Mastering data visualization: 15+ graphs, charts and beyond

Data visualization involves transforming raw data into visual representations that convey logical connections and facilitate informed decision-making. To assist you with these data visualization chart types, in the post below, we have provided a comprehensive list of data visualization types and how to choose the right data visualization type you need.

List of 15+ most common data visualization types (Use cases included)

1. Bar Graph

Bar graph is a visual representation of data, utilizing bars of varying heights to illustrate values. Bar graphs can be constructed with vertical or horizontal bars, as well as grouped bars for comparing values within a category, or stacked bars to display multiple types of information.  

Best for: 

• Comparing discrete categories 
• Highlighting changes over time 
• Communicating data simply and effectively 

Use cases: 

• Sales analysis 
• Website traffic analysis 
• Survey results visualization 
• Financial data analysis 
• Educational purposes 

Pros: 

• Intuitive and readily understood 
• Effective emphasis on differences 
• Customization flexibility 
• Works with diverse data 

Cons: 

• Limited insight into relationships 
• Clutter with many categories 
• Misrepresentation of proportions 
• Limited detail within categories

2. Line Graph

Line graph is a common chart type that is easily recognizable. A line graph is used to show trends, progress, or changes over time. It is most effective when the data set is continuous rather than fragmented. 

Best for: 

• Whether it’s analyzing stock prices, website traffic, or temperature fluctuations, line graphs excel at showcasing how data points evolve over a continuous period. 
• Line graphs can reveal correlations and patterns between two or more variables, such as sales and marketing spend or temperature and rainfall. 
• Presenting continuous data 

Use cases: 

• Financial analysis 
• Scientific data visualization 
• Engineering and technical analyses 
• General trend analysis 

Pros: 

• Effective portrayal of trends and changes 
• Highlighting relationships between variables 
• Scalability and versatility 
• Clear and concise communication 

Cons: 

• Limited comparison across categories 
• Potential for misinterpretation 
• Clutter with multiple lines 
• Loss of detail within data points

3. Bullet Graphs

Bullet graphs are an effective tool for visually monitoring your progress. Resembling a bar graph in its layout, it also integrates additional visual components. Thanks to bullet graphs, you can initiate the process by selecting a primary measure. Subsequently, you compare this measure to another (or multiple) measures, enabling you to uncover a more profound significance and correlation. 

Best for: 

• Comparing a single measure to a target and qualitative range, all neatly packed into a compact and visually intuitive format. 
• Consolidating information and presenting it in a space-efficient way, ideal for dashboards and reports with limited real estate. 

Use cases: 

• Sales performance monitoring 
• Marketing campaign analysis 
• Project management tracking 
• Financial performance analysis 
• Individual performance evaluation 

Pros: 

• Combines multiple data points (target, actual value, and qualitative range) into a single, easy-to-understand visual. 
• Powerful progress visualization 
• Space-saving design 
• Versatile for various data types 

Cons: 

• Limited to single measures 
• Potential for misinterpretation 
• Complexity in setup 
• Not ideal for intricate relationships 

4. Column Chart

One of the most popular categories of data visualization tools is a column chart. Column charts have measurable metrics or values displayed on the vertical (Y) axis, which is sometimes referred to as the chart’s left side, and data labels along the horizontal (X) axis.  

Best for: 

• If you have data with distinct categories, like product types, customer segments, or years, a column chart excels at visually showing their differences in magnitude. 

Pros:

• Simple to read and understand 
• Modifying one data set won’t impact the others 
• Easy to add data labels without overcrowding the chart 

Cons:

• Having numerous categories can make things appear messy 
• Complex clustered column charts are typically more challenging to quickly comprehend

5. Dual-Axis Chart

Most visualization charts use a single Y-axis and X-axis, but a dual-axis chart has a shared X-axis and two separate Y-axes. It combines features of a column chart and a line chart, and you can customize the graphing styles based on your data.  

