Mastering data charts: A comprehensive guide to visualization

By 2025, global data creation will grow to more than 180 zettabytes. Data grows as people and companies create, capture, and copy it daily. Yet raw data isn't particularly beneficial by itself.

Data visualizations are crucial for businesses seeking to transform data into a compelling and understandable story. Charts and graphs make it easier to digest complex data, spot patterns, and make data-driven decisions. This guide will discuss the power of data charts, including the different types and their specific use cases.

The power of data visualization with charts

Visualization can help our brains understand complex data. Charts can take large amounts of data and transform it into an understandable format. A data-driven company may rely on charts to help leaders quickly analyze data and make better-informed business decisions.

Additional benefits of data visualization include:

  • Quickly identifying trends and patterns
  • Enhancing communication between teams
  • Discovering valuable and actionable insights
  • Reducing the risk of missed opportunities

What are data charts?

Data charts are essential tools for presenting data analysis. Visualization provides a universal language anyone can understand. However, you cannot insert data into just any chart type. Different charts can better represent your data and use case. Let's explore the different types of charts and their specific use cases.

Types of data charts and their uses

There are several types of data charts, each with a specific use case. Outlined below are brief explanations of each data chart type and what each chart is best suited for:

Bar chart

The bar chart is one of the most fundamental data visualizations. Levels are plotted on one axis, while values are on the other. The length of the bar indicates its value.

Best for: Showing the distribution of data points and comparing subgroup values

Line chart

A line chart plots points and connects them with a line moving from left to right. By observing whether the line rises or falls, readers can discern the changes in the data.

Best for: Predicting outcomes and revealing trends

Scatter plot

A scatter plot identifies two or more variables and reveals their relationship. The position of various data points can provide visual trends and patterns.

Best for: Identifying outliers and gaps in data

Box plot

A box plot uses boxes and whiskers to show the distribution of values within a group. Boxes contain 50% of the data, and whiskers contain the remaining.

Best for: Comparing multiple groups

Histogram chart

A histogram is a type of bar chart. One variable is quantitative, which can help visualize the data distribution.

Best for: Showing the distribution of a data set

Stacked bar chart

A stacked bar chart is when one bar measures multiple components. It's divided into different categories based on the grouping variable.

Best for: Revealing subgroups that make up the entire bar while still comparing primary groups

Grouped bar chart

A grouped bar chart has the same principles as a stacked bar chart. But instead of dividing the bar, it clusters the categories into multiple bars.

Best for: Reviewing subgroup comparisons but not allowing for primary group comparisons.

Dot plot

Similar to bar charts, a dot plot compares categorical groupings. However, it uses dots instead of bars to demonstrate the data's value.

Best for: Comparing values with unordered categories

Area chart

An area chart is a specialized form of a line graph. It has a shaded region between the line and the baseline.

Best for: Showing changes over time

Dual-axis chart

A dual-axis chart combines two different types of graphs into one visualization. It shares the same x-axis but has a different y-axis.

Best for: Comparing two trends and analyzing for correlation

Bubble chart

A bubble chart is a modification of a scatter plot. It can add variables using color, shape, or size for each point as indicators.

Best for: Discovering a relationship between three variables

Density curve

Also known as the kernel density estimate, a density curve shows the distribution of data. It can reveal skewness and the number of peaks.

Best for: Finding a smoothed estimate of the underlying distribution

Violin plot

A violin plot is a type of box plot. But instead of boxes and whiskers, it uses a density curve to provide a comprehensive view of data distribution.

Best for: Comparing data shapes between groups.


A heatmap is another data visualization that reveals relationships between groups. In this case, color demonstrates values—typically when one or more variables are not numerical.

Best for: Comparing data density and user interaction analysis

Pie chart

Pie charts depict values as areas sliced from a circle. Categorical data is a good option for pie charts. Keep the number of categories small and ensure the data is additive for the best results.

Best for: Part-to-whole comparisons and showing relative sizes

Funnel chart

A funnel chart demonstrates how data moves through a process. It's a suitable choice if data goes through separate stages, with a noticeable amount of data getting lost in each stage.

Best for: Process flow visualization or sequential process analysis

Bullet chart

The bullet chart adds more context to a single bar. It provides more context on its value and helps measure multiple variables.

Best for: Comparing actual results to benchmarks

Data charts across different industries

Data visualizations play a vital role in decision-making across industries. Here are a few examples of how industries use data charts:

  • Healthcare: Monitor disease trends and track patient satisfaction scores
  • Finance: Analyze market movements, guide investment options, and communicate complex financial information
  • Marketing: Review consumer trends, customer responses to campaigns, and consumer preferences
  • Retail: Anticipate product demand fluctuations and analyze sales trends

The art of creating data charts

Data visualization requires a blend of art and science. Accurately analyzing raw data is crucial, but even the colors of your charts can influence the storytelling behind the data.

An effective chart should highlight the key insights of the data. Leaders should easily understand the data and what course of action to take. But it begins with picking the right tools to build your charts and graphs.

Pick a business intelligence tool that can gather, evaluate, and visualize data for you to make your company's data usable. Once you've collected your data, you can hone in on creating the best visualization for your dataset.

Choosing the right data chart

Knowing which data chart is best for your specific use case can be difficult with various data chart types available. One factor to consider when choosing a data visualization is your audience.

Simple charts are preferable for general audiences since they are easy to understand. More complex charts better suit professional audiences with particular expertise or interests.

