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Understanding The Value Of Column Charts With Examples & Templates 

Column charts blog post by datapine

Data visualization has been a part of our lives for many many years now. Even if you don’t work with data in your day-to-day job, you have most likely encountered graphs and charts at some point in your life. This is because visual representations of data are everywhere, in the news, politics, the media, and advertising, among other areas. For that reason, it is important to know how to interpret graphs and charts and understand their uses in various contexts. 

To help you in that task, at datapine, we are putting together a series of blog posts that offer an in-depth look into different types of graphs and charts, teaching you when to use them through interactive examples. We already dived into professional gauge charts and bar charts, now it's time to explore the power of column graphs. 

Here, you’ll learn the definition, advantages and disadvantages, scenarios on when to use them, types and variations, and a list of column chart examples in a business context. Let’s get started with the definition! 

What Is A Column Chart or Graph?

A column chart or graph is a visual representation of categorical data that uses vertical columns to show comparisons and trends. It is often used interchangeably with bar charts, but they differ in that one has horizontal bars and the other one has vertical columns. 

Accounts payable turnover ratio as a column chart example

If you are aware of any type of graph then it is probably a column graph. These visuals are widely used in multiple contexts to make data more understandable for any type of audience. Making them the perfect tool to use in the news and the media. In fact, during COVID-19, we saw thousands and thousands of articles showing different types of bar and column graphs tracking the number of deaths or the rise in cases by months or weeks. These visuals, sometimes more misleading than others, helped in keeping the public informed about the latest developments of the pandemic. 

A similar situation happens when using column charts in a business context. These visuals are great tools to display trends and comparisons that enable businesses to extract insights quickly from their data. Insights that are later translated into better strategic decisions. Plus, their clean design of vertical bars makes the information very easy to understand with just a glance and with no need for deep analytical knowledge. 

As we mentioned before, column graphs are often used interchangeably or considered a variation of bar charts. While this is not wrong, for the purpose of this post, we will consider them as two separate types of visuals as their uses and design choices vary depending on the context. 

Disadvantages & Advantages Of Column Charts

Just like any type of chart, columns have advantages and disadvantages to them. Being able to identify each of them will help you get started with the right feet when it comes to choosing the right visual for your analytical aims. So, let’s look at some pros and cons of column graphs. Starting off with advantages.  

  • They are easy to understand: Column graphs are one of the easiest visualizations to understand. This is because you only need to look at the height of the columns to get a sense of what the data is telling you and you don’t need any technical knowledge to do that. Plus, its visual simplicity enables users to immediately spot if the data is not correct, making them one of the most accurate visuals out there. This is one of its greatest advantages and the reason why it is so popular in the analytical world. 
  • They are diverse: Although very simple in nature, column charts can serve a number of purposes. In a business context, they are great to compare the performance of different areas and scenarios and extract valuable conclusions. For example, comparing employee performance against a target or against other employees.  They are also highly valuable to summarize large data sets into a comprehensible visualization and identify key trends and patterns.

Now, let’s discuss some disadvantages: 

  • Not enough context: As mentioned before, column charts are fairly simple visuals that provide insights into categorical data based on a specific variable. This makes it harder to extract any deeper conclusions besides what you can see in the chart. That said, this doesn’t mean they are not valuable, a good use of labels, legends, and titles can provide enough context to support important decisions. Plus, when placed together with other charts in a professional business dashboard you’ll get a complete data story that you can explore and navigate to answer many analytical questions. 
  • Can be used to mislead: While we want to think that every person that uses data visualization does it in an ethical manner, this is not always the case, especially in the media and politics. Column graphs can be easily manipulated to make it seem like the data is showing a different conclusion than what it is supposed to. This is easily done by starting the scales at a value that is not 0, distorting the actual sizes of the columns. While this is a bad practice that happens a lot and on purpose, it can also happen by mistake if you don’t know how to correctly plot the data in a column chart. We will discuss this point in more detail later in the post. 
Your Chance: Want to test modern data visualization software for free? Try our 14-day trial & start building professional column charts today!

When To Use A Column Chart (And When Not To)

There is a wide range of contexts and areas in which column charts can be useful. As we already mentioned a couple of times already, the better use-case for this chart type is when you want to display data in an easy and comprehensible way. However, not all data will be valuable for a column chart which leads to the second most common use-case of these charts: comparisons. 

Column charts are the perfect tools to compare categorical data. For example, in a business context, you can use one to compare sales by product category, specific products, or even sales representatives. The only requirement is that you need to have categories that can be compared with each other to extract conclusions. 

On that same note, these charts are also highly valuable to monitor changes over time. For example, sales by month or by year. This enables users to compare the different time periods and find trends and patterns that can be valuable for their decision-making process. In some cases, you can also add a benchmark or target to each column and compare them based on that value, providing deeper insights. 

Now, you might be wondering, when should I not use a column chart? The answer is fairly easy. They should be avoided when you are dealing with multiple categories. The vertical orientation of these visuals means they can fit fewer categories before they look overcrowded. So it is recommended to choose a bar chart instead as the horizontal orientations mean more bars can fit into the chart. Additionally, column graphs should also be avoided if you are dealing with data that is not categorical or if it's in rates. 

Types Of Column Charts

Now that we’ve explored the advantages and disadvantages as well as the uses of column charts, let’s dive into the different types of column charts. Here, we will look at a definition of each type and present an example to put their value into perspective. 

1. Traditional column chart

There is not much more we can say that we haven’t mentioned about this chart type already. The “traditional” column chart is one that uses vertical columns or bars to display values in various categories. Which are usually displayed in the horizontal axes with values on the vertical one. The length of the columns is proportional to the values that each represents which makes it possible to understand the data at-a-glance.  

The average time to fill as an example of a column chart with a target

The image above shows an example of a column chart tracking the overtime hours of employees by age group. Just by looking at the length of the columns, we can conclude that younger employees are more open to working overtime than older ones. 

2. Grouped column chart

Also known as a clustered column chart, this type of visual uses the same logic of the traditional column chart, but instead of having one categorical variable, it has two. This enables users to extract deeper conclusions about their categorical data as variables can be compared within a specific group or with other groups on the charts. Each category is represented by a specific color to make it easier to identify within each group. Let’s put it into perspective with the example below. 

Grouped column chart example tracking the amount of sales per channel and country

**click to enlarge**

Our example is tracking the number of sales per channel and country. A business could analyze the chart to see where to focus its promotional efforts in each of its target countries. For instance, the USA is the second-highest country in sales for SEM and SEO, but the second-lowest in coupon sales. Based on that information, the business can take action and decide if they want to dedicate the budget of coupons to boost sales through SEM and SEO even more or if they want to put more resources into increasing sales by coupons in the USA.

3. Stacked column chart

A stacked column chart is similar to the grouped one but instead of displaying the categories side by side, they are stacked on top of each other completing a total value. This total can be in a percentage of 100% or just the total of a specific value such as total sales by product category, total sales by a quarter of a specific year, or others. Therefore, you should always use this type of column graph when you are trying to compare parts to a whole. 

Stacked column chart template tracking the age of new customers per quarter

**click to enlarge**

The example above shows a stacked column displaying the age of new customers per quarter. Each column represents the total of customers by the quarter and the subgroups represent the percentages of each age range. This information enables you to look at how the different age groups increased or decreased during each quarter and extract valuable conclusions. For instance, we can see that ages over 56 decreased from 16% to 9% in Q4. Is this because you started focusing your efforts on younger customers or because something happened that needs to be addressed? 

4. Column line chart

More than a type of column graph itself, the column line chart is a variation that is made by mixing a column and a line chart together to generate an advanced visual that helps users extract deeper insights. 