Best for: 

• Comparing two data sets with different units of measurement or magnitudes that are related in some way but displaying them on the same chart for enhanced comparison 
• Saving space by combining two relevant data sets into a single chart 

Use cases: 

• Marketing analysis 
• Financial analysis 
• Scientific data visualization 
• Project management 
• Performance tracking 

Pros: 

• Enhanced comparison 
• Space efficiency 
• Correlation insights 
• Focus on relationships 

Cons: 

• Increased complexity 
• Misinterpretation risk 
• Limited data types 
• Overshadowing details

6. Area Chart

Area charts are a type of chart that combines lines and filled areas to present information visually. It is commonly used to display quantitative data and is based on a line chart. The area between the axis and the line is often highlighted using colors, textures, or hatchings. 

Area charts are frequently used to compare two or more quantities and to show cumulative totals over time, either in numbers or percentages (known as stacked area charts). 

Best for: 

• Visualizing trends over time, especially when the total value is important and you want to understand how its parts contribute to the whole.  
• Highlighting changes in magnitude and proportions between categories within a whole over time.  
• Emphasizing the cumulative nature of data, where understanding the accumulation of values over time is crucial.  

Use cases: 

• Market share analysis 
• Financial analysis 
• Website traffic analysis 
• Population growth trends 
• Investment portfolio analysis 

Pros: 

• Effective trend visualization 
• Part-to-whole relationships 
• Cumulative data clarity 
• Intuitive and visually appealing 

Cons: 

• Limited comparison across categories 
• Misinterpretation of proportions 
• Clutter with many categories 
• Loss of detail within data points

7. Stacked Bar Chart

Stacked bar graph is a visual representation of data that uses horizontal or vertical bars to compare different categories. Each bar is divided into segments, with each segment representing a subcategory or component of the main category.  

Best for: 

• Ideal for data representing things like budget allocations, product category breakdowns, or website traffic sources. 
• Emphasizing the relative size of categories within a dataset, especially when absolute values are not crucial.  

Use cases: 

• Budget allocation analysis 
• Visualize sales breakdowns by product type, highlighting which categories contribute most to revenue. 
• Analyze website visits by source (organic, social, direct) over time, understanding how visitor acquisition channels evolve. 
• Track market share evolution for different competitors, showcasing the relative dominance of each player. 

Pros: 

• Composition clarity 
• Proportion shifts highlight 
• Stacked bars excel at comparing the relative size of categories within a dataset, even when absolute values are not the main focus. 
• The layered design can be visually appealing and help viewers grasp complex data relationships. 

Cons: 

• Comparing absolute values between categories can be difficult due to stacked layers obscuring individual heights.  
• Improper scaling or overlapping bars can lead to misrepresentation of the relative size of different components.  
• Too many categories can lead to a visually cluttered chart with overlapping bars, hindering clarity. 
• Stacked bars show overall composition but may not reveal finer details within each individual category

8. Mekko Chart

This chart may not be as familiar to you unless you work in the field of data analysis. Known as a Marimekko chart or Mekko chart, it shares similarities with a stacked bar graph, but with one significant difference: rather than tracking time progression, the X-axis represents a different dimension of your data sets. 

Best for: 

• Analyzing relationships between two categorical variables and one quantitative variable, especially when both the proportions within categories and the total size of categories are important.  
• Presenting complex data in a visually compact and informative way, especially for dashboards or reports with limited space. 

Use cases: 

• Customer segmentation analysis 
• Employee demographics and salary analysis 
• Analyze website traffic by source and region, revealing which source drives the most visits in each region. 
• Financial portfolio analysis 

Pros: 

• Reveals complex relationships 
• Proportions and size emphasis 
• The multi-layered design effectively presents complex data in a compact format, ideal for limited space presentations. 
• Can be adapted to various data types, including percentages, ratios, and absolute values. 