Overall, there are six common use cases for data visualizations. Pairing your use case with the proper data visualization can help enhance your data storytelling.

Matching data types to chart types

1. Showing change over time

  • Bar chart
  • Line chart
  • Box plot

2. Showing part-to-whole composition

  • Pie chart
  • Stacked bar chart
  • Stacked area chart

3. Showing data distribution

  • Bar chart
  • Histogram
  • Density curve
  • Violin plot
  • Box plot

4. Comparing values between groups

  • Bar chart
  • Dot plot
  • Line chart
  • Grouped bar chart
  • Violin plot
  • Box plot
  • Funnel chart

5. Observing relationships between variables

  • Scatter plot
  • Bubble chart
  • Dual-axis plot
  • Heatmap

6. Evaluating geographical data

  • Choropleth
  • Cartograms

Data charting best practices

Creating clear and visually appealing data charts is essential to conveying your message to your audience. Here are some tips on how to design effective data charts:

  • Avoid unnecessary usage of color
  • Maintain color consistency throughout your charts
  • Consider cross-cultural meanings of colors
  • Choose an appropriate measurement interval
  • Remove unnecessary elements to hone in on essential data
  • Make legends and labels clear
  • Use the full axis range to ensure accuracy
  • Ensure viewers can easily read charts without squinting

Common mistakes to avoid in data charting

When creating data charts, you should avoid these common mistakes:

  • Choosing the wrong chart type
  • Using misleading or biased data visualizations
  • Inserting too much data
  • Making poor color choices
  • Applying 3D effects

When it comes to choosing colors, a designer should also consider accessibility. Color blindness can make it difficult to read charts. You may need to alter lightness and saturation to ensure it’s readable to all audiences. Tools like Coblis or Viz Palette can help you with color accessibility.

Following the best practices for data charting will help you communicate your message. Ultimately, excellent data visualization can impact a person's decision-making process.

Interpreting data charts

Interpreting data charts is critical in extracting meaningful insights from visualized information. In the following sections, we delve into the key elements of data visualization and provide techniques for uncovering actionable insights. By understanding these fundamental concepts, users can effectively interpret data charts and make informed decisions based on the presented information.

Deciphering data insights from charts

When you receive a data visualization, you need to interpret it accurately. Let's look at crucial data visualization elements and tips for finding valuable insights from data charts.

Understanding key data chart elements

Depending on the data chart, it may have the following key elements:

  • Title: A short description of what the chart represents
  • Legend: Explains the data in the chart
  • Data points: Singular data markers on the chart
  • Plot: Area that displays the data
  • Categories: Labels for data groups
  • Axes: X-axis (Horizontal) and Y-axis (Vertical) with corresponding values or categories

Techniques for extracting meaningful information

When reviewing a data visualization, consider these tips to glean actionable insights:

  • Analyze axes to understand the categories, values, and measurement interval
  • Identify patterns like clusters or consistent changes in the data
  • Consider what factors may have influenced the data
  • Determine if the creator is authoritative and non-biased

After reviewing a data chart, a viewer should easily understand the main takeaway of the visualization. It can enable them to interpret the data effectively and make data-informed decisions.

Advanced data charting techniques

Understandable data is essential, but making it usable is another task. Democratized data means every employee can access data at any point.

Taking your data charts to this next level involves using advanced data charting tools and features. You want to look for a business intelligence platform to create interactive and dynamic data charting. Some features can include:

  • Sharing findings with dynamic dashboards
  • Setting up custom alerts for important developments
  • Collaborating within the platform
  • Integrating with a tech stack for data optimization

Troubleshooting data chart challenges

Data visuals should aid in telling your story. Sometimes, users misleadingly create data charts. Avoiding these issues is crucial to having an honest and transparent conversation.

Below are several techniques you can use to correct data chart errors:

  • Choose the right chart type that best suits your dataset.
  • Avoid information overload by only using essential data chart elements.
  • Apply consistency to scales and measurement intervals.
  • Use the correct baseline for the chart type - some charts, like a line chart, don't need a zero-value baseline.
  • Show the full data chart to avoid misinterpretation. Truncated axes, or cutting off a portion of the axis, should be avoided.

Data charting for growth

As companies grow, so does the amount of data they collect. Scaling data charting in businesses requires establishing a solid strategy. Dynamic data handling is often part of the foundation for data charting.

Your business may have hundreds of data sources, so pulling them together in one place is essential. Using a business intelligence platform can help automate this task. It can also handle organizing large amounts of data. Using automated processes is an integral part of sustainable growth for businesses.

Staying current in data charting

Data charting is a scientific art form, with best practices evolving as technology improves. Professionals should stay on top of the latest trends in data analysis to ensure relevance and maintain their competitive edge.

Developing advanced data charting skills requires professionals to have a willingness to experiment. Testing data visualization tools can lead to better charts. Better visuals enhance your current presentations and provide more compelling storytelling.

Unlocking insights through strategic data visualization strategies

Data analysis and visualization affect nearly every industry—influencing decision-making across various teams. However, effective data charting requires attention to detail and appealing visuals.

Choosing the right chart type can ensure data accuracy and identify patterns. Exploring the world of data charts can lead to engaging and visual storytelling. It's an impactful way to communicate insights and help decision-makers act on data.

Additional resources

For more information on how you can use SQL techniques to further enhance your data management processes, check out these helpful resources: