making research for the last 30 years is a dual-process account of decision making. Dual-process theories propose that we have two types of decision processes: one for automatic, easy decisions (Type 1); and another for more contemplative decisions (Type 2) (Kahneman & Frederick, 2002 ; Stanovich, 1999 ). Footnote 1 Even though many research areas involving higher-level cognition have made significant efforts to incorporate dual-process theories (Evans, 2008 ), visualization research has yet to directly test the application of current decision-making frameworks or develop an effective cognitive model for decision making with visualizations. The goal of this work is to integrate a dual-process account of decision making with established cognitive frameworks of visualization comprehension.

In this paper, we present an overview of current decision-making theories and existing visualization cognition frameworks, followed by a proposal for an integrated model of decision making with visualizations, and a selective review of visualization decision-making studies to determine if there is cross-domain support for a dual-process account of decision making with visualizations. As a preview, we will illustrate Type 1 and 2 processing in decision making with visualizations using four cross-domain findings that we observed in the literature review. Our focus here is on demonstrating how dual-processing can be a useful framework for examining visualization decision-making research. We selected the cross-domain findings as relevant demonstrations of Type 1 and 2 processing that were shared across the studies reviewed, but they do not represent all possible examples of dual-processing in visualization decision-making research. The review documents each of the cross-domain findings, in turn, using examples from studies in multiple domains. These cross-domain findings differ in their reliance on Type 1 and Type 2 processing. We conclude with recommendations for future work and implications for visualization designers.

Decision-making frameworks

Decision-making researchers have pursued two dominant research paths to study how humans make decisions under risk. The first assumes that humans make rational decisions, which are based on weighted and ordered probability functions and can be mathematically modeled (e.g. Kunz, 2004 ; Von Neumann, 1953 ). The second proposes that people often make intuitive decisions using heuristics (Gigerenzer, Todd, & ABC Research Group, 2000 ; Kahneman & Tversky, 1982 ). While there is fervent disagreement on the efficacy of heuristics and whether human behavior is rational (Vranas, 2000 ), there is more consensus that we can make both intuitive and strategic decisions (Epstein, Pacini, Denes-Raj, & Heier, 1996 ; Evans, 2008 ; Evans & Stanovich, 2013 ; cf. Keren & Schul, 2009 ). The capacity to make intuitive and strategic decisions is described by a dual-process account of decision making, which suggests that humans make fast, easy, and computationally light decisions (known as Type 1 processing) by default, but can also make slow, contemplative, and effortful decisions by employing Type 2 processing (Kahneman, 2011 ). Various versions of dual-processing theory exist, with the key distinctions being in the attributes associated with each type of process (for a more detailed review of dual-process theories, see Evans & Stanovich, 2013 ). For example, older dual-systems accounts of decision making suggest that each process is associated with specific cognitive or neurological systems. In contrast, dual-process (sometimes termed dual-type) theories propose that the processes are distinct but do not necessarily occur in separate cognitive or neurological systems (hence the use of process over system) (Evans & Stanovich, 2013 ).

Many applied domains have adapted a dual-processing model to explain task- and domain-specific decisions, with varying degrees of success (Evans, 2008 ). For example, when a physician is deciding if a patient should be assigned to a coronary care unit or a regular nursing bed, the doctor can use a heuristic or utilize heart disease predictive instruments to make the decision (Marewski & Gigerenzer, 2012 ). In the case of the heuristic, the doctor would employ a few simple rules (diagrammed in Fig.  1 ) that would guide her decision, such as considering the patient’s chief complaint being chest pain. Another approach is to apply deliberate mental effort to make a more time-consuming and effortful decision, which could include using heart disease predictive instruments (Marewski & Gigerenzer, 2012 ). In a review of how applied domains in higher-level cognition have implemented a dual-processing model for domain-specific decisions, Evans ( 2008 ) argues that prior work has conflicting accounts of Type 1 and 2 processing. Some studies suggest that the two types work in parallel while others reveal conflicts between the Types (Sloman, 2002 ). In the physician example proposed by Marewski and Gigerenzer ( 2012 ), the two types are not mutually exclusive, as doctors can utilize Type 2 to make a more thoughtful decision that is also influenced by some rules of thumb or Type 1. In sum, Evans ( 2008 ) argues that due to the inconsistency of classifying Type 1 and 2, the distinction between only two types is likely an oversimplification. Evans ( 2008 ) suggests that the literature only consistently supports the identification of processes that require a capacity-limited, working memory resource versus those that do not. Evans and Stanovich ( 2013 ) updated their definition based on new behavioral and neuroscience evidence stating, “the defining characteristic of Type 1 processes is their autonomy. They do not require ‘controlled attention,’ which is another way of saying that they make minimal demands on working memory resources” (p. 236). There is also debate on how to define the term working memory (Cowan, 2017 ). In line with prior work on decision making with visualizations (Patterson et al., 2014 ), we adopt the definition that working memory consists of multiple components that maintain a limited amount of information (their capacity) for a finite period (Cowan, 2017 ). Contemporary theories of working memory also stress the ability to engage attention in a controlled manner to suppress automatic responses and maintain the most task-relevant information with limited capacity (Engle, Kane, & Tuholski, 1999 ; Kane, Bleckley, Conway, & Engle, 2001 ; Shipstead, Harrison, & Engle, 2015 ).

figure 1

Coronary care unit decision tree, which illustrates a sequence of rules that a doctor could use to guide treatment decisions. Redrawn from “Heuristic decision making in medicine” by J. Marewski, and G. Gigerenzer 2012, Dialogues in clinical neuroscience, 14(1) , 77. ST-segment change refers to if certain anomaly appears in the patient’s electrocardiogram. NTG nitroglycerin, MI myocardial infarction, T T-waves with peaking or inversion

Identifying processes that require significant working memory provides a definition of Type 2 processing with observable neural correlates. Therefore, in line with Evans and Stanovich ( 2013 ), in the remainder of this manuscript, we will use significant working memory capacity demands and significant need for cognitive control, as defined above, as the criterion for Type 2 processing. In the context of visualization decision making, processes that require significant working memory are those that depend on the deliberate application of working memory to function. Type 1 processing occurs outside of users’ conscious awareness and may utilize small amounts of working memory but does not rely on conscious processing in working memory to drive the process. It should be noted that Type 1 and 2 processing are not mutually exclusive and many real-world decisions likely incorporate all processes. This review will attempt to identify tasks in visualization decision making that require significant working memory and capacity (Type 2 processing) and those that rely more heavily on Type 1 processing, as a first step to combining decision theory with visualization cognition.

Visualization cognition

Visualization cognition is a subset of visuospatial reasoning, which involves deriving meaning from external representations of visual information that maintain consistent spatial relations (Tversky, 2005 ). Broadly, two distinct approaches delineate visualization cognition models (Shah, Freedman, & Vekiri, 2005 ). The first approach refers to perceptually focused frameworks which attempt to specify the processes involved in perceiving visual information in displays and make predictions about the speed and efficiency of acquiring information from a visualization (e.g. Hollands & Spence, 1992 ; Lohse, 1993 ; Meyer, 2000 ; Simkin & Hastie, 1987 ). The second approach considers the influence of prior knowledge as well as perception. For example, Cognitive Fit Theory (Vessey, 1991), suggests that the user compares a learned graphic convention (mental schema) to the visual depiction. Visualizations that do not match the mental schema require cognitive transformations to make the visualization and mental representation align. For example, Fig.  2 illustrates a fictional relationship between the population growth of Species X and a predator species. At first glance, it may appear that when the predator species was introduced that the population of Species X dropped. However, after careful observation, you may notice that the higher population values are located lower on the Y-axis, which does not match our mental schema for graphs. With some effort, you can mentally reorder the values on the Y-axis to match your mental schema and then you may notice that the introduction of the predator species actually correlates with growth in the population of Species X. When the viewer is forced to mentally transform the visualization to match their mental schema, processing steps are increased, which may increase errors, time to complete a task, and demand on working memory (Vessey, 1991).

figure 2

Fictional relationship between the population growth of Species X and a predator species, where the Y-axis ordering does not match standard graphic conventions. Notice that the y-axis is reverse ordered. This figure was inspired by a controversial graphic produced by Christine Chan of Reuters, which showed the relationship between Florida’s “Stand Your Ground” law and firearm murders with the Y-axis reversed ordered (Lallanilla, 2014 )

Pinker ( 1990 ) proposed a cognitive model (see Fig.  3 ), which provides an integrative structure that denotes the distinction between top-down and bottom-up encoding mechanisms in understanding data graphs. Researchers have generalized this model to propose theories of comprehension, learning, and memory with visual information (Hegarty, 2011 ; Kriz & Hegarty, 2007 ; Shah & Freedman, 2011 ). The Pinker ( 1990 ) model suggests that from the visual array , defined as the unprocessed neuronal firing in response to visualizations, bottom-up encoding mechanisms are utilized to construct a visual description , which is the mental encoding of the visual stimulus. Following encoding, viewers mentally search long-term memory for knowledge relevant for interpreting the visualization. This knowledge is proposed to be in the form of a graph schema.

figure 3

Adapted figure from the Pinker ( 1990 ) model of visualization comprehension, which illustrates each process

Then viewers use a match process, where the graph schema that is the most similar to the visual array is retrieved. When a matching graph schema is found, the schema becomes instantiated . The visualization conventions associated with the graph schema can then help the viewer interpret the visualization ( message assembly process). For example, Fig. 3 illustrates comprehension of a bar chart using the Pinker ( 1990 ) model. In this example, the matched graph schema for a bar graph specifies that the dependent variable is on the Y-axis and the independent variable is on the X-axis; the instantiated graph schema incorporates the visual description and this additional information. The conceptual message is the resulting mental representation of the visualization that includes all supplemental information from long-term memory and any mental transformations the viewer may perform on the visualization. Viewers may need to transform their mental representation of the visualization based on their task or conceptual question . In this example, the viewer’s task is to find the average of A and B. To do this, the viewer must interpolate information in the bar chart and update the conceptual message with this additional information. The conceptual question can guide the construction of the mental representation through interrogation , which is the process of seeking out information that is necessary to answer the conceptual question. Top-down encoding mechanisms can influence each of the processes.

