What Is Data Visualization?

As humans, we’re wired to connect with stories. Data in its rawest form is anything but a story—if anything, it feels like the exact opposite.

But when you watch the numbers transform into visualizations? These tell you everything you need to know to understand your business story from start to finish.

In a lightweight BI tool like PowerMetrics, you can create data visualizations even if you’re not a data analyst. After you connect your data through a pre-built or custom connector, you can watch it transform. All you have to do is select the visualization type that effectively communicates the story or message that you want to bring to life with your data.

Here’s what I’ll cover in this article:

• What is data visualization?
• Why do we visualize data?
• What makes a good data visualization?
• How do I pick the right visualization for my dashboard?
• 12 commonly used data visualizations
• How to level up your data visualizations

With the help of this article, my hope is that you will be equipped with all of the tools you need to tell—and understand—your data story.

Let’s go!

What is data visualization?

A data visualization is a graphical representation of data or information—for example, taking raw numbers from a spreadsheet and transforming that into a bar or line chart. Using data visualizations helps you to easily understand and analyze trends and outcomes within your data.

Selecting the right graphical representation depends on the story you are trying to tell. You can choose any visualization and assume it’ll do the trick, but it’s worth the time and effort to select and customize the right visualization for long-lasting impact.

Where do you put data visualizations? Data visualizations are everywhere: on the evening news, embedded in tweets, or on the front page of the newspaper. In a business context, you often find data visualizations in reports, presentations, or on a business dashboard.

Business dashboards display data visualizations to help you:

• Keep a pulse on key metrics related to the health of your business
• Integrate your data sets and data sources in one place
• Provide an at-a-glance view of business performance for key decision-makers
• Get your team on the same page regarding performance and outcomes
• Intuitively and logically interpret data

Why do we visualize data?

We use data visualizations to help us tell stories. By nature, humans are visual beings—we’re drawn to colors, shapes, and patterns. So, using those basic human conditions to turn numbers into visuals is an incredible skill to have in your back pocket.

You’ve likely heard the old adage: a picture is worth a thousand words. But if I might suggest a slight edit: data visualization is worth a thousand words.

You should put data visualizations on a dashboard to:

• Boost engagement 
• Improve analysis
• Save time
• Tell a story
• Identify relationships
• Compare performance using numbers

When done well, data visualization will tell the story that is hidden within the numbers in your spreadsheet without words. If you’ve had an excellent quarter and your revenue is up, your line chart will be making a slow climb to the upper right. That’s an easier story to capture than sifting through the rows of an Excel file, right?

When you put your data visualizations on a dashboard, you have the tools and information you need to make and validate your business decisions in an accessible and easy-to-share format.

What makes a good data visualization?

We define good data visualizations as graphical representations that serve their intended purpose. If a user can interpret your data visualization by asking questions about the information displayed versus how or what is displayed, then you know you’re on the right path.

Now, you may be wondering, “How do I pick the right data visualization to ensure it serves its intended purpose?”

Let’s break it down.

How do I pick the right data visualization for my dashboard?

It’s not as easy as picking any data visualization to present your data and information. It’s really important to match your data to the right visualization so you and your users can get the most value. 

If you’re not sure where to start, ask yourself these five questions:

1. What relationship am I trying to understand between my data sets?
2. Do I want to understand the distribution of data and identify outliers?
3. Am I looking to compare multiple values or looking to analyze a single value over time?
4. Am I interested in analyzing trends in my data?
5. Is this data visualization an important part of my overarching story?

Answering these five questions will help you narrow down which category you should focus on and then drill down further to which type of data visualization within that category is the best fit.

Data visualization categories

There are five types of data visualization categories:

1. Temporal
2. Hierarchical
3. Network
4. Multidimensional
5. Geospatial

Temporal data visualizations

Temporal data visualizations are linear and one-dimensional and are most commonly used to represent a time series.

It’s common in newspapers to show information like housing market fluctuations quarter over quarter or in company reports to visualize gains and losses.

The advantage of using temporal data visualization is that we have a predisposed understanding of how and when to interpret them, so it gives your users an edge when they look at the data.

Examples of temporal data visualizations include:

• Bar chart
• Line chart
• Scatter plots
• Polar area diagrams
• Time series sequences
• Timelines
• Gantt chart

Hierarchical data visualizations

Hierarchical data visualizations order a collection of items that link back to a parent item. These are best used to display a cluster of information, especially if it flows from a single origin point (like a tree diagram).

Examples of hierarchical data visualizations include:

• Treemap or diagram
• Ring charts
• Sunburst diagrams

Network data visualizations

Network data visualizations show relationships between entities—nodes (the circles on the visualization) and links (the lines that connect to the nodes)—without using words.

Examples of network data visualizations include:

• Matrix chart
• Node-link diagrams
• Word clouds
• Alluvial diagrams

Multidimensional data visualizations

If you’re looking to drill down and filter your data, multidimensional data visualizations are the best type of visual to use because you can break down your data in a number of ways to capture the key takeaways.

Examples of multidimensional data visualizations include:

• Scatter plots
• Pie charts
• Venn diagrams
• Stacked bar graphs
• Histograms

Geospatial data visualizations

One of the earliest forms of data visualization, geospatial (or spatial) visualizations overlay familiar maps with data points. Geospatial data visualizations have a long history, too, as they were used for navigation before computational analysis came along.

Examples of geospatial data visualizations include:

• Flow map
• Density map
• Cartogram
• Heat map

Commonly used data visualization layouts

In a business context, it’s important to choose the data visualization that will help you extract the most value from information displayed on a dashboard. It can be hard to know which data visualization is best for your data set, your dashboard, and your users, but my hope is that this guide will help you do just that.

Once you pick a data visualization, it’s important to consider how it fits into your overall dashboard design, too. We have all the tips and tricks to help you design an incredible dashboard in our guide to dashboard design.

In a lightweight modern BI tool like PowerMetrics, there are a number of data visualizations to choose from, including:

• Bar or column chart
• Line or area chart
• Pie or donut chart
• Scatter chart
• Bubble chart
• Combination chart

Let’s take an in-depth look at each data visualization type.

Bar or column chart

Most commonly used to compare related data sets, bar charts organize data into rectangular bars proportional to the value it represents.

The x-axis of a bar chart shows the categories that are being compared, and the y-axis represents the value. 

For example, let’s say you want to visualize real-estate market data: you could plot types of homes being sold (townhouse, condo, or detached) on the x-axis and the dollar value that the home sold for on the y-axis to help you understand the type of homes available within an allocated budget.

Use a bar chart to:

• Compare two or more values in the same category
• Compare parts of a whole
• Compare less than 10 groups of related data

Bar charts aren’t suitable for visualizing a category with only one value or visualizing continuous data. Use consistent colors and labels to easily identify relationships in the data. 

Also, simplify the length of your y-axis and start from 0 so your data is orderly.

Line chart or area chart

Much like bar charts, line charts are a popular way to visualize data in a compact and precise format. Data points are represented by dots that are then connected by straight line segments.

Line charts visualize your data relative to a continuous variable, usually something like time or money, and the data points are ordered by their x-axis value.

It’s important to consider color in a line chart. Different colored lines make it easier to interpret the information being presented. You can read more about the proper use of color in our dashboard design guide.

Use a line chart to:

• Understand trends, patterns, and fluctuations in data
• Compare different but related data sets with multiple series
• Make projections beyond your data

Line charts aren’t the best data visualization if you want to demonstrate an in-depth view of your data. Use a different color for each category you’re comparing, and use solid lines to keep the chart clear and concise.

And remember: avoid comparing more than four categories (it can become confusing with too many!)

Pie or donut chart

Appropriately named, a pie or donut chart is a data visualization tool that displays information in slices within a circular graphic. Each slice of the pie represents a segment of your data. Pie charts are visualized in a full circle, whereas a donut chart looks like a donut — it has a hollow center!

Pie charts differ in appearance from bar charts but serve the same purpose since they compare values in the same data visualization category. Pie charts are easy to read because the parts-of-a-whole relationship is obvious at first glance. 

There is a disadvantage to pie charts, though; the percentage of each section isn’t obvious without adding numerical values to each slice. Keep this in mind if you’re putting a pie chart on a dashboard that is commonly used for quick analysis.

Pie charts are still a quick way to scan and gather insights as long as you follow the best practices outlined below.

Use a pie or donut chart to:

• Compare relative values
• Compare parts of a whole
• Rapidly scan your data (keeping in mind that the numerical values have to be added to each slice for deeper analysis)

Don’t use a pie chart for precise comparisons of data, not because it’s not capable of it, but because there are better data visualizations to choose from if precision is a requirement. Order the pieces of your pie according to size.

Make sure the slices of your pie (or donut) equal 100%. To make this easier, add numerical values and percentages to your data visualization to help you and your readers

Also, don’t compare more than five categories in a pie chart. Otherwise, you run the risk of unclear differentiation between slices

Scatter chart

Do you have a lot of different data points that you want to visualize in the same set? Scatter charts are a great way to do this.

Like a radar chart, scatter charts are helpful data visualization tools for identifying outliers or understanding the distribution of data.

If the values on your chart form a band that extends from the lower left to the upper right, this usually indicates a positive correlation between variables. Alternatively, if the band extends from upper left to lower right, it indicates a negative correlation. No pattern? No correlation!

Use a scatter chart to:

• Show a relationship between two variables
• Display a compact data visualization

Scatter charts aren’t appropriate if you want to scan your dashboard for information quickly or if you need precise data points. Start at 0 for the y-axis so your data is orderly. 

Trend lines are an excellent way to analyze the data on a scatter chart, but we recommend you stick to 1 or 2 trend lines to avoid any confusion

Bubble chart

Most charts have a value axis and a date or category axis. Where a bubble chart differs as a data visualization tool is that it intersects three values—on the x-axis, on the y-axis, and the third displays as the bubble size (the bigger the value, the bigger the bubble).

For example, you could plot the data for the North American real estate market and the European real estate market to compare average house price (y-axis) versus average income (x-axis) versus house category—detached, townhome, condo (bubble size). 

A bubble chart enables you to compare three values about a particular geographic area against another.

Like the scatter chart, bubble charts allow you to identify outliers and correlations in your data set quickly.

Use a bubble chart to:

• Show a relationship between three variables

• Display a compact data visualization

• Call out outliers and correlations in your data

Remember to use a smaller set of data—the more values, the more bubbles that can be meant for misinterpretation.

Combination chart

Aptly named, combination charts compare two data sets over the same dimension (date or country, for example). Combination charts display a set of values as columns on the left axis and a set of values as a line on the right axis, appropriately combining both into a combination bar/line chart.

Combination charts are unique from other data visualizations because you can compare two data sets that have different numeric scales or formats—like comparing dollar values and percentages in the same data visualization.

Use a combination chart to:

• Compare dissimilar data sets in the same visualization (i.e., comparing currency and percentage)

How to level up your data visualizations

When it comes to dashboard and data visualization design, always seek input from the people who will be using it. Develop iteratively. And know that you probably won’t get it right the first time. 

Make sure you invest time to ask for feedback and understand what’s working and what needs improvement. Data visualization is a skill, and like any other, it has to be practiced.

The best piece of advice I can give? Have fun with your data visualizations. If you’re looking to level up your skills outside of the business context, create a data visualization for your fantasy hockey or football league, the stock market, or even your favorite film genre. 

Be creative, explore, and push the limits. That’s what data visualization is all about.