Cons: 

• Learning curve and interpretation complexity 
• Mekko charts prioritize understanding relationships between factors over precise analysis of individual data points. 
• Less intuitive for general audiences

9. Pie Chart

Pie charts are visual representations of a fixed value, divided into different categories that make up its individual segments. Pie charts are particularly beneficial in the field of digital marketing, as they allow you to present a comprehensive breakdown of various aspects. 

Best for: 

• Highlighting proportions between distinct categories that add up to a whole, especially when the focus is on the relative size of each category rather than their absolute values.  
• Presenting data simply and concisely, especially for general audiences who need a quick overview of the composition of a whole. 
• Adding variety and visual interest to your data visualization portfolio, particularly when combined with other charts for deeper analysis. 

Use cases: 

• Survey results visualization 
• Budget allocation analysis 
• Website traffic analysis 
• Educational purposes 

Pros: 

• Intuitive and familiar 
• Focus on proportions 
• Simplicity and conciseness 
• Visual variety and appeal 

Cons: 

• Limited data capacity 
• Loss of detail 
• Misinterpretation of proportions 
• Unreliable for comparisons across datasets 
• Limited trend analysis 

10. Scatter Plot Chart

A scattergram, also known as a scatter plot, is a type of visualization that displays various variables plotted along two axes. Both the X-axis and the Y-axis are value axes, as a scatter plot does not utilize a category axis. These forms of data visualization are most effective when analyzing multiple data points and searching for similarities within the dataset.  

Best for: 

• Exploring correlations or potential relationships between two numerical variables 
• Identifying outliers or unusual data points that might not be apparent in other chart types. 
• Visualizing trends and patterns in high-dimensional data sets, especially when multiple variables are involved. 

Use cases: 

• Financial analysis 
• Scientific data visualization 
• Marketing analysis 
• Medical research 
• Engineering and technical analysis 

Pros: 

• Relationship revelation 
• Outlier detection 
• Flexibility for diverse data 
• High-dimensional data exploration 

Cons: 

• Can be visually cluttered with dense data 
• Not ideal for causal inferences 
• Requires careful axis labeling and annotations 
• Limited ability to show distributions or proportions

11. Bubble Chart

Bubble charts, similar to a scatter chart, do not utilize a category axis and can be used to display relationships or distributions. However, instead of data points, bubbles are used in this variation. In addition, the sizes of the bubbles are varied to represent a third data set. Instead, the data sets are plotted using X-values, Y-values, and now, Z-values (bubble size). 

Best for: 

• Visualizing three dimensions of data simultaneously: showing not only the relationship between two numerical variables but also adding an additional layer of information represented by the size of the bubbles.  
• Adding visual interest and engagement to your data presentation, particularly when other chart types might feel too rigid or monotonous. 

Use cases: 

• Visualize website visits by source (organic, social, direct) and average engagement time (scrolling, clicks), revealing which sources drive the most engaged traffic. 
• Population change analysis 
• Scientific data visualization 
• Track ad campaign performance by clicks and conversion rates 

Pros: 

• Multi-dimensional data exploration 
• Outlier and cluster identification 
• Make data presentations more engaging. 

Cons: 

• Clutter and misinterpretation with complex data 
• Limited storytelling capacity 
• Not ideal for precise comparisons 
• May not be suitable for all audiences

12. Waterfall Chart

The waterfall chart is also referred to as a flying brick chart or Mario chart because the columns (bricks) appear to be suspended in mid-air. Waterfall charts are a type of data visualization that aids in comprehending the overall impact of sequentially introduced positive or negative values. These values can be based on time or categories.  

Best for: 

• Visually showcasing the cumulative effect of different factors on a final value, particularly when the individual contributions and intermediate changes are important to understand. 
• High-level overview of change and contribution 

Use cases: 

• Financial analysis 
• Project management 
• Investment performance analysis 
• Sales analysis 
• Inventory management 

Pros: 

• Cumulative effect clarity 
• Easy to understand how individual factors, both positive and negative, contribute to the final value, aiding in deeper analysis. 
• The cascading design can be visually appealing and enhance audience engagement with the data. 