The influences of top-down processes are also emphasized in a previous attempt by Patterson et al. ( 2014 ) to extend visualization cognition theories to decision making. The Patterson et al. ( 2014 ) model illustrates how top-down cognitive processing influences encoding, pattern recognition, and working memory, but not decision making or the response. Patterson et al. ( 2014 ) use the multicomponent definition of working memory, proposed by Baddeley and Hitch ( 1974 ) and summarized by Cowan ( 2017 ) as a “multicomponent system that holds information temporarily and mediates its use in ongoing mental activities” (p. 1160). In this conception of working memory, a central executive controls the functions of working memory. The central executive can, among other functions, control attention and hold information in a visuo-spatial temporary store , which is where information can be maintained temporally for decision making without being stored in long-term memory (Baddeley & Hitch, 1974 ).

While incorporating working memory into a visualization decision-making model is valuable, the Patterson et al. ( 2014 ) model leaves some open questions about relationships between components and processes. For example, their model lacks a pathway for working memory to influence decisions based on top-down processing, which is inconsistent with well-established research in decision science (e.g. Gigerenzer & Todd, 1999; Kahneman & Tversky, 1982 ). Additionally, the normal processing pathway, depicted in the Patterson model, is an oversimplification of the interaction between top-down and bottom-up processing that is documented in a large body of literature (e.g. Engel, Fries, & Singer, 2001 ; Mechelli, Price, Friston, & Ishai, 2004 ).

A proposed integrated model of decision making with visualizations

Our proposed model (Fig.  4 ) introduces a dual-process account of decision making (Evans & Stanovich, 2013 ; Gigerenzer & Gaissmaier, 2011 ; Kahneman, 2011 ) into the Pinker ( 1990 ) model of visualization comprehension. A primary addition of our model is the inclusion of working memory, which is utilized to answer the conceptual question and could have a subsequent impact on each stage of the decision-making process, except bottom-up attention. The final stage of our model includes a decision-making process that derives from the conceptual message and informs behavior. In line with a dual-process account (Evans & Stanovich, 2013 ; Gigerenzer & Gaissmaier, 2011 ; Kahneman, 2011 ), the decision step can either be completed with Type 1 processing, which only uses minimal working memory (Evans & Stanovich, 2013 ) or recruit significant working memory, constituting Type 2 processing. Also following Evans and Stanovich ( 2013 ), we argue that people can make a decision with a visualization while using minimal amounts of working memory. We classify this as Type 1 thinking. Lohse ( 1997 ) found that when participants made judgments about budget allocation using profit charts, individuals with less working memory capacity performed equally well compared to those with more working memory capacity, when they only made decisions about three regions (easier task). However, when participants made judgments about nine regions (harder task), individuals with more working memory capacity outperformed those with less working memory capacity. The results of the study reveal that individual differences in working memory capacity only influence performance on complex decision-making tasks (Lohse, 1997 ). Figure  5 (top) illustrates one way that a viewer could make a Type 1 decision about whether the average value of bars A and B is closer to 2 or 2.2. Figure 5 (top) illustrates how a viewer might make a fast and computationally light decision in which she decides that the middle point between the two bars is closer to the salient tick mark of 2 on the Y-axis and answers 2 (which is incorrect). In contrast, Fig.  5 (bottom) shows a second possible method of solving the same problem by utilizing significant working memory (Type 2 processing). In this example, the viewer has recently learned a strategy to address similar problems, uses working memory to guide a top-down attentional search of the visual array, and identifies the values of A and B. Next, she instantiates a different graph schema than in the prior example by utilizing working memory and completes an effortful mental computation of 2.4 + 1.9/2. Ultimately, the application of working memory leads to a different and more effortful decision than in Fig. 5 (top). This example illustrates how significant amounts of working memory can be used at early stages of the decision-making process and produce downstream effects and more considered responses. In the following sections, we provide a selective review of work on decision making with visualizations that demonstrates direct and indirect evidence for our proposed model.

figure 4

Model of visualization decision making, which emphasizes the influence of working memory. Long-term memory can influence all components and processes in the model either via pre-attentive processes or by conscious application of knowledge

figure 5

Examples of a fast Type 1 (top) and slow Type 2 (bottom) decision outlined in our proposed model of decision making with visualizations. In these examples, the viewer’s task is to decide if the average value of bars A and B are closer to 2 or 2.2. The thick dotted line denotes significant working memory and the thin dotted line negligible working memory

Empirical studies of visualization decision making

Review method.

To determine if there is cross-domain empirical support for a dual-process account of decision making with visualizations, we selectively reviewed studies of complex decision making with computer-generated two-dimensional (2D) static visualizations. To illustrate the application of a dual-process account of decision making to visualization research, this review highlights representative studies from diverse application areas. Interdisciplinary groups conducted many of these studies and, as such, it is not accurate to classify the studies in a single discipline. However, to help the reader evaluate the cross-domain nature of these findings, Table  1 includes the application area for the specific tasks used in each study.

In reviewing this work, we observed four key cross-domain findings that support a dual-process account of decision making (see Table  2 ). The first two support the inclusion of Type 1 processing, which is illustrated by the direct path for bottom-up attention to guide decision making with the minimal application of working memory (see Fig. 5 top). The first finding is that visualizations direct viewers’ bottom-up attention , which can both help and hinder decision making (see “ Bottom-up attention ”). The second finding is that visual-spatial biases comprise a unique category of bias that is a direct result of the visual encoding technique (see “ Visual-Spatial Biases ”). The third finding supports the inclusion of Type 2 processing in our proposed model and suggests that visualizations vary in cognitive fit between the visual description, graph schema, and conceptual question. If the fit is poor (i.e. there is a mismatch between the visualization and a decision-making component), working memory is used to perform corrective mental transformations (see “ Cognitive fit ”). The final cross-domain finding proposes that knowledge-driven processes may interact with the effects of the visual encoding technique (see “ Knowledge-driven processing ”) and could be a function of either Type 1 or 2 processes. Each of these findings will be detailed at length in the relevant sections. The four cross-domain findings do not represent an exhaustive list of all cross-domain findings that pertain to visualization cognition. However, these were selected as illustrative examples of Type 1 and 2 processing that include significant contributions from multiple domains. Further, some of the studies could fit into multiple sections and were included in a particular section as illustrative examples.

Bottom-up attention

The first cross-domain finding that characterizes Type 1 processing in visualization decision making is that visualizations direct participants’ bottom-up attention to specific visual features, which can be either beneficial or detrimental to decision making. Bottom-up attention consists of involuntary shifts in focus to salient features of a visualization and does not utilize working memory (Connor, Egeth, & Yantis, 2004 ), therefore it is a Type 1 process. The research reviewed in this section illustrates that bottom-up attention has a profound influence on decision making with visualizations. A summary of visual features that studies have used to attract bottom-up attention can be found in Table  3 .

Numerous studies show that salient information in a visualization draws viewers’ attention (Fabrikant, Hespanha, & Hegarty, 2010 ; Hegarty, Canham, & Fabrikant, 2010 ; Hegarty, Friedman, Boone, & Barrett, 2016 ; Padilla, Ruginski, & Creem-Regehr, 2017 ; Schirillo & Stone, 2005 ; Stone et al., 2003 ; Stone, Yates, & Parker, 1997 ). The most common methods for demonstrating that visualizations focus viewers’ attention is by showing that viewers miss non-salient but task-relevant information (Schirillo & Stone, 2005 ; Stone et al., 1997 ; Stone et al., 2003 ), viewers are biased by salient information (Hegarty et al., 2016 ; Padilla, Ruginski et al., 2017 ) or viewers spend more time looking at salient information in a visualization (Fabrikant et al., 2010 ; Hegarty et al., 2010 ). For example, Stone et al. ( 1997 ) demonstrated that when viewers are asked how much they would pay for an improved product using the visualizations in Fig.  6 , they focus on the number of icons while missing the base rate of 5,000,000. If a viewer simply totals the icons, the standard product appears to be twice as dangerous as the improved product, but because the base rate is large, the actual difference between the two products is insignificantly small (0.0000003; Stone et al., 1997 ). In one experiment, participants were willing to pay $125 more for improved tires when viewing the visualizations in Fig. 6 compared to a purely textual representation of the information. The authors also demonstrated the same effect for improved toothpaste, with participants paying $0.95 more when viewing a visual depiction compared to text. The authors’ term this heuristic of focusing on salient information and ignoring other data the foreground effect (Stone et al., 1997 ) (see also Schirillo & Stone, 2005 ; Stone et al., 2003 ).

figure 6

Icon arrays used to illustrate the risk of standard or improved tires. Participants were tasked with deciding how much they would pay for the improved tires. Note the base rate of 5 M drivers was represented in text. Redrawn from “Effects of numerical and graphical displays on professed risk-taking behavior” by E. R. Stone, J. F. Yates, & A. M. Parker. 1997, Journal of Experimental Psychology: Applied , 3 (4), 243

A more direct test of visualizations guiding bottom-up attention is to examine if salient information biases viewers’ judgments. One method involves identifying salient features using a behaviorally validated saliency model, which predicts the locations that will attract viewers’ bottom-up attention (Harel, 2015 ; Itti, Koch, & Niebur, 1998 ; Rosenholtz & Jin, 2005 ). In one study, researchers compared participants’ judgments with different hurricane forecast visualizations and then, using the Itti et al. ( 1998 ) saliency algorithm, found that the differences in what was salient in the two visualizations correlated with participants’ performance (Padilla, Ruginski et al., 2017 ). Specifically, they suggested that the salient borders of the Cone of Uncertainty (see Fig.  7 , left), which is used by the National Hurricane Center to display hurricane track forecasts, leads some people to incorrectly believe that the hurricane is growing in physical size, which is a misunderstanding of the probability distribution of hurricane paths that the cone is intended to represent (Padilla, Ruginski et al., 2017 ; see also Ruginski et al., 2016 ). Further, they found that when the same data were represented as individual hurricane paths, such that there was no salient boundary (see Fig. 7 , right), viewers intuited the probability of hurricane paths more effectively than the Cone of Uncertainty. However, an individual hurricane path biased viewers’ judgments if it intersected a point of interest. For example, in Fig. 7 (right), participants accurately judged that locations closer to the densely populated lines (highest likelihood of storm path) would receive more damage. This correct judgment changed when a location farther from the center of the storm was intersected by a path, but the closer location was not (see locations a and b in Fig. 7 right). With both visualizations, the researchers found that viewers were negatively biased by the salient features for some tasks (Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ).

