Guide to Data Visualization Techniques: Examples and Best Practices

Data is more abundant than ever. But without context and clarity, even your richest data sets can fall flat. That’s where data visualization comes in to transform complex numbers into clear, visual formats. Using data visualization, your organization can more easily and effectively spot patterns, communicate insights, and drive smart decisions.

In this guide, we’ll break down the most effective data visualization techniques and explain when and why you might use them. We’ll take you through and share some real-world examples to help bring your data to life. Whether you’re a data analyst designing dashboards or a business leader looking to understand trends at a glance, this guide will help you visualize with confidence.

What is data visualization?

Data visualization takes information and data and turns it into something you can easily see and understand. It uses graphical elements like charts, graphs, maps, and infographics to communicate data in a visual format, making it easier for people to discover trends, patterns, outliers, and relationships.

When done well, data visualization turns raw data into an intuitive story. It’s about making data accessible and actionable for everyone in your organization.

Effective visualizations reduce cognitive load, helping you focus on what matters without having to sift through rows of numbers. They serve as a bridge between technical analysis and strategic decision-making, allowing teams to collaborate around a shared understanding of performance, opportunities, and challenges. From dashboards and presentations to real-time monitoring tools, visualizations are the lens through which modern organizations interpret and act on their data.

When insights are clearly visualized, they become easier to share, easier to trust, and far more likely to drive measurable business impact.

Why data visualization matters

In a business environment where speed and clarity are critical, data visualization empowers faster, more confident decision-making. It helps teams interpret findings, identify risks, and communicate value to both technical and non-technical stakeholders. At its core, visualization transforms data from a passive asset into a strategic tool.

Data visualization bridges the gap between raw information and practical action, enabling organizations to make smarter, more consistent decisions in real time. Whether analyzing marketing performance or tracking supply chain metrics, visualizations keep stakeholders aligned and informed. The result is better collaboration, faster execution, and a stronger foundation for innovation.

Here’s why data visualization is essential:

• Improves understanding
  Visuals simplify complex data and bring focus to what matters. Instead of sifting through spreadsheets, users can immediately grasp insights through clear, intuitive visuals.
• Enables faster decisions
  Executives can spot key trends in seconds—without diving into spreadsheets. This agility helps organizations pivot quickly and respond to real-time changes with confidence.
• Reveals patterns and outliers
  Charts can uncover relationships, clusters, or anomalies that might go unnoticed in tabular data. This leads to better diagnostics, forecasting, and risk management.
• Boosts data engagement
  Well-designed visuals make data more accessible, encouraging exploration and collaboration. When people can interact with data, they’re more likely to use it.
• Drives alignment
  Clear dashboards and reports create a shared understanding across departments. This fosters cross-functional teamwork and helps everyone stay on the same page regarding goals and performance.

Common data visualization techniques

Let’s explore some of the most common—and useful—visualization types. Each serves a unique purpose, depending on your data and goals. Well-chosen visuals don’t just clarify; they persuade, inform, and spark action.

Bar chart

Use when: You need to compare discrete categories
Example: Sales by region
Visual: A side-by-side bar chart showing Q1 sales by product (e.g., shoes, shirts, accessories)
Tips:

• Use consistent bar width
• Limit to 5–10 bars for readability
• Use horizontal layout for long labels

Line chart

Use when: You want to show change over time
Example: Monthly website traffic
Visual: A time-series line chart showing daily visits over 12 months
Tips:

• Use one line per metric
• Highlight key changes with annotations
• Use markers to clarify data points

Pie chart

Use when: You want to show parts of a whole
Example: Share of revenue by product category
Visual: A circular chart divided into labeled segments for electronics, home goods, apparel
Tips:

• Limit to 5–6 categories
• Avoid for precise comparisons
• Label slices clearly

Scatter plot

Use when: You want to explore relationships or outliers
Example: Customer income vs. churn rate
Visual: A scatterplot showing income along the x-axis, churn rate on the y-axis, with color-coded customer segments
Tips:

• Add trendlines to show correlation
• Use size or color for a third variable
• Highlight noteworthy outliers

Histogram

Use when: You want to explore frequency distributions
Example: Response times grouped by interval
Visual: A histogram showing delivery times binned into 5-minute intervals
Tips:

• Keep bin sizes consistent
• Avoid too many or too few bins
• Use for continuous data only

Heatmap

Use when: You want to show intensity, density, or correlation
Example: Website clicks by hour and day
Visual: A matrix-style heatmap with darker colors for higher traffic
Tips:

• Use a perceptually uniform color scale
• Provide a clear legend
• Be mindful of colorblind accessibility

Area chart

Use when: You want to show cumulative data over time
Example: Accumulated revenue over quarters
Visual: A filled area chart comparing revenue growth across regions
Tips:

• Use stacked areas for comparison
• Avoid clutter with too many layers
• Use transparency for overlapping areas

Bubble chart

Use when: You want to show three variables in one chart
Example: Sales (x), profit margin (y), and store size (bubble size)
Visual: A bubble plot where each bubble represents a store
Tips:

• Use tooltips or labels for detail
• Be careful with overlapping circles
• Scale bubble size proportionally

Treemap

Use when: You want to visualize hierarchical data
Example: Revenue by department and subdepartment
Visual: A color-coded block layout showing marketing, sales, operations, each subdivided
Tips:

• Avoid too many small boxes
• Use hover interactions for detail
• Combine with drill-down views

Geospatial map

Use when: You want to show location-based data
Example: Sales by region or customer concentration by state
Visual: A US map shaded by revenue levels or pin density
Tips:

• Choose a projection that fits your region
• Include clear legends
• Use consistent scale across regions

Advanced data visualization techniques

If you’re ready to go beyond the basics, these advanced techniques communicate more nuanced or multi-dimensional data. They’re especially useful for uncovering deeper insights, modeling complexity, or telling richer data stories in areas like project planning, operations, and predictive analytics.