Cons: 

• Complexity and potential misinterpretation 
• Limited trend analysis 
• Requires careful labeling and annotations

13. Funnel Chart

Funnel charts are commonly employed to visually depict the various stages within a sales process and show the revenue at each stage. In a funnel chart, the size of each section within the chart is determined by the series value, represented as a percentage of the sum of all values.  

Best for: 

• Ideal for data representing things like website visitor progression through signup forms, customer segments entering and exiting loyalty programs, or leads progressing through sales stages. 
• Communicating a clear and concise flow 

Use cases: 

• Website traffic analysis 
• Marketing campaign analysis 
• Customer acquisition analysis 
• Sales performance analysis 
• Project management 

Pros: 

• Clear process visualization 
• Conversion rate optimization insights 
• Easy to understand for broad audiences 
• Focus on key stages and drop-offs 

Cons: 

• Funnel charts work best for linear, sequential processes and may not be suitable for complex, branching journeys with multiple paths or loops. 
• Oversimplification of individual stages 
• Visual clutter with many stages

14. Heat Map

Heat map is a visual representation of data that shows the intensity or magnitude of individual values within a dataset using colors. There are two primary types of heat maps: spatial and grid. Spatial heat map indicates the magnitude of a spatial phenomenon by assigning colors to different areas on the map. 

Best for: 

• Heat maps effectively draw attention to areas with high or low values, helping to identify areas of focus or anomalies. 
• Heat maps enable efficient comparison across different regions, categories, or time periods, readily showcasing variations and patterns. 

Use cases: 

• Sensor data analysis 
• Analyze stock market performance over time 
• Customer data analysis 
• Map temperature changes or species distribution in an ecosystem, showcasing spatial patterns and potential trends. 

Pros: 

• Intuitive visual representation 
• Efficient pattern identification 
• Comparative analysis across dimensions 

Cons: 

• Loss of detail with coarse data 
• Color perception limitations 
• Overlaying too many variables or categories can lead to a visually cluttered chart and difficulty discerning patterns 
• Not ideal for precise comparisons

15. Gantt Chart

Gantt charts are a graphical representation of a project schedule, presented as a bar chart. It displays the tasks to be completed on the vertical axis and time intervals on the horizontal axis. The length of the bars represents the duration of each activity.  

Best for: 

• Planning, tracking, and communicating timelines for complex projects with multiple tasks and dependencies.  
• Gantt charts offer a clear overview of the project schedule for team members, facilitating collaboration and monitoring progress effectively. 

Use cases: 

• Software development projects 
• Marketing campaigns 
• Construction projects 
• Event planning 
• Product launches 

Pros: 

• Gantt charts offer a straightforward representation of project timelines, tasks, and their durations, making complex schedules easily understandable. 
• Dependency and critical path highlighting 
• Resource allocation and monitoring 
• Collaboration and communication tool 

Cons: 

• Large projects with numerous tasks and dependencies can lead to a visually cluttered chart and difficulty in grasping the overall view 
• Limited resource flexibility 
• Static representation of progress 
• Focus on time rather than resources

16. Treemap

A treemap is a graphical technique for illustrating hierarchical information, employing nested rectangles to depict the various branches of a tree diagram. The size of each rectangle corresponds to the quantity of data it represents. 

Best for: 

• Exploring hierarchical data and understanding the relative size and composition of its subcategories within each level.  
• Presenting complex data in a visually compact and engaging way, especially for situations where displaying numerous categories within limited space is crucial. 

Use cases: 

• Company financial analysis 
• Biological taxonomy visualization 
• Market share analysis 
• Customer segmentation analysis 

Pros: 

• Hierarchical data clarity 
• Pattern and relationship discovery 
• Tree maps offer a space-efficient way to present complex data with numerous categories, making them visually appealing and engaging for viewers. 