figure 7

An example of the Cone of Uncertainty ( left ) and the same data represented as hurricane paths ( right ). Participants were tasked with evaluating the level of damage that would incur to offshore oil rigs at specific locations, based on the hurricane forecast visualization. Redrawn from “Effects of ensemble and summary displays on interpretations of geospatial uncertainty data” by L. M. Padilla, I. Ruginski, and S. H. Creem-Regehr. 2017, Cognitive Research: Principles and Implications , 2 (1), 40

That is not to say that saliency only negatively impacts decisions. When incorporated into visualization design, saliency can guide bottom-up attention to task-relevant information, thereby improving performance (e.g. Fabrikant et al., 2010 ; Fagerlin, Wang, & Ubel, 2005 ; Hegarty et al., 2010 ; Schirillo & Stone, 2005 ; Stone et al., 2003 ; Waters, Weinstein, Colditz, & Emmons, 2007 ). One compelling example using both eye-tracking measures and a saliency algorithm demonstrated that salient features of weather maps directed viewers’ attention to different variables that were visualized on the maps (Hegarty et al., 2010 ) (see also Fabrikant et al., 2010 ). Interestingly, when the researchers manipulated the relative salience of temperature versus pressure (see Fig.  8 ), the salient features captured viewers’ overt attention (as measured by eye fixations) but did not influence performance, until participants were trained on how to effectively interpret the features. Once viewers were trained, their judgments were facilitated when the relevant features were more salient (Hegarty et al., 2010 ). This is an instructive example of how saliency may direct viewers’ bottom-up attention but may not influence their performance until viewers have the relevant top-down knowledge to capitalize on the affordances of the visualization.

figure 8

Eye-tracking data from Hegarty et al. ( 2010 ). Participants viewed an arrow located in Utah (obscured by eye-tracking data in the figure) and made judgments about whether the arrow correctly identified the wind direction. The black isobars were the task-relevant information. Notice that after instructions, viewers with the pressure-salient visualizations focused on the isobars surrounding Utah, rather than on the legend or in other regions. The panels correspond to the conditions in the original study

In sum, the reviewed studies suggest that bottom-up attention has a profound influence on decision making with visualizations. This is noteworthy because bottom-up attention is a Type 1 process. At a minimum, the work suggests that Type 1 processing influences the first stages of decision making with visualizations. Further, the studies cited in this section provide support for the inclusion of bottom-up attention in our proposed model.

  • Visual-spatial biases

A second cross-domain finding that relates to Type 1 processing is that visualizations can give rise to visual-spatial biases that can be either beneficial or detrimental to decision making. We are proposing the new concept of visual-spatial biases and defining this term as a bias that elicits heuristics, which are a direct result of the visual encoding technique. Visual-spatial biases likely originate as a Type 1 process as we suspect they are connected to bottom-up attention, and if detrimental to decision making, have to be actively suppressed by top-down knowledge and cognitive control mechanisms (see Table  4 for summary of biases documented in this section). Visual-spatial biases can also improve decision-making performance. As Card, Mackinlay, and Shneiderman ( 1999 ) point out, we can use vision to think , meaning that visualizations can capitalize on visual perception to interpret a visualization without effort when the visual biases elucidated by the visualization are consistent with the correct interpretation.

Tversky ( 2011 ) presents a taxonomy of visual-spatial communications that are intrinsically related to thought, which are likely the bases for visual-spatial biases (see also Fabrikant & Skupin, 2005 ). One of the most commonly documented visual-spatial biases that we observed across domains is a containment conceptualization of boundary representations in visualizations. Tversky ( 2011 ) makes the analogy, “Framing a picture is a way of saying that what is inside the picture has a different status from what is outside the picture” (p. 522). Similarly, Fabrikant and Skupin ( 2005 ) describe how, “They [boundaries] help partition an information space into zones of relative semantic homogeneity” (p. 673). However, in visualization design, it is common to take continuous data and visually represent them with boundaries (i.e. summary statistics, error bars, isocontours, or regions of interest; Padilla et al., 2015 ; Padilla, Quinan, Meyer, & Creem-Regehr, 2017 ). Binning continuous data is a reasonable approach, particularly when intended to make the data simpler for viewers to understand (Padilla, Quinan, et al., 2017 ). However, it may have the unintended consequence of creating artificial boundaries that can bias users—leading them to respond as if data within a containment is more similar than data across boundaries. For example, McKenzie, Hegarty, Barrett, and Goodchild ( 2016 ) showed that participants were more likely to use a containment heuristic to make decisions about Google Map’s blue dot visualization when the positional uncertainty data were visualized as a bounded circle (Fig.  9 right) compared to a Gaussian fade (Fig. 9 left) (see also Newman & Scholl, 2012 ; Ruginski et al., 2016 ). Recent work by Grounds, Joslyn, and Otsuka ( 2017 ) found that viewers demonstrate a “deterministic construal error” or the belief that visualizations of temperature uncertainty represent a deterministic forecast. However, the deterministic construal error was not observed with textual representations of the same data (see also Joslyn & LeClerc, 2013 ).

figure 9

Example stimuli from McKenzie et al. ( 2016 ) showing circular semi-transparent overlays used by Google Maps to indicate the uncertainty of the users’ location. Participants compared two versions of these visualizations and determined which represented the most accurate positional location. Redrawn from “Assessing the effectiveness of different visualizations for judgments of positional uncertainty” by G. McKenzie, M. Hegarty, T. Barrett, and M. Goodchild. 2016, International Journal of Geographical Information Science , 30 (2), 221–239

Additionally, some visual-spatial biases follow the same principles as more well-known decision-making biases revealed by researchers in behavioral economics and decision science. In fact, some decision-making biases, such as anchoring , the tendency to use the first data point to make relative judgments, seem to have visual correlates (Belia, Fidler, Williams, & Cumming, 2005 ). For example, Belia et al. ( 2005 ) asked experts with experience in statistics to align two means (representing “Group 1” and “Group 2”) with error bars so that they represented data ranges that were just significantly different (see Fig.  10 for example of stimuli). They found that when the starting position of Group 2 was around 800 ms, participants placed Group 2 higher than when the starting position for Group 2 was at around 300 ms. This work demonstrates that participants used the starting mean of Group 2 as an anchor or starting point of reference, even though the starting position was arbitrary. Other work finds that visualizations can be used to reduce some decision-making biases including anecdotal evidence bias (Fagerlin et al., 2005 ), side effect aversion (Waters et al., 2007 ; Waters, Weinstein, Colditz, & Emmons, 2006 ), and risk aversion (Schirillo & Stone, 2005 ).

figure 10

Example display and instructions from Belia et al. ( 2005 ). Redrawn from “Researchers misunderstand confidence intervals and standard error bars” by S. Belia, F. Fidler, J. Williams, and G. Cumming. 2005, Psychological Methods, 10 (4), 390. Copyright 2005 by “American Psychological Association”

Additionally, the mere presence of a visualization may inherently bias viewers. For example, viewers find scientific articles with high-quality neuroimaging figures to have greater scientific reasoning than the same article with a bar chart or without a figure (McCabe & Castel, 2008 ). People tend to unconsciously believe that high-quality scientific images reflect high-quality science—as illustrated by work from Keehner, Mayberry, and Fischer ( 2011 ) showing that viewers rate articles with three-dimensional brain images as more scientific than those with 2D images, schematic drawings, or diagrams (See Fig.  11 ). Unintuitively, however, high-quality complex images can be detrimental to performance compared to simpler visualizations (Hegarty, Smallman, & Stull, 2012 ; St. John, Cowen, Smallman, & Oonk, 2001 ; Wilkening & Fabrikant, 2011 ). Hegarty et al. ( 2012 ) demonstrated that novice users prefer realistically depicted maps (see Fig.  12 ), even though these maps increased the time taken to complete the task and focused participants’ attention on irrelevant information (Ancker, Senathirajah, Kukafka, & Starren, 2006 ; Brügger, Fabrikant, & Çöltekin, 2017 ; St. John et al., 2001 ; Wainer, Hambleton, & Meara, 1999 ; Wilkening & Fabrikant, 2011 ). Interestingly, professional meteorologists also demonstrated the same biases as novice viewers (Hegarty et al., 2012 ) (see also Nadav-Greenberg, Joslyn, & Taing, 2008 ).

figure 11

Image showing participants’ ratings of three-dimensionality and scientific credibility for a given neuroimaging visualization, originally published in grayscale (Keehner et al., 2011 )

figure 12

Example stimuli from Hegarty et al. ( 2012 ) showing maps with varying levels of realism. Both novice viewers and meteorologists were tasked with selecting a visualization to use and performing a geospatial task. The panels correspond to the conditions in the original study

We argue that visual-spatial biases reflect a Type 1 process, occurring automatically with minimal working memory. Work by Sanchez and Wiley ( 2006 ) provides direct evidence for this assertion using eye-tracking data to demonstrate that individuals with less working memory capacity attend to irrelevant images in a scientific article more than those with greater working memory capacity. The authors argue that we are naturally drawn to images (particularly high-quality depictions) and that significant working memory capacity is required to shift focus away from images that are task-irrelevant. The ease by which visualizations captivate our focus and direct our bottom-up attention to specific features likely increases the impact of these biases, which may be why some visual-spatial biases are notoriously difficult to override using working memory capacity (see Belia et al., 2005 ; Boone, Gunalp, & Hegarty, in press ; Joslyn & LeClerc, 2013 ; Newman & Scholl, 2012 ). We speculate that some visual-spatial biases are intertwined with bottom-up attention—occurring early in the decision-making process and influencing the down-stream processes (see our model in Fig. 4 for reference), making them particularly unremitting.

Cognitive fit

We also observe a cross-domain finding involving Type 2 processing, which suggests that if there is a mismatch between the visualization and a decision-making component, working memory is used to perform corrective mental transformations. Cognitive fit is a term used to describe the correspondence between the visualization and conceptual question or task (see our model for reference; for an overview of cognitive fit, see Vessey, Zhang, & Galletta, 2006 ). Those interested in examining cognitive fit generally attempt to identify and reduce mismatches between the visualization and one of the decision-making components (see Table  5 for a breakdown of the decision-making components that the reviewed studies evaluated). When there is a mismatch produced by the default Type 1 processing, it is argued that significant working memory (Type 2 processing) is required to resolve the discrepancy via mental transformations (Vessey et al., 2006 ). As working memory is capacity limited, the magnitude of mental transformation or amount of working memory required is one predictor of reaction times and errors.

Direct evidence for this claim comes from work demonstrating that cognitive fit differentially influenced the performance of individuals with more and less working memory capacity (Zhu & Watts, 2010 ). The task was to identify which two nodes in a social media network diagram should be removed to disconnect the maximal number of nodes. As predicted by cognitive fit theory, when the visualization did not facilitate the task (Fig.  13 left), participants with less working memory capacity were slower than those with more working memory capacity. However, when the visualization aligned with the task (Fig.  13 right), there was no difference in performance. This work suggests that when there is misalignment between the visualization and a decision-making process, people with more working memory capacity have the resources to resolve the conflict, while those with less resources show performance degradations. Footnote 2 Other work only found a modest relationship between working memory capacity and correct interpretations of high and low temperature forecast visualizations (Grounds et al., 2017 ), which suggests that, for some visualizations, viewers utilize little working memory.

figure 13

Examples of social media network diagrams from Zhu and Watts ( 2010 ). The authors argue that the figure on the right is more aligned with the task of identifying the most interconnected nodes than the figure on the left

As illustrated in our model, working memory can be recruited to aid all stages of the decision-making process except bottom-up attention. Work that examines cognitive fit theory provides indirect evidence that working memory is required to resolve conflicts in the schema matching and a decision-making component. For example, one way that a mismatch between a viewer’s mental schema and visualization can arise is when the viewer uses a schema that is not optimal for the task. Tversky, Corter, Yu, Mason, and Nickerson ( 2012 ) primed participants to use different schemas by describing the connections in Fig.  14 in terms of either transfer speed or security levels. Participants then decided on the most efficient or secure route for information to travel between computer nodes with either a visualization that encoded data using the thickness of connections, containment, or physical distance (see Fig.  14 ). Tversky et al. ( 2012 ) found that when the links were described based on their information transfer speed, thickness and distance visualizations were the most effective—suggesting that the speed mental schema was most closely matched to the thickness and distance visualizations, whereas the speed schema required mental transformations to align with the containment visualization. Similarly, the thickness and containment visualizations outperformed the distance visualization when the nodes were described as belonging to specific systems with different security levels. This work and others (Feeney, Hola, Liversedge, Findlay, & Metcalf, 2000 ; Gattis & Holyoak, 1996 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ) provides indirect evidence that gratuitous realignment between mental schema and the visualization can be error-prone and visualization designers should work to reduce the number of transformations required in the decision-making process.

figure 14

Example of stimuli from Tversky et al. ( 2012 ) showing three types of encoding techniques for connections between nodes (thickness, containment, and distance). Participants were asked to select routes between nodes with different descriptions of the visualizations. Redrawn from “Representing category and continuum: Visualizing thought” by B. Tversky, J. Corter, L. Yu, D. Mason, and J. Nickerson. In Diagrams 2012 (p. 27), P. Cox, P. Rodgers, and B. Plimmer (Eds.), 2012, Berlin Heidelberg: Springer-Verlag

Researchers from multiple domains have also documented cases of misalignment between the task, or conceptual question, and the visualization. For example, Vessey and Galletta ( 1991 ) found that participants completed a financial-based task faster when the visualization they chose (graph or table, see Fig.  15 ) matched the task (spatial or textual). For the spatial task, participants decided which month had the greatest difference between deposits and withdrawals. The textual or symbolic tasks involved reporting specific deposit and withdrawal amounts for various months. The authors argued that when there is a mismatch between the task and visualization, the additional transformation accounts for the increased time taken to complete the task (Vessey & Galletta, 1991 ) (see also Dennis & Carte, 1998 ; Huang et al., 2006 ), which likely takes place in the inference process of our proposed model.

figure 15

Examples of stimuli from Vessey and Galletta ( 1991 ) depicting deposits and withdraw amounts over the course of a year with a graph ( a ) and table ( b ). Participants completed either a spatial or textual task with the chart or table. Redrawn from “Cognitive fit: An empirical study of information acquisition” by I. Vessey, and D. Galletta. 1991, Information systems research, 2 (1), 72–73. Copyright 1991 by “INFORMS”

The aforementioned studies provide direct (Zhu & Watts, 2010 ) and indirect (Dennis & Carte, 1998 ; Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Huang et al., 2006 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ; Vessey & Galletta, 1991 ) evidence that Type 2 processing recruits working memory to resolve misalignment between decision-making processes and the visualization that arise from default Type 1 processing. These examples of Type 2 processing using working memory to perform effortful mental computations are consistent with the assertions of Evans and Stanovich ( 2013 ) that Type 2 processes enact goal directed complex processing. However, it is not clear from the reviewed work how exactly the visualization and decision-making components are matched. Newman and Scholl ( 2012 ) propose that we match the schema and visualization based on the similarities between the salient visual features, although this proposal has not been tested. Further, work that assesses cognitive fit in terms of the visualization and task only examines the alignment of broad categories (i.e., spatial or semantic). Beyond these broad classifications, it is not clear how to predict if a task and visualization are aligned. In sum, there is not a sufficient cross-disciplinary theory for how mental schemas and tasks are matched to visualizations. However, it is apparent from the reviewed work that Type 2 processes (requiring working memory) can be recruited during the schema matching and inference processes.

Either type 1 and/or 2

Knowledge-driven processing.

In a review of map-reading cognition, Lobben ( 2004 ) states, “…research should focus not only on the needs of the map reader but also on their map-reading skills and abilities” (p. 271). In line with this statement, the final cross-domain finding is that the effects of knowledge can interact with the affordances or biases inherent in the visualization method. Knowledge may be held temporally in working memory (Type 2), held in long-term knowledge but effortfully used (Type 2), or held in long-term knowledge but automatically applied (Type 1). As a result, knowledge-driven processing can involve either Type 1 or Type 2 processes.

Both short- and long-term knowledge can influence visualization affordances and biases. However, it is difficult to distinguish whether Type 2 processing is using significant working memory capacity to temporarily hold knowledge or if participants have stored the relevant knowledge in long-term memory and processing is more automatic. Complicating the issue, knowledge stored in long-term memory can influence decision making with visualizations using both Type 1 and 2 processing. For example, if you try to remember Pythagorean’s Theorem, which you may have learned in high school or middle school, you may recall that a 2  + b 2  = c 2 , where c represents the length of the hypotenuse and a and b represent the lengths of the other two sides of a triangle. Unless you use geometry regularly, you likely had to strenuously search in long-term memory for the equation, which is a Type 2 process and requires significant working memory capacity. In contrast, if you are asked to recall your childhood phone number, the number might automatically come to mind with minimal working memory required (Type 1 processing).

In this section, we highlight cases where knowledge either influenced decision making with visualizations or was present but did not influence decisions (see Table  6 for the type of knowledge examined in each study). These studies are organized based on how much time the viewers had to incorporate the knowledge (i.e. short-term instructions and long-term individual differences in abilities and expertise), which may be indicative of where the knowledge is stored. However, many factors other than time influence the process of transferring knowledge by working memory capacity to long-term knowledge. Therefore, each of the studies cited in this section could be either Type 1, Type 2, or both types of processing.

One example of participants using short-term knowledge to override a familiarity bias comes from work by Bailey, Carswell, Grant, and Basham ( 2007 ) (see also Shen, Carswell, Santhanam, & Bailey, 2012 ). In a complex geospatial task for which participants made judgments about terrorism threats, participants were more likely to select familiar map-like visualizations rather than ones that would be optimal for the task (see Fig.  16 ) (Bailey et al., 2007 ). Using the same task and visualizations, Shen et al. ( 2012 ) showed that users were more likely to choose an efficacious visualization when given training concerning the importance of cognitive fit and effective visualization techniques. In this case, viewers were able to use knowledge-driven processing to improve their performance. However, Joslyn and LeClerc ( 2013 ) found that when participants viewed temperature uncertainty, visualized as error bars around a mean temperature prediction, they incorrectly believed that the error bars represented high and low temperatures. Surprisingly, participants maintained this belief despite a key, which detailed the correct way to interpret each temperature forecast (see also Boone et al., in press ). The authors speculated that the error bars might have matched viewers’ mental schema for high- and low-temperature forecasts (stored in long-term memory) and they incorrectly utilized the high-/low-temperature schema rather than incorporating new information from the key. Additionally, the authors propose that because the error bars were visually represented as discrete values, that viewers may have had difficulty reimagining the error bars as points on a distribution, which they term a deterministic construal error (Joslyn & LeClerc, 2013 ). Deterministic construal visual-spatial biases may also be one of the sources of misunderstanding of the Cone of Uncertainty (Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ). A notable difference between these studies and the work of Shen et al. ( 2012 ) is that Shen et al. ( 2012 ) used instructions to correct a familiarity bias, which is a cognitive bias originally documented in the decision-making literature that is not based on the visual elements in the display. In contrast, the biases in Joslyn and LeClerc ( 2013 ) were visual-spatial biases. This provides further evidence that visual-spatial biases may be a unique category of biases that warrant dedicated exploration, as they are harder to influence with knowledge-driven processing.

figure 16

Example of different types of view orientations used by examined by Bailey et al. ( 2007 ). Participants selected one of these visualizations and then used their selection to make judgments including identifying safe passageways, determining appropriate locations for firefighters, and identifying suspicious locations based on the height of buildings. The panels correspond to the conditions in the original study

