Top 10 Data Visualization Types: How To Choose The Right One?

Data visualization is one of the essential things, and there is no doubt in it. Every day, there are billions of data being created, shared, and analyzed, and as a company, you have a lot of data to handle. Learn more about the basics of data visualization.

And to handle your big data, you will need to use data visualization types to visualize your data. So what are some of the top types of data visualization types are available out there?

Well, in this article, I am going to answer this question only. So let’s just head into the topic without wasting much of the time:

What is Data Visualization?

Data visualization simply refers to techniques that are used to communicate with insights from data through a visual representation. To simplify the thing, you can say that data visualization visually puts data. So we can easily understand it. One has to master different data visualization tools to become effective.

The main goal of data visualization is to put large datasets into visual graphics. And it is one of the important steps when it comes to data science. Also, it is a simple way to track different data points.

No matter if you wish to track website metrics, sales team performance, marketing campaign, product adoption rate, or any other thing. Data visualization is what will help you out. To learn more about data visualization and other visual representation in data science, check out our data science certifications from recognized universities.  

10 Types of Data Visualization To Look For

1. Column Chart

A column chart is one of the common types of data visualization tools that you can try out. As you already know, we have been taught how to make column charts in elementary school. As they are simple to understand, less time consuming, and it shows the comparison among different sets of data. And you can use a column chart to track data sets over time.

A column chart usually includes data labels along with the horizontal (X) axis with measured metrics or values presented on the vertical (Y) axis, also known as the left side of the chart. The Y-axis usually starts at 0 and goes as high as the largest measurement that you are tracking.

This data visualization type can be used for tracking monthly sales figures, revenue per landing page, similar measurements, and so on.

2. Line Graph

Next, there is the Line graph. And it is one of the popular standard chart types which are instantly recognizable. A line graph is usually used for revealing trends, progress, or changes that occur over time.

And it works the best when your data is continuous rather than full of starts and stops. Also, just like a column chart, data labels are placed on a line graph are on the X-axis while measurements are on the Y-axis. However, you should use solid lines to avoid plotting more than four lines.

3. Bar Graph

You can also try using a bar graph. The bar graph and a column chart get used in the same way. However, column charts limit your label and comparison space. Hence, it is always a good idea to use a bar graph.

You should use a bar graph while you are working with a lengthier label, displaying negative numbers, or comparing 10 or more than 10 items. In these cases, your data label will go along the Y-axis while the measurements are along the X-axis. 

Also, a bar graph makes it easy for us to compare sets of data between different groups at a glance. The graph simply represents the categories on one axis and then creates a discrete value in the other. The goal over here is to show the relationship between two axes as well as the bar charts can also show significant changes in data over time.

Moreover, it is an effective way to compare items between different groups. The bar graph shows a comparison of numbers on a quarterly basis over four years.

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4. Stacked Bar Graph

In case if you are comparing many different items, then the stacked bar graph is the best option for you. It works the best way when you wish to track the individual growth of each data set. As well as the whole group’s growth too.

A stacked bar graph is a chart that uses bars to show a comparison between categories of data. But it also offers you the ability to break down and compare parts of a whole. Each bar in the chart represents a whole, and segments in the bar represent different parts or categories of the whole.

5. Dual-Axis Chart

Mostly, data visualization charts use a single y-axis or x-axis. But a dual-axis chart or multiple axes chart uses two axes to quickly illustrate the relationship between two variables with different magnitudes and scales of measurements.

And you should use a dual-axis chart when you are combining multiple charts and adding a second y-axis for comparison. With this type, you will easily be able to see two variables with very different scales. Also, it is much easier to see them within the same graph than to flip between two charts.

6. Pie Chart

There is no doubt that the pie chart is one of the most common data visualization types. And almost all of us have heard of it. A pie chart usually represents one static number, and it is divided into categories that constitute its portions.

Also, when you use a pie chart, you will usually represent numerical amounts in percentages. And there are quite a lot of cases where you can use a pie chart. Like in market share, marketing expenditure, customer demographics, customer device usage, online traffic sources, and so on.

Also, you want your pie chart to have multiple differentiation between slides. Hence it is a good idea to limit the number of categories you wish to visualize.

You can use a pie chart when you have categorical data. As in this case, each pie chart slice can represent a different category. Also, you can use it to compare areas of growth within a business, such as turnover, profit, and exposure.

7. Mekko Chart

Mekko chart is also one of the uncommon data visualization types that you hardly be familiar with. You might only know about it if you belong to the data analysis industry. A mekko chart comes with a similar layout as a stacked bar graph.

However, it has one major exception. Instead of tracking time progress, the X-axis measures another dimension of your data sets. And with the help of the Mekko Chart, you will easily be able to compare values, measure, and composition of each value. Also, you will be able to analyze data distribution all at the same time.

Overall, Mekko Chart is a great way to answer a variety of market overview questions. For example, if you want to understand who has the most valuable franchises, then the mekko chart is one of the best ways to show it.

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8. Scatter Plot

A scatter plot or scatter diagram or scatter graph usually uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis simply indicates the value for individual data points. And scatter plots are mainly used for observing relationships between different variables.

The dots in a scatter plot simply represent the values of individual data points. As well as it represents the patterns when the data are taken as a whole. Also, identification of correlational relationships is common with scatter plots.

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9. Bubble Chart

Just like the scatter charts, a bubble chart is also capable of showing relationships and distributions. In these variations, you will have to replace the data points with bubbles. Also, a Bubble Chart is a multivariable graph that is a cross between a Scatterplot and a Proportional Area Chart. Also, like scatter charts, bubble charts uses a Cartesian coordinate system to plot points along a grid where the X and Y axis are separate variables.

