How to choose the right data visualization chart type - 2025 Guide

Do you often wonder whether to use a pie chart or a bar graph to show proportions? If you're struggling to find the right way to present your data, you're not alone. With so many data visualization types to choose from, it’s easy to feel overwhelmed and end up with visuals that confuse rather than clarify.

Using the wrong chart or graph can distort your message, which may lead to misinterpreted or missed insights. This not only wastes time but also damages your credibility with stakeholders.

In this guide, we’ll show you how to choose the right visualization type for your specific data and goals. Whether you’re presenting trends, comparisons, or relationships, we’ll help you make informed decisions that turn complex data into clear, actionable insights.

Key considerations when choosing data visualizations

Choosing the right data visualization isn’t just about selecting a chart that looks appealing; it’s about selecting a visual that best communicates the story your data tells. Below are the key considerations to keep in mind when making your decision.

Data type 

Understanding your data is the first step. The structure of your data—whether it’s categorical, numerical, hierarchical, or geographical—dictates the most appropriate visualization type.

By understanding the nature of your data, you can narrow down the types of charts that will effectively convey your insights.

Purpose of the visualization 

Your visualization should directly serve the purpose of your analysis. Ask yourself, “What am I trying to show?” Are you comparing values, demonstrating changes over time, showing parts of a whole, or visualizing relationships? Each purpose uses different types of charts.

Choosing a chart that aligns with your goal ensures your data story is clear and engaging.

Understanding your audience 

Tailoring the complexity of your visualizations to your audience is critical. If your viewers are non-technical, simple charts like bar graphs or pie charts may be more appropriate. For a more specialized or technical audience, complex visualizations like scatter plots, Sankey diagrams, or heat maps may be suitable.

Additionally, for those beginning their data visualization journey or working within budget constraints, exploring free data visualization tools can be beneficial. These tools often provide a range of basic to advanced features, enabling users to create effective visualizations without financial investment.

Context 

Finally, think about where your visualizations will be used. Visuals for a large report or dashboard can include more complex details than visuals for a presentation slide.

By considering these factors, you can confidently select the most effective visualization for your data.

How to choose the right data visualization?

When choosing a data visualization, it’s essential to understand what each chart is best suited for. Below, we categorize common data visualization charts based on their functions and explain when to use them for your data analysis.

Types of data visualizations charts and when to use them

1. Comparison and distribution charts

• Bar chart
• Stacked bar chart
• Combo chart
• Line chart
• Area chart
• Race chart

Bar chart

Bar charts use horizontal or vertical bars to represent data. They're best for comparing values across distinct categories, such as sales by region or product performance.

Stacked bar chart

Stacked bar charts break down and compare parts of a whole across categories. You can use bar charts when you want to show how different sub-categories contribute to the total value over multiple categories (e.g., sales by region, broken down by product type).

Combo chart

Combo charts combine two chart types, typically bars and lines, to show different kinds of information together. These are ideal for comparing two different types of data in one view, such as revenue (bar) and profit margin (line) over time.

Line chart

Line charts display trends over time by connecting data points with a line. This chart type can be used for time-series data—like stock prices, website traffic, or monthly sales figures—to illustrate trends or fluctuations over a period of time.

Area chart

Area charts are similar to a line chart, but the space between the line and the X-axis is filled in. They're best for visualizing cumulative data over time, such as showing revenue growth or total users across months.

Race chart

Race charts show animated changes in values over time for different categories, where bars change position as values fluctuate. Use this type of chart if you want to show rank changes over time dynamically, like population growth or monthly sales.

2. Part-to-whole and hierarchical charts

• Pie chart
• Tree map
• Sunburst chart
• Sankey diagram
• Funnel chart

Pie chart

Pie charts divide a circle into slices to represent portions of a whole. They're ideal for showing simple proportions or percentages, such as market share distribution. Pie charts are best used for five or fewer categories.

Tree map

Tree maps display hierarchical data as nested rectangles where the size of each rectangle corresponds to its value. You can use tree maps when you want to show hierarchical data (e.g., sales by department, sub-department, and product) in a compact visual form.

Sunburst chart

Sunburst charts are a radial visualization where each ring represents a level in the hierarchy. They're best for visualizing multilevel hierarchical data, like organizational structures or folder and file breakdowns.

Sankey diagram

Sankey diagrams visualize flow data, with the width of the flow indicating the quantity. You can use these diagrams to illustrate flow between categories, like web traffic between pages or budget allocations across departments.

Funnel chart

Funnel charts show the progressive reduction of data as it passes through different stages and are often used in processes like sales or lead generation. Use this type of chart when visualizing stages in a process, such as the customer journey from awareness to purchase.