Regarding longer-term knowledge, there is substantial evidence that individual differences in knowledge impact decision making with visualizations. For example, numerous studies document the benefit of visualizations for individuals with less health literacy, graph literacy, and numeracy (Galesic & Garcia-Retamero, 2011 ; Galesic, Garcia-Retamero, & Gigerenzer, 2009 ; Keller, Siegrist, & Visschers, 2009 ; Okan, Galesic, & Garcia-Retamero, 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Reyna, Nelson, Han, & Dieckmann, 2009 ; Rodríguez et al., 2013 ). Visual depictions of health data are particularly useful because health data often take the form of probabilities, which are unintuitive. Visualizations inherently illustrate probabilities (i.e. 10%) as natural frequencies (i.e. 10 out of 100), which are more intuitive (Hoffrage & Gigerenzer, 1998 ). Further, by depicting natural frequencies visually (see example in Fig.  17 ), viewers can make perceptual comparisons rather than mathematical calculations. This dual benefit is likely the reason visualizations produce facilitation for individuals with less health literacy, graph literacy, and numeracy.

figure 17

Example of stimuli used by Galesic et al. ( 2009 ) in a study demonstrating that natural frequency visualizations can help individuals overcome less numeracy. Participants completed three medical scenario tasks using similar visualizations as depicted here, in which they were asked about the effects of aspirin on risk of stroke or heart attack and about a hypothetical new drug. Redrawn from “Using icon arrays to communicate medical risks: overcoming less numeracy” by M. Galesic, R. Garcia-Retamero, and G. Gigerenzer. 2009, Health Psychology, 28 (2), 210

These studies are good examples of how designers can create visualizations that capitalize on Type 1 processing to help viewers accurately make decisions with complex data even when they lack relevant knowledge. Based on the reviewed work, we speculate that well-designed visualizations that utilize Type 1 processing to intuitively illustrate task-relevant relationships in the data may be particularly beneficial for individuals with less numeracy and graph literacy, even for simple tasks. However, poorly designed visualizations that require superfluous mental transformations may be detrimental to the same individuals. Further, individual differences in expertise, such as graph literacy, which have received more attention in healthcare communication (Galesic & Garcia-Retamero, 2011 ; Nayak et al., 2016 ; Okan et al., 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Rodríguez et al., 2013 ), may play a large role in how viewers complete even simple tasks in other domains such as map-reading (Kinkeldey et al., 2017 ).

Less consistent are findings on how more experienced users incorporate knowledge acquired over longer periods of time to make decisions with visualizations. Some research finds that students’ decision-making and spatial abilities improved during a semester-long course on Geographic Information Science (GIS) (Lee & Bednarz, 2009 ). Other work finds that experts perform the same as novices (Riveiro, 2016 ), experts can exhibit visual-spatial biases (St. John et al., 2001 ) and experts perform more poorly than expected in their domain of visual expertise (Belia et al., 2005 ). This inconsistency may be due in part to the difficulty in identifying when and if more experienced viewers are automatically applying their knowledge or employing working memory. For example, it is unclear if the students in the GIS course documented by Lee and Bednarz ( 2009 ) developed automatic responses (Type 1) or if they learned the information and used working memory capacity to apply their training (Type 2).

Cheong et al. ( 2016 ) offer one way to gauge how performance may change when one is forced to use Type 1 processing, but then allowed to use Type 2 processing. In a wildfire task using multiple depictions of uncertainty (see Fig.  18 ), Cheong et al. ( 2016 ) found that the type of uncertainty visualization mattered when participants had to make fast Type 1 decisions (5 s) about evacuating from a wildfire. But when given sufficient time to make Type 2 decisions (30 s), participants were not influenced by the visualization technique (see also Wilkening & Fabrikant, 2011 ).

figure 18

Example of multiple uncertainty visualization techniques for wildfire risk by Cheong et al. ( 2016 ). Participants were presented with a house location (indicated by an X), and asked if they would stay or leave based on one of the wildfire hazard communication techniques shown here. The panels correspond to the conditions in the original study

Interesting future work could limit experts’ time to complete a task (forcing Type 1 processing) and then determine if their judgments change when given more time to complete the task (allowing for Type 2 processing). To test this possibility further, a dual-task paradigm could be used such that experts’ working memory capacity is depleted by a difficult secondary task that also required working memory capacity. Some examples of secondary tasks in a dual-task paradigm include span tasks that require participants to remember or follow patterns of information, while completing the primary task, then report the remembered or relevant information from the pattern (for a full description of theoretical bases for a dual-task paradigm see Pashler, 1994 ). To our knowledge, only one study has used a dual-task paradigm to evaluate cognitive load of a visualization decision-making task (Bandlow et al., 2011 ). However, a growing body of research on other domains, such as wayfinding and spatial cognition, demonstrates the utility of using dual-task paradigms to understand the types of working memory that users employ for a task (Caffò, Picucci, Di Masi, & Bosco, 2011 ; Meilinger, Knauff, & Bülthoff, 2008 ; Ratliff & Newcombe, 2005 ; Trueswell & Papafragou, 2010 ).

Span tasks are examples of spatial or verbal secondary tasks, which include remembering the orientations of an arrow (taxes visual-spatial memory, (Shah & Miyake, 1996 ) or counting backward by 3 s (taxes verbal processing and short-term memory) (Castro, Strayer, Matzke, & Heathcote, 2018 ). One should expect more interference if the primary and secondary tasks recruit the same processes (i.e. visual-spatial primary task paired with a visual-spatial memory span task). An example of such an experimental design is illustrated in Fig.  19 . In the dual-task trial illustrated in Fig.  19 , if participants responses are as fast and accurate as the baseline trial then participants are likely not using significant amounts of working memory capacity for that task. If the task does require significant working memory capacity, then the inclusion of the secondary task should increase the time taken to complete the primary task and potentially produce errors in both the secondary and primary tasks. In visualization decision-making research, this is an open area of exploration for researchers and designers that are interested in understanding how working memory capacity and a dual-process account of decision making applies to their visualizations and application domains.

figure 19

A diagram of a dual-tasking experiment is shown using the same task as in Fig. 5 . Responses resulting from Type 1 and 2 processing are illustrated. The dual-task trial illustrates how to place additional load on working memory capacity by having the participant perform a demanding secondary task. The impact of the secondary task is illustrated for both time and accuracy. Long-term memory can influence all components and processes in the model either via pre-attentive processes or by conscious application of knowledge

In sum, this section documents cases where knowledge-driven processing does and does not influence decision making with visualizations. Notably, we describe numerous studies where well-designed visualizations (capitalizing on Type 1 processing) focus viewers’ attention on task-relevant relationships in the data, which improves decision accuracy for individuals with less developed health literacy, graph literacy, and numeracy. However, the current work does not test how knowledge-driven processing maps on to the dual-process model of decision making. Knowledge may be held temporally by working memory capacity (Type 2), held in long-term knowledge but strenuously utilized (Type 2), or held in long-term knowledge but automatically applied (Type 1). More work is needed to understand if a dual-process account of decision making accurately describes the influence of knowledge-driven processing on decision making with visualizations. Finally, we detailed an example of a dual-task paradigm as one way to evaluate if viewers are employing Type 1 processing.

Review summary

Throughout this review, we have provided significant direct and indirect evidence that a dual-process account of decision making effectively describes prior findings from numerous domains interested in visualization decision making. The reviewed work provides support for specific processes in our proposed model including the influences of working memory, bottom-up attention, schema matching, inference processes, and decision making. Further, we identified key commonalities in the reviewed work relating to Type 1 and Type 2 processing, which we added to our proposed visualization decision-making model. The first is that utilizing Type 1 processing, visualizations serve to direct participants’ bottom-up attention to specific information, which can be either beneficial or detrimental for decision making (Fabrikant et al., 2010 ; Fagerlin et al., 2005 ; Hegarty et al., 2010 ; Hegarty et al., 2016 ; Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ; Schirillo & Stone, 2005 ; Stone et al., 1997 ; Stone et al., 2003 ; Waters et al., 2007 ). Consistent with assertions from cognitive science and scientific visualization (Munzner, 2014 ), we propose that visualization designers should identify the critical information needed for a task and use a visual encoding technique that directs participants’ attention to this information. We encourage visualization designers who are interested in determining which elements in their visualizations will likely attract viewers’ bottom-up attention, to see the Itti et al. ( 1998 ) saliency model, which has been validated with eye-tracking measures (for implementation of this model along with Matlab code see Padilla, Ruginski et al., 2017 ). If deliberate effort is not made to capitalize on Type 1 processing by focusing the viewer’s attention on task-relevant information, then the viewer will likely focus on distractors via Type 1 processing, resulting in poor decision outcomes.

A second cross-domain finding is the introduction of a new concept, visual-spatial biases , which can also be both beneficial and detrimental to decision making. We define this term as a bias that elicits heuristics, which is a direct result of the visual encoding technique. We provide numerous examples of visual-spatial biases across domains (for implementation of this model along with Matlab code, see Padilla, Ruginski et al., 2017 ). The novel utility of identifying visual-spatial biases is that they potentially arise early in the decision-making process during bottom-up attention, thus influencing the entire downstream process, whereas standard heuristics do not exclusively occur at the first stage of decision making. This possibly accounts for the fact that visual-spatial biases have proven difficult to overcome (Belia et al., 2005 ; Grounds et al., 2017 ; Joslyn & LeClerc, 2013 ; Liu et al., 2016 ; McKenzie et al., 2016 ; Newman & Scholl, 2012 ; Padilla, Ruginski et al., 2017 ; Ruginski et al., 2016 ). Work by Tversky ( 2011 ) presents a taxonomy of visual-spatial communications that are intrinsically related to thought, which are likely the bases for visual-spatial biases.