But unlike Scatterplot, each point is assigned a label or category. Moreover, each plot point then represents a third variable by the area of its circle. Bubble charts are typically used for comparing and showing the relationships between categorized circles. Also, you can use it for analyzing patterns/correlations.

10. Bullet Graph

In the end, I have a Bullet Graph. It is also one of the commonly used data visualization types. If your team is working toward a goal, then a bullet graph can help you out to visually track your progress. It has a similar layout to the bar graph. A bullet graph is usually used for displaying performance data, and bullet graphs function like a bar chart.

But they are accompanied by extra visuals elements. And it was developed as an alternative to the dashboard gauges and meters. This is because they do not often display enough information.

Also, with this data visualization type, you will be able to show and hide axis division value, customize axis limits, customize tick marks, and values. Also, you can customize plot cosmetics, draw circular, semicircular range bar. Plus, you can even customize the color of the range var. Overall, you can use bullet graphs in situations where you do not have enough space available for other types of gauges or widgets.

Five Essential Reasons to Implement Data Visualization Tools

Now that you know about different data visualization types and kind of know when to use what data visualization type. It’s time to discuss why data visualization is important and why you should use it. This would help you to get a clear idea about when to use data visualization and what kind of data visualization type would work best for your company.

1. Comparing Values

As a data analyst, you will get to see a fair share of data sets. And when you want to compare differences and similarities between these sets, charts are a great option. They quickly offer you the high and low values of a particular game so you can note significant differences, gaps, and other trends. 

Also, if you wish to create a comparison chart, then here are some of the common data visualization types that you can use:

  • Bullet Graph
  • Pie Chart
  • Mekko Chart
  • Bar Graph
  • Scatter Plot
  • Line Graph

Any of these visualization techniques will allow you to scan through a large number of data and create informative patterns for you, which will help your business.

2. Show Comparison

If you wish to show comparison, data visualization is definitely the technique that you will need to check out. Often in companies, we have to create a chart where we have to show how individuals units affect the more excellent picture.

For example, you might want to track the overall mobile accesses on your website by device type and geographical location. Or you wish to know which elements work best for you in your recent digital marketing campaign.

In these cases, you can simply compare two or more values and draw a pattern quickly. And to create a comparison chart, you can use these follow data visualization types:

  • Pie Chart
  • Mekko Chart
  • Stacked Bar Graph
  • Stacked Column Chart
  • Waterfall Chart
  • Area Chart

All of these data types simply allow you to measure individual performance levels and helps you to determine their effect on the overall data set. 

3. Determine Distribution

If you are looking ahead to understand the distribution of your data, then a distribution chart is the best way. As it will help you to show all the possible intervals or values of the values set as well as how often they occur. 

And from this visualization type, you will easily be able to identify the prevailing trends. As wells, you can determine the outliers which can disrupt the patterns. You can also get a clear picture of how extensive the range is between your information values.

To determine distribution, you can use any of these following data visualization types:

  • Scatter Plot
  • Mekko Chart
  • Line Graph
  • Column Chart
  • Bar Chart.

4. Researching Trends

You can use data visualization in researching trends. If you wish to determine how a particular data set has performed during a set time frame, then, in this case, you will need to use data visualization.

To create a data visualization for researching trends, you can use these following data visualization types:

  • Line Graph
  • Dual-Axis Line Graph
  • Column Chart

5. Understanding Relationships in Different Types of Data Visualization

Finally, if you wish to understand the relationships in different types of data visualization, then you can also use data visualization.

There are times when we want to understand a given variable and to see how it relates to one or multiple other variables. For instance, one variable could have a positive or negative effect on another.

And to create such type of data visualization, you can use these data visualization types:

  • Scatter Plot
  • Bubble Chart
  • Line Graph


So those were some of the most common type data visualization types that you can try out. So go ahead and check them out.

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What are the disadvantages of data visualization?

If you are using data visualization as a new form of communication, then it must be authentic in its explanation of the goal. If the design isn't done correctly, it might cause communication problems. The human interface lies at the heart of data visualization, which means that the data that serves as the foundation for visualization might be skewed. A useful all-purpose statistic is the 'mean' or average of a collection of data used in data visualization. Because outliers can distort data in one direction or the other, this is the case. One of the problems with information perception is that, however rational, its clarity in clarifying is entirely dependent on the crowd's focus point.

What is LLN with respect to data visualization and is it useful?

LLN or Law of Large Numbers states that any data visualization based on a tiny sample size is equally as skewed as the sample size. The concept behind the rule of big numbers is that only huge samples can produce reliable conclusions. Large data sets have a propensity to more precisely depict reality, according to formal definitions. However, when utilizing LLN with small sample sizes, the drawback is that the viewer does not perceive the sample size. As a result, LLN may be beneficial in big sample sizes but not in small ones.

What are some of the essential principles of data visualization?

Use sensory features like size, color, visuals, and typefaces to draw your audience's attention to the most essential pieces of information when you build your visualizations. Make sure the important sections are well-illustrated. Because a user's attention is directed to the top-left corner, you may want to add crucial data points there. Ensure that your information displays are vertically and horizontally aligned so that they may be correctly compared. This also aids in the avoidance of deceptive visual illusions in your presentation. Avoid using elaborate gauges and labelling that might obstruct visibility. When labelling the axis of a graph or chart, always start at zero unless there's a good reason not to, such as when the data is grouped at abnormally high levels.

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