3. Relationship and correlation charts

• Scatter plot
• Bubble chart
• Web chart

Scatter plot

Scatter plots show the relationships between numerical variables using points on a grid. They're useful when analyzing the correlation or relationship between two variables, such as age vs. income or marketing spending vs. sales.

Bubble chart

Bubble charts extend the scatter plot by adding a third dimension (i.e., the size of the bubble) that represents another variable. Use bubble charts when you need to compare three variables at once, like revenue (X-axis), profit (Y-axis), and market share (bubble size).

Web chart

Web charts are radial graphs where multiple variables are plotted along separate axes from the center. They're best for showing performance across multiple metrics, such as competitor analyses or employee skill assessments.

4. Geographical and spatial data charts

• Map chart
• Heat map

Map chart

Map charts visualize data across geographical areas using color coding, bubbles, or patterns to represent values. This type of chart is ideal for showing data sets that have geographical components, such as population density, sales by region, or election results.

Heat map

Heat maps use color intensity to represent data density or magnitude, often over a geographical area or a matrix. Use heat maps when visualizing patterns, such as website clicks by region or customer distribution.

By understanding the strengths of each chart type and when to use them, you’ll be able to convey your data in the clearest, most impactful way possible.

How to avoid common pitfalls

Here are some common pitfalls to avoid when designing data visualizations:

Overcomplicating the chart 

One of the most frequent errors in data visualization is trying to display too much information in one chart. Overloading your chart with excessive data points, multiple chart types, or too many colors can overwhelm your audience and make it difficult to interpret the data.

Solution: Keep your charts simple and focused on a single message. If necessary, break your data into multiple visuals to maintain clarity. Less is often more when it comes to conveying insights effectively.

Using too many colors 

Color is a powerful tool in data visualization, but overusing it can cause confusion. Too many colors or not enough contrast can make it difficult for viewers to focus on what matters most. Color also conveys meaning, so using inconsistent color schemes can make it harder to interpret the data.

Solution: Use color strategically. Stick to a minimal, consistent color palette that draws attention to the most important elements of your data. Avoid using more than five colors in a single chart, and make sure to use contrasting colors for clarity.

Lack of labels and annotations

Charts without proper labeling can leave viewers guessing about what the data represents. Failing to label your axes, not providing units of measurement, or not including annotations can make your visualization unclear or incomplete.

Solution: Always include clear, concise labels on your axes and data points. Provide titles and brief descriptions that explain what the data is showing.

Choosing the wrong chart type 

Selecting the wrong chart can lead to misinterpretation or confusion. Although our guide can be helpful in choosing the right data visualization type, it's not ideal to refer to it every time you create a visualization.

Solution: Let Zoho Analytics handle your data visualization needs. Powered by AI, Zoho Analytics is a BI and analytics platform that automatically selects the best visualization for your data.

Related read: 7 best data visualization tools for understanding your data better in 2025

How Zoho Analytics simplifies data visualization

Zoho Analytics offers two simple ways to create data visualizations:

• Drag-and-drop visualization builder: Just drag and drop the required columns into the respective fields and click the Generate button. Zoho Analytics automatically creates a visualization based on your data type, which you can change as needed.

• AI assistant: You can easily use Ask Zia, Zoho's AI assistant, by submitting questions in plain language. Zia will generate reports for you in only a few seconds.

Other key features:

• Support for 500+ data sources, such as cloud business apps, files and feeds, databases, data lakes, and data warehouses

• Built-in AI-powered data preparation studio

• Dynamic and interactive data visualizations

• Advanced sharing and collaboration options

• Data alerts

• Options to embed visualizations

• Automated interactive slideshow generation

Best practices for data visualization

Creating effective data visualizations involves more than just choosing the right chart type; it requires thoughtful design and attention to detail. Here are some best practices to ensure your visuals communicate data clearly and effectively:

Keep it simple 

The goal of any data visualization is to make data easy to understand. Avoid unnecessary complexity by focusing on the key message you want to convey. Simplify your chart design by removing distractions like grid lines or excessive data points. A clean, minimal design keeps the viewer’s attention on the data.

💡Tip: Ask yourself, “Can I remove this element without losing meaning?” If yes, it’s probably not necessary.

Use consistent colors 

Consistency in color schemes is essential for clarity. When working with multiple charts in a dashboard or report, maintain a consistent color palette to help users quickly associate colors with categories.

💡Tip: Stick to a limited color palette with a maximum of five or six colors. Use contrasting colors only to highlight important data points.

Label clearly and add annotations 

Proper labeling ensures that your audience understands what they’re looking at. Always include descriptive titles, clear axis labels, and units of measurement.

💡Tip: Avoid cluttering the chart with too much text. Keep labels concise but informative.