We have also revealed cross-domain findings involving Type 2 processing, which suggest that if there is a mismatch between the visualization and a decision-making component, working memory is used to perform corrective mental transformations. In scenarios where the visualization is aligned with the mental schema and task, performance is fast and accurate (Joslyn & LeClerc, 2013 ). The types of mismatches observed in the reviewed literature are likely both domain-specific and domain-general. For example, situations where viewers employ the correct graph schema for the visualization, but the graph schema does not align with the task, are likely domain-specific (Dennis & Carte, 1998 ; Frownfelter-Lohrke, 1998 ; Gattis & Holyoak, 1996 ; Huang et al., 2006 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ). However, other work demonstrates cases where viewers employ a graph schema that does not match the visualization, which is likely domain-general (e.g. Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Tversky et al., 2012 ). In these cases, viewers could accidentally use the wrong graph schema because it appears to match the visualization or they might not have learned a relevant schema. The likelihood of viewers making attribution errors because they do not know the corresponding schema increases when the visualization is less common, such as with uncertainty visualizations. When there is a mismatch, additional working memory is required resulting in increased time taken to complete the task and in some cases errors (e.g. Joslyn & LeClerc, 2013 ; McKenzie et al., 2016 ; Padilla, Ruginski et al., 2017 ). Based on these findings, we recommend that visualization designers should aim to create visualizations that most closely align with a viewer’s mental schema and task. However, additional empirical research is required to understand the nature of the alignment processes, including the exact method we use to mentally select a schema and the classifications of tasks that match visualizations.

The final cross-domain finding is that knowledge-driven processes can interact or override effects of visualization methods. We find that short-term (Dennis & Carte, 1998 ; Feeney et al., 2000 ; Gattis & Holyoak, 1996 ; Joslyn & LeClerc, 2013 ; Smelcer & Carmel, 1997 ; Tversky et al., 2012 ) and long-term knowledge acquisition (Shen et al., 2012 ) can influence decision making with visualizations. However, there are also examples of knowledge having little influence on decisions, even when prior knowledge could be used to improve performance (Galesic et al., 2009 ; Galesic & Garcia-Retamero, 2011 ; Keller et al., 2009 ; Lee & Bednarz, 2009 ; Okan et al., 2015 ; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012 ; Okan, Garcia-Retamero, Galesic, & Cokely, 2012 ; Reyna et al., 2009 ; Rodríguez et al., 2013 ). We point out that prior knowledge seems to have more of an effect on non-visual-spatial biases, such as a familiarity bias (Belia et al., 2005 ; Joslyn & LeClerc, 2013 ; Riveiro, 2016 ; St. John et al., 2001 ), which suggests that visual-spatial biases may be closely related to bottom-up attention. Further, it is unclear from the reviewed work when knowledge switches from relying on working memory capacity for application to automatic application. We argue that Type 1 and 2 processing have unique advantages and disadvantages for visualization decision making. Therefore, it is valuable to understand which process users are applying for specific tasks in order to make visualizations that elicit optimal performance. In the case of experts and long-term knowledge, we propose that one interesting way to test if users are utilizing significant working memory capacity is to employ a dual-task paradigm (illustrated in Fig.  19 ). A dual-task paradigm can be used to evaluate the amount of working memory required and compare the relative working memory required between competing visualization techniques.

We have also proposed a variety of practical recommendations for visualization designers based on the empirical findings and our cognitive framework. Below is a summary list of our recommendations along with relevant section numbers for reference:

Identify the critical information needed for a task and use a visual encoding technique that directs participants’ attention to this information (“ Bottom-up attention ” section);

To determine which elements in a visualization will likely attract viewers’ bottom-up attention try employing a saliency algorithm (see Padilla, Quinan, et al., 2017 ) (see “ Bottom-up attention ”);

Aim to create visualizations that most closely align with a viewer’s mental schema and task demands (see “ Visual-Spatial Biases ”);

Work to reduce the number of transformations required in the decision-making process (see " Cognitive fit ");

To understand if a viewer is using Type 1 or 2 processing employ a dual-task paradigm (see Fig.  19 );

Consider evaluating the impact of individual differences such as graphic literacy and numeracy on visualization decision making.


We use visual information to inform many important decisions. To develop visualizations that account for real-life decision making, we must understand how and why we come to conclusions with visual information. We propose a dual-process cognitive framework expanding on visualization comprehension theory that is supported by empirical studies to describe the process of decision making with visualizations. We offer practical recommendations for visualization designers that take into account human decision-making processes. Finally, we propose a new avenue of research focused on the influence of visual-spatial biases on decision making.

Change history

02 september 2018.

The original article (Padilla et al., 2018) contained a formatting error in Table 2; this has now been corrected with the appropriate boxes marked clearly.

Dual-process theory will be described in greater detail in next section.

It should be noted that in some cases the activation of Type 2 processing should improve decision accuracy. More research is needed that examines cases where Type 2 could improve decision performance with visualizations.

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LMP is the primary author of this study; she was central to the development, writing, and conclusions of this work. SHC, MH, and JS contributed to the theoretical development and manuscript preparation. All authors read and approved the final manuscript.

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LMP is a Ph.D. student at the University of Utah in the Cognitive Neural Science department. LMP is a member of the Visual Perception and Spatial Cognition Research Group directed by Sarah Creem-Regehr, Ph.D., Jeanine Stefanucci, Ph.D., and William Thompson, Ph.D. Her work focuses on graphical cognition, decision making with visualizations, and visual perception. She works on large interdisciplinary projects with visualization scientists and anthropologists.

SHC is a Professor in the Psychology Department of the University of Utah. She received her MA and Ph.D. in Psychology from the University of Virginia. Her research serves joint goals of developing theories of perception-action processing mechanisms and applying these theories to relevant real-world problems in order to facilitate observers’ understanding of their spatial environments. In particular, her interests are in space perception, spatial cognition, embodied cognition, and virtual environments. She co-authored the book Visual Perception from a Computer Graphics Perspective ; previously, she was Associate Editor of Psychonomic Bulletin & Review and Experimental Psychology: Human Perception and Performance .

MH is a Professor in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara. She received her Ph.D. in Psychology from Carnegie Mellon University. Her research is concerned with spatial cognition, broadly defined, and includes research on small-scale spatial abilities (e.g. mental rotation and perspective taking), large-scale spatial abilities involved in navigation, comprehension of graphics, and the role of spatial cognition in STEM learning. She served as chair of the governing board of the Cognitive Science Society and is associate editor of Topics in Cognitive Science and past Associate Editor of Journal of Experimental Psychology: Applied .

JS is an Associate Professor in the Psychology Department at the University of Utah. She received her M.A. and Ph.D. in Psychology from the University of Virginia. Her research focuses on better understanding if a person’s bodily states, whether emotional, physiological, or physical, affects their spatial perception and cognition. She conducts this research in natural settings (outdoor or indoor) and in virtual environments. This work is inherently interdisciplinary given it spans research on emotion, health, spatial perception and cognition, and virtual environments. She is on the editorial boards for the Journal of Experimental Psychology: General and Virtual Environments: Frontiers in Robotics and AI . She also co-authored the book Visual Perception from a Computer Graphics Perspective .

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Padilla, L.M., Creem-Regehr, S.H., Hegarty, M. et al. Decision making with visualizations: a cognitive framework across disciplines. Cogn. Research 3 , 29 (2018). https://doi.org/10.1186/s41235-018-0120-9

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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.

Photo of a student working on her math assignment, with diagrams and formulas written on the photo

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?”

Cartoon image of a photon drawn by the author

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?

Cartoon image of a series of photons hitting Earth’s atmosphere drawn by the author

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?

Chart of math resources provided by the author

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.

What is visual representation?

In the vast landscape of communication, where words alone may fall short, visual representation emerges as a powerful ally. In a world inundated with information, the ability to convey complex ideas, emotions, and data through visual means is becoming increasingly crucial. But what exactly is visual representation, and why does it hold such sway in our understanding?

Defining Visual Representation:

Visual representation is the act of conveying information, ideas, or concepts through visual elements such as images, charts, graphs, maps, and other graphical forms. It’s a means of translating the abstract into the tangible, providing a visual language that transcends the limitations of words alone.

The Power of Images:

The adage “a picture is worth a thousand words” encapsulates the essence of visual representation. Images have an unparalleled ability to evoke emotions, tell stories, and communicate complex ideas in an instant. Whether it’s a photograph capturing a poignant moment or an infographic distilling intricate data, images possess a unique capacity to resonate with and engage the viewer on a visceral level.

Facilitating Understanding:

One of the primary functions of visual representation is to enhance understanding. Humans are inherently visual creatures, and we often process and retain visual information more effectively than text. Complex concepts that might be challenging to grasp through written explanations can be simplified and clarified through visual aids. This is particularly valuable in fields such as science, where intricate processes and structures can be elucidated through diagrams and illustrations.

Visual representation also plays a crucial role in education. In classrooms around the world, teachers leverage visual aids to facilitate learning, making lessons more engaging and accessible. From simple charts that break down historical timelines to interactive simulations that bring scientific principles to life, visual representation is a cornerstone of effective pedagogy.

Data Visualization:

In an era dominated by big data, the importance of data visualization cannot be overstated. Raw numbers and statistics can be overwhelming and abstract, but when presented visually, they transform into meaningful insights. Graphs, charts, and maps are powerful tools for conveying trends, patterns, and correlations, enabling decision-makers to glean actionable intelligence from vast datasets.

Consider the impact of a well-crafted infographic that distills complex research findings into a visually digestible format. Data visualization not only simplifies information but also allows for more informed decision-making in fields ranging from business and healthcare to social sciences and environmental studies.

Cultural and Artistic Expression:

Visual representation extends beyond the realm of information and education; it is also a potent form of cultural and artistic expression. Paintings, sculptures, photographs, and other visual arts serve as mediums through which individuals can convey their emotions, perspectives, and cultural narratives. Artistic visual representation has the power to transcend language barriers, fostering a shared human experience that resonates universally.


In a world inundated with information, visual representation stands as a beacon of clarity and understanding. Whether it’s simplifying complex concepts, conveying data-driven insights, or expressing the depth of human emotion, visual elements enrich our communication in ways that words alone cannot. As we navigate an increasingly visual society, recognizing and harnessing the power of visual representation is not just a skill but a necessity for effective communication and comprehension. So, let us embrace the visual language that surrounds us, unlocking a deeper, more nuanced understanding of the world.

Math Is Visual

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  • Visualizing Powers Through Exponential Growth Patterns

In This Set of Visual Number Talk Prompts…

Students will extend patterns with exponential growth, make near and far predictions about the pattern, describe the pattern in words, and determine the general term. 

