8 types of charts you need to master data visualization

1. Line charts – show change over time

A line chart is a type of chart used to show change over time. It’s great for tracking trends and seeing how things rise, fall, or stay consistent over a period.

How to read it: Most line charts have a horizontal X axis showing time (for example, months or years) and a vertical Y axis showing values. Start by checking what the Y axis represents – is it revenue, percentage, number of users, or something else? Then follow the line from left to right to see how it changes.

When to use it: When your data includes dates (hours, quarters, months, years) and you want to show how one or more things progressively change over time.

Tip: In Flourish, you can add labels directly onto your lines instead of relying on a separate legend. This makes charts much easier to read, especially when you have multiple lines, as users don’t need to move their eyes back and forth between the chart and the legend to understand what each line represents.

2. Bar and column charts – compare categories

A bar or column chart is a type of chart used to compare values across categories. It’s commonly used to compare things like sales by region or survey responses by category.

How to read it: Bar and column charts have one axis that shows categories (for example, countries) and another axis that shows values. In a column chart, categories are usually along the bottom, while in a bar chart they appear on the side – the difference is just orientation.

To read the chart, focus on length. The longer the bar or column, the larger the value.

When to use it: When you have different groups (such as countries, teams, or products) and you want to compare their values side by side.

Tip: Sort your bars in a logical order. In most cases, this means ordering them by value so patterns are easy to see – Flourish does this automatically for you. But if your categories follow a natural sequence (like time periods or ranges), keep that order instead so the chart remains intuitive.

3. Pie and donut charts – show parts of a whole

A pie or donut chart is a type of chart used to show how a whole is divided into parts. It’s best for illustrating proportions when each category contributes to a single total.

How to read it: Start by identifying the largest slice, as this gives you a clear reference point. Then compare the remaining slices relative to it. Each slice represents a proportion of the whole, so you’re always reading values in relation to the total.

When to use it: Use a pie or donut chart when all categories together make up a complete total (for example, 100%). This makes it ideal for showing things like market share, budget allocation, or traffic sources.

Tip: Keep the number of slices low – ideally no more than five to seven. If comparison becomes difficult, consider switching to a bar chart instead.

4. Scatter plots – reveal relationships

A scatter plot is a type of chart used to show the relationship between two variables. It’s useful for identifying patterns, trends, and outliers in your data.

How to read it: Scatter plots have two axes, each representing a numeric value. Each dot on the chart represents one data point (for example, a country, product, or campaign).

Instead of focusing on individual points, look at the overall pattern. Are the points forming a clear upward or downward trend? Are there clusters where many points group together? Are there any outliers that sit far away from the rest?

If size or color is used, this often represents an additional variable, adding more context to the chart.

When to use it: As a rule of thumb, if you have two sets of numbers and want to see how they relate, a scatter plot is a good choice. For example, ad spend vs conversions, price vs product rating, or income vs life expectancy.

Tip: In Flourish, you can add a trend line to help highlight the overall relationship between your variables. You can also reduce clutter by showing labels for only selected data points or adding annotations to highlight key insights.

5. Heatmaps – highlight patterns in dense data

A heatmap is a type of chart used to compare values across two categories using color. It’s especially useful for spotting patterns in data that would be hard to read in a table.

How to read it: Heatmaps are usually arranged as a grid, where each row and column represents a category, and each cell shows a value using color.

Start by checking the color scale to understand what high and low values look like. Then scan the chart for patterns.

• Look for three things:
  • Patterns: Do values follow a trend, such as higher values in certain months or times of day?
  • Exceptions: Are there any cells that stand out from the rest?
  • Seasonality: Do similar patterns repeat across rows or columns?

Rather than focusing on individual cells, try to understand the overall structure of the data.

When to use it: Use a heatmap when you have two categorical variables and want to compare how they relate. For example, days of the week vs hours of the day, cities vs air quality levels, or months vs temperature.

Tip: Use a clear and intuitive color scale so differences are easy to see. For more guidance on using color effectively in data visualization, check out our blog post.

6. Sankey diagrams – visualize flows

A Sankey diagram is a type of chart used to show how a quantity flows between categories or stages. It’s ideal for visualizing how something moves, splits, or is distributed within a system.

How to read it: Sankey diagrams are most commonly read from left to right. The blocks (nodes) represent stages or categories, and the connecting bands show the flow between them.

• Focus on two things:
  • Width of the flow: The thicker the band, the larger the value it represents. Thinner bands represent smaller values.
  • Splits and merges: Look at how flows divide into multiple paths or combine into one. This shows how a quantity is distributed or consolidated as it moves through the system.

When to use it: Use a Sankey diagram when a single quantity moves through a process or breaks down into subcategories. For example, how users move through a funnel, how a budget is allocated across departments, or how energy is distributed across sectors.

Tip: Use color and emphasis to guide attention. In Flourish, you can highlight key flows and mute less important ones using color overrides, helping users focus on the most important parts of the diagram.

7. Network graphs – map connections

A network graph is a type of chart used to show relationships between connected entities, such as people, organizations, or topics. It’s useful for understanding how things are linked within a system.

How to read it: Network graphs are made up of two main elements:

• Nodes: The individual items (such as people or companies)
• Links: The connections between them

Start by looking at the overall structure. Are there clusters of nodes grouped together? These often represent communities or closely related groups.

Then look for central nodes – items that have many connections. These are often more important or influential within the network.

If arrows are used, they show direction, helping you understand how relationships or influence flow from one node to another.

Rather than trying to read every connection, focus on how the network is organized and where the key relationships sit.

When to use it: Use a network graph when your data is about connections. For example, social networks, company ownership structures, or relationships between people, products, or ideas.

Tip: Network graphs can become complex quickly. In Flourish, interactivity helps you explore them step by step – you can hover over nodes to highlight connections, or use layouts like radial view to spread nodes more evenly and make the structure easier to read.

8. Treemaps – show hierarchical structure

A treemap is a type of chart used to display hierarchical data using nested rectangles. It’s ideal for showing both proportions and structure in a single view.

How to read it: Treemaps are made up of rectangles nested inside each other. Each rectangle represents a category, and its size reflects its value (for example, sales, population, or traffic).

Start by looking at the largest rectangles to understand the biggest contributors. Then look at how those rectangles are divided into smaller sections to see how the data breaks down.

When to use it: Use a treemap when your data has a hierarchy (for example, categories and subcategories) and you want to show how each part contributes to the whole. It’s also a good alternative to a pie chart when you have many categories, as it handles larger datasets more clearly.

Tip: Alongside treemaps, options like packed circles, sunbursts, or hierarchical bars can sometimes make relationships even clearer depending on your data.