A column line chart that combines a line chart with a column chart to represent the number of orders and revenue

**click to enlarge**

In our example above, we see a column graph combined with a line graph to show the number of orders and revenue by month over a year. In this case, each graph type has a y-axis: the number of orders for the column chart and the revenue for the line. Combined, these visuals make it possible to extract valuable conclusions regarding the performance of each month and how it can be optimized to achieve better results. 

Your Chance: Want to test modern data visualization software for free? Try our 14-day trial & start building professional column charts today!

Tips To Make Professional Column Graphs

When it comes to generating a successful column graph, you can follow a couple of best practices to avoid making mistakes that will damage your analysis. These tips relate to the order of categories, manipulation of values, and the overall design of the graphic. Let’s look at them in more detail below. 

  • Sort categories smartly

Organizing the categories in a way that makes sense can significantly enhance the way column graphs are perceived. Usually, the order that makes more sense is from smaller to largest or largest to smallest, as this allows the audience to immediately extract conclusions from the heights of the columns without the effort of looking at each value. That said, this is not always possible as sometimes values must be organized by a time frame or age group, for example. Therefore, it is important to think about this order in advance and go with something that makes sense for the analytical purpose as well as the type of data. 

  • The y-axis should always start at 0

While this might sound like an obvious point, it is a common practice in the visualization world, in some cases by mistake and in others as a tactic to mislead audiences. We are talking about a practice known as “truncating an axis”. It happens when the Y axis is plotted to start at a different value than zero, making the difference within the columns look different than what they actually should. 

Same Data, Different Y-Axis Data Visualization Designed to Mislead

Source: Gizmodo

The example above is an image we used in our misleading data visualization blog post and it depicts this issue perfectly. In the first image, the Y axis starts at 3.140% and finishes at 3.154% making it seem like the interest rate from 2008 to 2012 has grown exponentially. However, when the axis is plotted correctly, starting with zero and with bigger numbers, we can see that the difference between the tax rates is actually so small that it might be irrelevant. 

Cases like this one are flooding the media where publications and politicians make misleading graphs to spread false conclusions that support their own interests. To avoid being misled by these bad practices, it is of utmost importance to have some education on the topic of data visualization. At datapine, we put together an insightful post on the top data visualization books that can help you dive into the topic in a friendly manner. 

  • Placement of values

The placement of values on the Y-axis is another aspect that you can consider to make your graph even more visually appealing and easier to analyze. If your analysis goal is to focus on the actual values then you can eliminate the vertical axis and place the values inside or on top of the columns, as seen in the example below. 

A column chart example showing the operating profit margin and its development over time

That said, if your goal is to show trends within the data, then we suggest you keep the traditional display of the horizontal axis. If you are displaying this chart in a professional online dashboard, then you can allow users to see the exact values when they hover over a specific column. 

  • Overall design

Our previous point was already design-focus, but here we will talk about purely visual choices that, if not done correctly, can damage your charts as much as the previous tips we already discussed. 

Starting by colors, while it might sound very tempting to use a specific color for each column this is not a good idea. Too many colors can confuse the audience and make the chart look a lot heavier than it needs to be. We suggest you keep your columns in a specific color range unless you are using a stacked or grouped column graph and need to represent different categories with different colors. But, even then, it is important to keep in mind your audience and the purpose of the chart. If you are using it in a business setting, consider colors that align with the brand image so that it will look familiar to the audience. 

The width of the columns is another important point to consider. Since simplicity is key when it comes to these visuals, you should try to avoid using columns that are too wide and will overcrowd the chart. Likewise, the spaces between columns should also be big enough so that the columns are distinguishable but not so big that it will mess up the design. 

On that same note, going too crazy about the shape of the columns is also something that you should avoid. Using 3D effects, rounding the columns, or adding shades will only make the graph harder to understand. So, keeping it simple should always be at the top of the list. This last point is one of the most important data visualization techniques and it should be applied to any type of chart.  

Your Chance: Want to test modern data visualization software for free? Try our 14-day trial & start building professional column charts today!

Column Chart Examples & Templates In Business

As we’ve already mentioned a couple of times already, column charts are widely used visuals that can work in a number of areas and industries. Tu put their value into perspective, we will now go through 5 column chart examples for various business areas and departments. 

These examples were generated with datapine’s column chart maker and show different variations of this chart type as well as how it can be complemented with additional features such as combining them with other graphs, adding benchmarks and targets, and more. Let’s start with finances. 

1. Finances

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

This stacked column chart template for financial analytics is displaying the cash conversion cycle (CCC) of a business. It measures how long it takes for a company to convert its inventory investments and other resources into cash flows from sales with the following formula: CCC = DIO (days of inventory outstanding) + DSO (days sales outstanding) – DPO (days payable outstanding)

In this case, the stacked columns are represented by each of the elements contained in the formula and complemented with a trend line that represents the CCC of each year. This is a great example of how a column and line chart can be used together to extract deeper insights. 

2. Sales

The revenue & profit per product as an example of a column graph for the sales department

This next example of a column chart is tracking one of the most important sales KPIs: the revenue & profit per product. In this case, we are using a grouped column chart to differentiate the revenue from the profit for each product which allows users to extract conclusions on itself. Paired with that, the chart includes a percentage in a small blue box that represents the profit margin of each product which tells you quickly how profitable each product is compared to its costs. 

3. Human Resources

The average time to fill as an example of a column chart with a target

Next in our list of column graph examples, we have a more traditional column showing the average time to fill by the department. This is a fairly simple example compared to the previous two but it provides insights that are just as powerful. 

Each of the columns in the template is complemented with a target. This target can be based on the previous period or on other criteria imposed by the business and it allows the HR department to optimize their recruitment processes if an area is way far from the target. For example, we can see that most departments are close to the target, however, sales are the one that is the furthest. This is something that needs to be looked into in more detail. 

4. Customer Service

A stacked column chart for the customer service department tracking the average number of issues per day by means of communication

Customer service analytics is another area that can benefit from this type of visual. The stacked column graph template above is tracking the number of issues per day by means of communication. A stacked column is the perfect choice to display this data as you need to look at parts of a whole which are the number of issues by a channel from the total of daily issues. 

What makes this visual so valuable is the fact that it provides you with the scale at the horizontal axis but also the specific total values at the top. Making it easier to compare the totals of the different columns. 

5. Procurement

Traditional column chart tracking the procurement ROI per supplier category complemented with a benchmark line

Last but not least, the procurement ROI is another KPI that can highly benefit from a column chart. In this case, the procurement ROI is depicted by supplier category and compared against a benchmark line. Just like the example above, the exact values are placed at the top of each column to complement the information provided by the horizontal axis. 

With this data in hand, we can see that most supplier categories are not reaching the expected benchmark value, meaning the overall return on investment from procurement activities might be affected as a whole. This is something that needs to be analyzed with the help of other visuals in an interactive procurement dashboard

Your Chance: Want to test modern data visualization software for free? Try our 14-day trial & start building professional column charts today!

Key Takeaways Column Charts 

As we reach the end of this insightful guide on column graphs we expect you were able to grasp the value of these data visualizations and how they can boost your analytical efforts in a number of ways. Here, we explored what are column charts used for, their pros and cons, and different types and examples to put their power into perspective. 

If used correctly, these visuals can significantly boost your analytical efforts and support your decision-making process to ensure continuous improvement and growth for your organization. If put together in a professional KPI dashboard with other critical KPIs, column charts can become part of a complete analytical story that will empower everyone in the business to use data for their strategic and operational decisions. 

If you are ready to start generating stunning visuals to represent your most important KPIs, then try our professional online data visualization software for a 14-day free trial today!