String of Related Problems

The following visual number talk is a set of visual patterns that you will share with students one at a time. In order to build on the context from day 1, we will be using the context of weeds (or flowers) growing in a garden. Ask students to describe the change. Collaboratively make a generalization about the relationship between the independent variable, x , and the dependent variable, y , in order to create an exponential general term, written as an expression or as an equation, depending on which you prefer to emerge. Use the exponential expression or equation to determine the value of the 7th term for each exponential relationship.

Visual Math Talk Prompt #1

Students will be prompted with:

How many flowers will there be in the garden by week 7?

Exponential growth

Students will likely recognize that beginning in week 0, the garden began with 1 flower, then doubled each week afterwards. 

By continuing this pattern, students will continue doubling as each week passes until reaching 128 flowers in week 7.

Exponential growth

Be sure to highlight that since we are doubling each week, we can simply take our original number of flowers (1) and multiply by 2 a total of 7 times – once for each of the 7 passing weeks. 

We can write this as:

1 x 2 x 2 x 2 x 2 x 2 x 2 x 2 


Therefore, you should expect 128 flowers by week 7.

Want to Explore These Concepts & Skills Further?

Two (2) additional number talk prompts are available in Day 2 of the Invasive Species problem based math unit that you can dive into now. 

Why not start from the beginning of this contextual 5-day unit of real world lessons from the Make Math Moments Problem Based Units page.

Did you use this in your classroom or at home? How’d it go? Post in the comments!

Math IS Visual. Let’s teach it that way.

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You may also like, using the graph of a linear relationship to make predictions, growing geometric patterns, constructing linear equations from a graph with rise over run, add comment, cancel reply.

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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.

A painting of an abstract night sky over a village, with a tree in the foreground.

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.

A sample usability report that shows a few statistics, a bell curve and a donut chart.

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.

A side-by-side comparison of a simple mockup, and a very verbose description of the same mockup. A developer understands the simple one, and is confused by the verbose one.

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.

A table of names and numbers indicating the funerals of victims of the plague in London in 1665.

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.

A diagram showing how to assemble a chest of drawers from furniture retailer IKEA.

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 simple illustration of a winking girl next to a photograph of a winking girl.

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.

A simple battery level icon next to a screenshot of a battery information dashboard.

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.

A screenshot of Google Maps showing the Design Museum in London, UK. On the left is a profile of the location, on the right is the map.

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.

A screenshot of the homepage Interaction Design Foundation website. Across the top is a menu bar. Beneath the menu bar is a header image with a call to action.

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.

visual representation of growth

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 .

Two pie charts showing user satisfaction. One visualizes data 1 day after release, and the other 1 month after release. The colors are consistent between both charts, but the segment sizes are different.

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.

A screenshot of an Interaction Design Foundation course page. It features information about the course and a video. Beneath this is a pop-up asking the user if they want to drop this course.

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.


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.

A painting of a woman sitting and looking straight at the viewer. Her expression is difficult to read.

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.

Designers use typography to direct attention, create hierarchy and establish rhythm. These benefits help with brand recognition and consistency across mediums.

Read our article to learn how web fonts are critical to the online user experience .

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.

Through iteration and feedback, designers refine visuals. They remove extraneous elements and highlight vital information. Testing with the target audience ensures the design resonates and is comprehensible.

Michal Malewicz explains how to master hierarchy in UI design using the Gestalt rule of proximity:

Answer a Short Quiz to Earn a Gift

Why do designers use visual representation?

  • To guarantee only a specific audience can understand the information
  • To replace the need for any form of written communication
  • To simplify complex information and make it understandable

Which type of visual representation helps to compare data?

  • Article images
  • Line charts
  • Text paragraphs

What is the main purpose of visual hierarchy in design?

  • To decorate the design with more colors
  • To guide the viewer’s attention to the most important elements first
  • To provide complex text for high-level readers

How does color impact visual representation?

  • It has no impact on the design at all.
  • It helps to distinguish different elements and set the mood.
  • It makes the design less engaging for a serious mood.

Why is consistency important in visual representation?

  • It limits creativity, but allows variation in design.
  • It makes sure the visual elements are cohesive and easy to understand.
  • It makes the design unpredictable yet interesting.

Better luck next time!

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Literature on Visual Representation

Here’s the entire UX literature on Visual Representation by the Interaction Design Foundation, collated in one place:

Learn more about Visual Representation

Take a deep dive into Visual Representation with our course Perception and Memory in HCI and UX .

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.

“You can design for all the people some of the time, and some of the people all the time, but you cannot design for all the people all the time.“ – William Hudson (with apologies to Abraham Lincoln)

While “design for all of the people all of the time” is an impossible goal, understanding how the human machine operates is essential to getting ever closer. And of course, building solutions for people with a wide range of abilities, including those with accessibility issues, involves knowing how and why some human faculties fail. As our course tutor, Professor Alan Dix, points out, this is not only a moral duty but, in most countries, also a legal obligation.

Portfolio Project

In the “ Build Your Portfolio: Perception and Memory Project ”, you’ll find a series of practical exercises that will give you first-hand experience in applying what we’ll cover. If you want to complete these optional exercises, you’ll create a series of case studies for your portfolio which you can show your future employer or freelance customers.

This in-depth, video-based course is created with the amazing Alan Dix , the co-author of the internationally best-selling textbook  Human-Computer Interaction and a superstar in the field of Human-Computer Interaction . Alan is currently a professor and Director of the Computational Foundry at Swansea University.

Gain an Industry-Recognized UX Course Certificate

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All open-source articles on Visual Representation

Data visualization for human perception.

visual representation of growth

The Key Elements & Principles of Visual Design

visual representation of growth


Guidelines for Good Visual Information Representations

visual representation of growth

  • 4 years ago

Philosophy of Interaction

Information visualization – an introduction to multivariate analysis.

visual representation of growth

  • 8 years ago

Aesthetic Computing

How to represent linear data visually for information visualization.

visual representation of growth

  • 5 years ago

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Blog Graphic Design 15 Effective Visual Presentation Tips To Wow Your Audience

15 Effective Visual Presentation Tips To Wow Your Audience

Written by: Krystle Wong Sep 28, 2023

Visual Presentation Tips

So, you’re gearing up for that big presentation and you want it to be more than just another snooze-fest with slides. You want it to be engaging, memorable and downright impressive. 

Well, you’ve come to the right place — I’ve got some slick tips on how to create a visual presentation that’ll take your presentation game up a notch. 

Packed with presentation templates that are easily customizable, keep reading this blog post to learn the secret sauce behind crafting presentations that captivate, inform and remain etched in the memory of your audience.

Click to jump ahead:

What is a visual presentation & why is it important?

15 effective tips to make your visual presentations more engaging, 6 major types of visual presentation you should know , what are some common mistakes to avoid in visual presentations, visual presentation faqs, 5 steps to create a visual presentation with venngage.

A visual presentation is a communication method that utilizes visual elements such as images, graphics, charts, slides and other visual aids to convey information, ideas or messages to an audience. 

Visual presentations aim to enhance comprehension engagement and the overall impact of the message through the strategic use of visuals. People remember what they see, making your point last longer in their heads. 

Without further ado, let’s jump right into some great visual presentation examples that would do a great job in keeping your audience interested and getting your point across.

In today’s fast-paced world, where information is constantly bombarding our senses, creating engaging visual presentations has never been more crucial. To help you design a presentation that’ll leave a lasting impression, I’ve compiled these examples of visual presentations that will elevate your game.

1. Use the rule of thirds for layout

Ever heard of the rule of thirds? It’s a presentation layout trick that can instantly up your slide game. Imagine dividing your slide into a 3×3 grid and then placing your text and visuals at the intersection points or along the lines. This simple tweak creates a balanced and seriously pleasing layout that’ll draw everyone’s eyes.

2. Get creative with visual metaphors

Got a complex idea to explain? Skip the jargon and use visual metaphors. Throw in images that symbolize your point – for example, using a road map to show your journey towards a goal or using metaphors to represent answer choices or progress indicators in an interactive quiz or poll.

3. Visualize your data with charts and graphs

The right data visualization tools not only make content more appealing but also aid comprehension and retention. Choosing the right visual presentation for your data is all about finding a good match. 

For ordinal data, where things have a clear order, consider using ordered bar charts or dot plots. When it comes to nominal data, where categories are on an equal footing, stick with the classics like bar charts, pie charts or simple frequency tables. And for interval-ratio data, where there’s a meaningful order, go for histograms, line graphs, scatterplots or box plots to help your data shine.

In an increasingly visual world, effective visual communication is a valuable skill for conveying messages. Here’s a guide on how to use visual communication to engage your audience while avoiding information overload.

visual representation of growth

4. Employ the power of contrast

Want your important stuff to pop? That’s where contrast comes in. Mix things up with contrasting colors, fonts or shapes. It’s like highlighting your key points with a neon marker – an instant attention grabber.

5. Tell a visual story

Structure your slides like a storybook and create a visual narrative by arranging your slides in a way that tells a story. Each slide should flow into the next, creating a visual narrative that keeps your audience hooked till the very end.

Icons and images are essential for adding visual appeal and clarity to your presentation. Venngage provides a vast library of icons and images, allowing you to choose visuals that resonate with your audience and complement your message. 

visual representation of growth

6. Show the “before and after” magic

Want to drive home the impact of your message or solution? Whip out the “before and after” technique. Show the current state (before) and the desired state (after) in a visual way. It’s like showing a makeover transformation, but for your ideas.

7. Add fun with visual quizzes and polls

To break the monotony and see if your audience is still with you, throw in some quick quizzes or polls. It’s like a mini-game break in your presentation — your audience gets involved and it makes your presentation way more dynamic and memorable.

8. End with a powerful visual punch

Your presentation closing should be a showstopper. Think a stunning clip art that wraps up your message with a visual bow, a killer quote that lingers in minds or a call to action that gets hearts racing.

visual representation of growth

9. Engage with storytelling through data

Use storytelling magic to bring your data to life. Don’t just throw numbers at your audience—explain what they mean, why they matter and add a bit of human touch. Turn those stats into relatable tales and watch your audience’s eyes light up with understanding.

visual representation of growth

10. Use visuals wisely

Your visuals are the secret sauce of a great presentation. Cherry-pick high-quality images, graphics, charts and videos that not only look good but also align with your message’s vibe. Each visual should have a purpose – they’re not just there for decoration. 

