Your complete guide
to bar charts

Learn how bar charts, a graph that plots values as bars
and compares several categories of information, can
help you make more informed business decisions.

When interpreted correctly, data can tell a story that informs future decisions. But when you’re dealing with a lot of data, it can be challenging to see patterns emerge 一 especially if you’re just looking at raw data. That’s where visualizations come into play, helping us see the bigger picture. While there are many types of graphs, we’re honing in on one in particular: the bar chart. This piece will explain what bar charts are, when to use them, and what types of bar charts work best for the types of data you’re representing.

What is a bar chart?

A bar chart is a graph that plots values as rectangular bars and compares several categories of information. For example, you might use a bar chart to show the difference in spending for each corporate budget category.

One axis of a bar chart lists category names. In the corporate budget example, your x-axis might say “G&A,” “S&M,” “Human Resources,” “Utilities,” etc. The other axis of a bar chart denotes the value of each category; the taller or longer the bar, the higher the value. In a business budget comparison bar chart, the y-axis would measure value in dollars.

In other cases, values might be represented as percentages or years. You can also decide to plot bars vertically or horizontally 一 more on why you would choose one way over the other later. While the budget example is relatively straightforward, bar charts can get more complex. If you’re examining financial data, you might stack bars on top of each other or group them to uncover patterns within your datasets.

When to use a bar chart

Bar charts are best for displaying differences between groups, like electricity consumption across different countries or revenue by department. To illustrate this a bit more, let’s say you’re thinking of launching a new feature, and send customers a survey with several potential options. While it’s helpful to know which new feature gets chosen most frequently, that may not give you a full sense of customer opinion.

A bar chart could help you visualize how wide the differences are in customer preferences. And if you segment by various groups of your customer base, other patterns might appear, giving extra color and context you wouldn’t have had if you focused on the most popular answer.

In this example, you’re examining customer perception at one point in time: when customers took your survey. But bar charts can also show change over time. Say you sent the same survey a year or two later. Stacking new survey results over old ones may expose key differences based on recent trends in the market or current events.

How to interpret a bar chart

Interpreting a bar chart starts by understanding what’s displayed on the graph itself. When you first look at a bar chart, it’s important to familiarize yourself with the categories and values on each axis. Next, look at the length of each bar.

What does the gap between the bar of one category and the bar of another tell you? Is it simply that one category has a higher value than the other, or could there be another, more nuanced meaning? For example, if you’re comparing the sales performance of various reps, maybe one joined the firm late into the quarter. She might have a lower bar, but she also had less time to prove her value.

If there are multiple bars, take a moment to determine whether they represent two groups or two periods of time. For example, a bar chart may compare album sales by band among Millennials versus Gen X listeners, where Millennial bars are one color and Gen X bars are another. Or, a bar chart could examine ticket sales by city for the same show this year versus last year.

Types of bar charts

There are many types of bar charts; choosing the right one depends on what kind of data you’re working with, what questions you want to answer, and what your audience would find most insightful.

Horizontal bar charts

A horizontal bar chart is what it sounds like, a bar chart with bars on the horizontal axis. In horizontal bar charts, categories are listed on the y-axis, and corresponding values are represented on the x-axis. Because of its orientation, horizontal bar charts give you a bit more room if you have longer category labels. If you use a typical vertical bar chart, the long labels could overlap, forcing people to rotate the chart to read it.

Vertical bar charts

A vertical bar chart is what most people think of when they hear “bar chart.” As you might expect, bars stand vertically, with categories on the x-axis and values on the y-axis. Vertical bar charts are best for short, easy-to-read categories. And since vertical bar charts are the type of bar chart people are most familiar with, they serve as a good default in most circumstances.

Segmented bar chart

A segmented bar chart is a type of stacked bar chart in which each bar represents the full discrete value. Within each bar, there may be two or more colors within each bar to indicate different groups. Segmented bar charts are best when you’re trying to show the breakdown of a certain category as part of the whole.

So, for example, say you’re interested in knowing customer communication preferences broken down by new and old customers. The categories on a segmented bar chart would be communication preference, such as text, email, and phone. Each category’s bar would represent 100% but would be split depending on the percentage of new and old customers that prefer each communication style. Additionally, new and old customers would be denoted by color for clarity.

Grouped bar chart

A grouped bar chart, sometimes called a clustered bar chart, is best used to compare two or more data sets in terms of multiple categories. Grouped bar charts can be displayed vertically or horizontally and look like a small cluster of bars (each with a specific color) within each category. A good use case for a grouped bar chart would be comparing sales performance quarter over quarter by sales representative. In this case, the categories would be each quarter of the year, each bar would represent a different sales rep (with a different color distinguishing each one) and the length of each bar would represent the annual recurring revenue (ARR) each rep brought in.

Lollipop chart

A lollipop chart shows the same information as a regular bar chart, but uses lines instead of bars, each with a dot at the end. The best time to use a lollipop chart is when you have a lot of categories that don’t have a significant spread in value. Changing the bars to lines with more distinct endpoints makes the bar chart easier to read and interpret.

Stacked bar chart

A stacked bar chart is ideal for showing sub-category influence on larger categories. Each bar in a stacked bar chart has different colored sub-bars stacked on top of each other to represent their contribution to the whole category. For example, you might use a stacked bar chart to analyze each product line’s impact on overall sales year-over-year. The categories on this stacked bar chart would be each year, the length of each bar would be the total overall sales, and the sub-bars within each bar would signify each product line. The stacked bar chart differs from a segmented bar chart in that the bars are not all the same length (e.g. each year might’ve had a different overall sales number).

Bar charts and related data visualizations

Bar charts have some very close cousins, including histograms, line charts, and pie charts. Let’s explore how each of those relates to each other.


Histograms are similar to bar charts but depict numeric, continuous frequency values. Bars in a histogram are placed right next to each other to emphasize this distribution, whereas bar charts have space between them to highlight the differences between each category.

Line chart

A line chart’s primary variable is also numeric and continuous but shows the connection between data points in one line instead of bars. Line graphs are better for exposing less significant changes over time, whereas bar charts are useful for comparing differences between groups.

Pie chart

Like a segmented bar chart, pie charts show how much each group or category makes up a whole, but use a full circle to represent 100% instead of bars. Therefore, the size of each slice of the pie represents its value. Although pie charts can be useful, bar charts are generally an easier way to shine a light on the differences between each category.

Three tips for using bar charts

You’re often creating a bar chart to prove a point or better understand a situation. So why not make it easy for you and your peers to interpret it? Below, we outline several best practices for ensuring your message gets across.


Stick to clear shapes

Some visualization tools like to get fancy with bar charts, rounding the tips of each bar or giving bars a 3D effect. While this may look aesthetically pleasing, these modifications make the bar charts much tougher for the average person to read. Defined shapes, on the other hand, allow readers to immediately discern each bar’s true value, making for quicker, easier comparisons.


Use color effectively

Color can impact viewers' understanding and interpretation of your bar chart. That’s why it’s critical to ensure that any colors you use have a purpose and meaning behind them. If you’re using colors to symbolize disparate groups, use contrasting colors rather than different shades of the same color to make the difference clear.


Organize your data

The order in which you plot your bars can make a big difference in how people understand your bar chart. There's no required order for a bar graph, so the person making the visualization chooses how things are displayed. It can be helpful to sort values based on what should warrant the most attention. Usually, that’s the highest or lowest value. Putting the bars in order makes comparisons more obvious to the reader.

What's next?

In theory, bar charts are an excellent way to draw comparisons between all different categories of data in your organization. But in practice, creating them isn’t always so easy. Counterintuitive analytics tools can cause employees to spend more time trying to collect and transform data than actually interpreting it.

ThoughtSpot empowers every employee to operationalize data-driven insights. With built-in AI and search capabilities, ThoughtSpot delivers truly self-service Live Analytics that make it possible for anyone to create instant, personalized, and actionable insights at the point of impact. To learn more about how ThoughtSpot can enable your team, sign up for a free 30-day trial today.

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