Cons: 

• Color perception limitations 
• Limited ability to show trends or changes over time 

5 Essential reasons to implement data visualization type

After learning about the various types of data visualization graphs, charts, and maps, let’s take a moment to talk about why data visualization is important. If you’re unsure about which visual type is suitable for your business, it’s beneficial to know the key business functions that data visualization can fulfill. Here are the top 5 functions to consider:

Comparing values

Charts are the perfect tool for comparing the similarities and differences between sets. It provide a clear visual representation of the high and low values within a set, making it easy to identify significant differences, gaps, and trends. The following types of visualizations are useful for creating a comparison chart: 

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

Show composition

You may also need to separate your value sets to demonstrate how specific units affect the overall picture. Or perhaps you’d like to know which aspects of your most recent digital marketing campaign were the most successful. To accomplish this, you have several options for data visualizations at your disposal: 

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

Determine distribution

The distribution chart provides a comprehensive display of all potential intervals or values within the value set, along with their respective frequencies of occurrence. This visual representation enables the identification of typical patterns and the detection of any anomalies that may disrupt these patterns.  

Also, it offers a clear depiction of the extent of variation between the information values. When determining distribution, you can use the following kinds of data visualizations: 

• Scatter plots 
• Mekko charts 
• Line graphs 
• Column charts 
• Bar charts

Researching trends

When you need to evaluate the performance of a specific data set within a designated time period, these kinds of visual aids are effective: 

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

Understanding relationships in different types of data visualization

To comprehend a specific variable better, it can be helpful to observe its connection with one or more other variables. For example, one variable may influence another either positively or negatively. These kinds of charts can be used to show relationships between things visually: 

• Scatter Plot  
• Bubble Chart  
• Line Graph 

How to choose the data visualization type you need

With multiple data sources on social media and blogs, it can be overwhelming to manage these complex content assets. What should you focus on tracking? What holds the most importance? Here are 4 steps to choose the data visualization type that you should refer to:

Start with WHY: Identify the goals and why you need data visualization

You can compare different values, comprehend how separate components affect the overall, and study trends with the aid of a chart or graph. In addition, charts and graphs are also helpful for identifying data that deviates from your experience or for illustrating the links between groups.

Figure out what data vis types you might need 

Various types of charts and graphs utilize distinct forms of data. Graphs typically depict numerical data, whereas charts visually represent data that may or may not involve numbers. 

Hence, although all graphs fall under the category of charts, not all charts are graphs. If you do not possess the required data, you may have to allocate some time to compile the necessary information before constructing your chart.

Gather your data 

In order to create a chart, invest additional time in collecting the appropriate numerical data. Alongside the quantitative data tools that track traffic, revenue, and user data, incorporating qualitative data may also be essential. Below are alternative methods for gathering data to enhance your data visualization: 

• Conducting interviews 
• Using quizzes and surveys 
• Analyzing customer reviews 
• Reviewing customer documents and records 
• Engaging with community boards.

Select the right type of data vis type  

Using an inappropriate visual aid or relying on the most prevalent form of data visualization may result in viewer confusion or misinterpretation of data. However, for your business to benefit from a chart, it must effectively and clearly convey your intended message.

Q&A

Wrapping up

After completely going through the full list of data visualization types in this post, you are now fully equipped to craft your own data visualization. Whether you are a data scientist or a marketer involved in data analysis, understanding the various types of data visualization is a critical skill for success. 

At the same time, the selection of the right data visualization type plays a vital role in effectively conveying the hidden patterns and insights within intricate data. When understanding the pros and cons of different types of data visualizations, both businesses and individuals can leverage the power of data visualization to enhance decision-making and achieve favorable outcomes. 

Hope that this sharing is useful for you in any situation. 

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