11. Utilize visual hierarchy

Employ design principles like contrast, alignment and proximity to make your key info stand out. Play around with fonts, colors and placement to make sure your audience can’t miss the important stuff.

12. Engage with multimedia

Static slides are so last year. Give your presentation some sizzle by tossing in multimedia elements. Think short video clips, animations, or a touch of sound when it makes sense, including an animated logo . But remember, these are sidekicks, not the main act, so use them smartly.

13. Interact with your audience

Turn your presentation into a two-way street. Start your presentation by encouraging your audience to join in with thought-provoking questions, quick polls or using interactive tools. Get them chatting and watch your presentation come alive.

visual representation of growth

When it comes to delivering a group presentation, it’s important to have everyone on the team on the same page. Venngage’s real-time collaboration tools enable you and your team to work together seamlessly, regardless of geographical locations. Collaborators can provide input, make edits and offer suggestions in real time. 

14. Incorporate stories and examples

Weave in relatable stories, personal anecdotes or real-life examples to illustrate your points. It’s like adding a dash of spice to your content – it becomes more memorable and relatable.

15. Nail that delivery

Don’t just stand there and recite facts like a robot — be a confident and engaging presenter. Lock eyes with your audience, mix up your tone and pace and use some gestures to drive your points home. Practice and brush up your presentation skills until you’ve got it down pat for a persuasive presentation that flows like a pro.

Venngage offers a wide selection of professionally designed presentation templates, each tailored for different purposes and styles. By choosing a template that aligns with your content and goals, you can create a visually cohesive and polished presentation that captivates your audience.

Looking for more presentation ideas ? Why not try using a presentation software that will take your presentations to the next level with a combination of user-friendly interfaces, stunning visuals, collaboration features and innovative functionalities that will take your presentations to the next level. 

Visual presentations come in various formats, each uniquely suited to convey information and engage audiences effectively. Here are six major types of visual presentations that you should be familiar with:

1. Slideshows or PowerPoint presentations

Slideshows are one of the most common forms of visual presentations. They typically consist of a series of slides containing text, images, charts, graphs and other visual elements. Slideshows are used for various purposes, including business presentations, educational lectures and conference talks.

visual representation of growth

2. Infographics

Infographics are visual representations of information, data or knowledge. They combine text, images and graphics to convey complex concepts or data in a concise and visually appealing manner. Infographics are often used in marketing, reporting and educational materials.

Don’t worry, they are also super easy to create thanks to Venngage’s fully customizable infographics templates that are professionally designed to bring your information to life. Be sure to try it out for your next visual presentation!

visual representation of growth

3. Video presentation

Videos are your dynamic storytellers. Whether it’s pre-recorded or happening in real-time, videos are the showstoppers. You can have interviews, demos, animations or even your own mini-documentary. Video presentations are highly engaging and can be shared in both in-person and virtual presentations .

4. Charts and graphs

Charts and graphs are visual representations of data that make it easier to understand and analyze numerical information. Common types include bar charts, line graphs, pie charts and scatterplots. They are commonly used in scientific research, business reports and academic presentations.

Effective data visualizations are crucial for simplifying complex information and Venngage has got you covered. Venngage’s tools enable you to create engaging charts, graphs,and infographics that enhance audience understanding and retention, leaving a lasting impression in your presentation.

visual representation of growth

5. Interactive presentations

Interactive presentations involve audience participation and engagement. These can include interactive polls, quizzes, games and multimedia elements that allow the audience to actively participate in the presentation. Interactive presentations are often used in workshops, training sessions and webinars.

Venngage’s interactive presentation tools enable you to create immersive experiences that leave a lasting impact and enhance audience retention. By incorporating features like clickable elements, quizzes and embedded multimedia, you can captivate your audience’s attention and encourage active participation.

6. Poster presentations

Poster presentations are the stars of the academic and research scene. They consist of a large poster that includes text, images and graphics to communicate research findings or project details and are usually used at conferences and exhibitions. For more poster ideas, browse through Venngage’s gallery of poster templates to inspire your next presentation.

visual representation of growth

Different visual presentations aside, different presentation methods also serve a unique purpose, tailored to specific objectives and audiences. Find out which type of presentation works best for the message you are sending across to better capture attention, maintain interest and leave a lasting impression. 

To make a good presentation , it’s crucial to be aware of common mistakes and how to avoid them. Without further ado, let’s explore some of these pitfalls along with valuable insights on how to sidestep them.

Overloading slides with text

Text heavy slides can be like trying to swallow a whole sandwich in one bite – overwhelming and unappetizing. Instead, opt for concise sentences and bullet points to keep your slides simple. Visuals can help convey your message in a more engaging way.

Using low-quality visuals

Grainy images and pixelated charts are the equivalent of a scratchy vinyl record at a DJ party. High-resolution visuals are your ticket to professionalism. Ensure that the images, charts and graphics you use are clear, relevant and sharp.

Choosing the right visuals for presentations is important. To find great visuals for your visual presentation, Browse Venngage’s extensive library of high-quality stock photos. These images can help you convey your message effectively, evoke emotions and create a visually pleasing narrative. 

Ignoring design consistency

Imagine a book with every chapter in a different font and color – it’s a visual mess. Consistency in fonts, colors and formatting throughout your presentation is key to a polished and professional look.

Reading directly from slides

Reading your slides word-for-word is like inviting your audience to a one-person audiobook session. Slides should complement your speech, not replace it. Use them as visual aids, offering key points and visuals to support your narrative.

Lack of visual hierarchy

Neglecting visual hierarchy is like trying to find Waldo in a crowd of clones. Use size, color and positioning to emphasize what’s most important. Guide your audience’s attention to key points so they don’t miss the forest for the trees.

Ignoring accessibility

Accessibility isn’t an option these days; it’s a must. Forgetting alt text for images, color contrast and closed captions for videos can exclude individuals with disabilities from understanding your presentation. 

Relying too heavily on animation

While animations can add pizzazz and draw attention, overdoing it can overshadow your message. Use animations sparingly and with purpose to enhance, not detract from your content.

Using jargon and complex language

Keep it simple. Use plain language and explain terms when needed. You want your message to resonate, not leave people scratching their heads.

Not testing interactive elements

Interactive elements can be the life of your whole presentation, but not testing them beforehand is like jumping into a pool without checking if there’s water. Ensure that all interactive features, from live polls to multimedia content, work seamlessly. A smooth experience keeps your audience engaged and avoids those awkward technical hiccups.

Presenting complex data and information in a clear and visually appealing way has never been easier with Venngage. Build professional-looking designs with our free visual chart slide templates for your next presentation.

What software or tools can I use to create visual presentations?

You can use various software and tools to create visual presentations, including Microsoft PowerPoint, Google Slides, Adobe Illustrator, Canva, Prezi and Venngage, among others.

What is the difference between a visual presentation and a written report?

The main difference between a visual presentation and a written report is the medium of communication. Visual presentations rely on visuals, such as slides, charts and images to convey information quickly, while written reports use text to provide detailed information in a linear format.

How do I effectively communicate data through visual presentations?

To effectively communicate data through visual presentations, simplify complex data into easily digestible charts and graphs, use clear labels and titles and ensure that your visuals support the key messages you want to convey.

Are there any accessibility considerations for visual presentations?

Accessibility considerations for visual presentations include providing alt text for images, ensuring good color contrast, using readable fonts and providing transcripts or captions for multimedia content to make the presentation inclusive.

Most design tools today make accessibility hard but Venngage’s Accessibility Design Tool comes with accessibility features baked in, including accessible-friendly and inclusive icons.

How do I choose the right visuals for my presentation?

Choose visuals that align with your content and message. Use charts for data, images for illustrating concepts, icons for emphasis and color to evoke emotions or convey themes.

What is the role of storytelling in visual presentations?

Storytelling plays a crucial role in visual presentations by providing a narrative structure that engages the audience, helps them relate to the content and makes the information more memorable.

How can I adapt my visual presentations for online or virtual audiences?

To adapt visual presentations for online or virtual audiences, focus on concise content, use engaging visuals, ensure clear audio, encourage audience interaction through chat or polls and rehearse for a smooth online delivery.

What is the role of data visualization in visual presentations?

Data visualization in visual presentations simplifies complex data by using charts, graphs and diagrams, making it easier for the audience to understand and interpret information.

How do I choose the right color scheme and fonts for my visual presentation?

Choose a color scheme that aligns with your content and brand and select fonts that are readable and appropriate for the message you want to convey.

How can I measure the effectiveness of my visual presentation?

Measure the effectiveness of your visual presentation by collecting feedback from the audience, tracking engagement metrics (e.g., click-through rates for online presentations) and evaluating whether the presentation achieved its intended objectives.

Ultimately, creating a memorable visual presentation isn’t just about throwing together pretty slides. It’s about mastering the art of making your message stick, captivating your audience and leaving a mark.

Lucky for you, Venngage simplifies the process of creating great presentations, empowering you to concentrate on delivering a compelling message. Follow the 5 simple steps below to make your entire presentation visually appealing and impactful:

1. Sign up and log In: Log in to your Venngage account or sign up for free and gain access to Venngage’s templates and design tools.

2. Choose a template: Browse through Venngage’s presentation template library and select one that best suits your presentation’s purpose and style. Venngage offers a variety of pre-designed templates for different types of visual presentations, including infographics, reports, posters and more.

3. Edit and customize your template: Replace the placeholder text, image and graphics with your own content and customize the colors, fonts and visual elements to align with your presentation’s theme or your organization’s branding.

4. Add visual elements: Venngage offers a wide range of visual elements, such as icons, illustrations, charts, graphs and images, that you can easily add to your presentation with the user-friendly drag-and-drop editor.

5. Save and export your presentation: Export your presentation in a format that suits your needs and then share it with your audience via email, social media or by embedding it on your website or blog .

So, as you gear up for your next presentation, whether it’s for business, education or pure creative expression, don’t forget to keep these visual presentation ideas in your back pocket.

Feel free to experiment and fine-tune your approach and let your passion and expertise shine through in your presentation. With practice, you’ll not only build presentations but also leave a lasting impact on your audience – one slide at a time.

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