Sankey diagram

Use when:You want to show flow from one set of values to another
Example:Web traffic journey from referral source to conversion
Visual: Flows where width represents volume between stages
Tips:

• Emphasize major paths by adjusting flow widths
• Limit categories to avoid visual clutter
• Use intuitive color-coding for stages

Radar chart

Use when:You want to compare multiple variables across entities
Example:Skill ratings across team members
Visual: Circular graph with axes radiating from a central point
Tips:

• Use consistent scales across all axes
• Limit variables to keep the chart readable
• Highlight key differences with shading or outlines

Waterfall chart

Use when:You want to show a running total over time
Example:Revenue breakdown from gross to net
Visual: Sequential columns showing additive and subtractive values
Tips:

• Start and end points should be clearly marked
• Differentiate positive and negative contributions with color
• Label key points for clarity

Gantt chart

Use when:You want to track timelines and dependencies
Example:Project phases and milestones
Visual: Horizontal bars representing tasks along a timeline
Tips:

• Show task dependencies with connecting lines
• Use color to indicate status (e.g., completed, in progress)
• Keep timelines updated to reflect changes

Box-and-whisker plot

Use when:You want to show data distribution with outliers
Example:Delivery times by warehouse
Visual: Box showing quartiles with “whiskers” for min and max values
Tips:

• Clearly label quartiles and outliers
• Use consistent scale across multiple plots
• Provide context to interpret variability meaningfully

Choosing the right visualization

The chart you choose can either clarify your message or cloud it. That’s why it’s essential to match the visualization type with the nature of your data and the story you’re trying to tell. Some charts highlight comparisons; others reveal trends, relationships, or structures. Ask yourself: What’s the key takeaway I want the viewer to grasp—and how can I make that insight as intuitive as possible?

Use this table to guide your decision-making:

Your goal—Best chart typesWhen you want to:Use this type of chart:Compare categoriesBar chart, column chartShow trends over timeLine chart, area chartShow part-to-wholePie chart, treemapShow relationshipsScatter plot, bubble chartShow distributionHistogram, box plotShow flow or sequenceSankey, waterfall chartShow location dataGeospatial map, choropleth mapShow hierarchyTreemap, sunburst chart

Choosing the right chart improves comprehension and increases impact. When your visualizations align with your audience’s intent, you unlock clarity and drive smarter actions.

Pitfalls to avoid in data visualization

Even well-intentioned visuals can mislead if not handled carefully. Poor design choices can obscure the message, confuse the viewer, or even cause incorrect conclusions. A chart that looks polished might still fall short if it lacks relevance or usability. Good visualization balances accuracy, simplicity, and storytelling to drive the right conclusions.

Avoid these common data visualization mistakes:

• Too much data
  Overloading a chart with information makes it hard to read. Simplify where possible and highlight what matters most.
• Poor color choices
  Bad color contrast or the use of red/green can hinder interpretation, especially for colorblind users. Use high-contrast, accessible palettes and avoid unnecessary decoration.
• Improper scaling
  Truncated axes or inconsistent intervals can distort trends and comparisons. Start bar charts at zero and use even spacing to avoid skewed visuals.
• Misleading chart types
  Don’t use a pie chart when a bar chart would be clearer. Choose the format that best matches the structure and purpose of your data.
• Unclear labeling
  Without proper labels, legends, or titles, even the best chart can confuse. Include context and explanations, especially when presenting to external stakeholders.
• Ignoring the audience
  Design visualizations for your end user, technical detail for analysts, and clear takeaways for execs. Align tone, granularity, and context with their expectations.

Clear design equals clear insight. When in doubt, test your visuals with users before sharing.

Dashboard design principles

Dashboards are where many visualizations come together. When designed thoughtfully, they serve as powerful tools for decision-making and performance tracking. 

These best practices ensure your dashboards tell a cohesive and meaningful story:

• Lead with the most important KPIs
  Place high-impact metrics at the top or top-left, where eyes naturally land first.
• Group related charts together
  Organize visuals by theme—marketing, sales, operations—so users can easily navigate insights.
• Use consistent design and color schemes
  A unified look reinforces brand identity and improves user experience.
• Limit to one screen where possible
  Avoid overwhelming users with scrolling. Use tabs or drill-downs for deeper dives.
• Offer filters for custom views
  Empower users to tailor insights to their roles, regions, or time frames.
• Test for clarity and relevance regularly
  Gather feedback, update stale data sources, and ensure every element serves a purpose. A great dashboard evolves with the business.

How AI is shaping the future of data visualization

AI and machine learning are transforming how organizations create and consume visualizations, shifting from static reports to dynamic, real-time insights that adapt to user needs.

• Automated chart suggestions: BI platforms like Domo now use AI to recommend the most effective chart for your data, reducing manual guesswork.
• Natural language querying: Users can ask questions in plain language and receive instant visual answers, making data accessible to non-technical teams.
• Predictive visualizations: Forecasts and what-if scenarios are visualized automatically from time-series models, enabling proactive decision-making.
• Smart alerting and annotations: Anomalies or significant changes are flagged and explained through automated visual callouts, helping teams act faster.

AI is turning data visualization into a proactive, personalized experience, reducing the gap between raw data and insight. As models learn from usage patterns, they’ll not only show you what’s happening, but also highlight what matters most, when it matters most.

Final thoughts

Data visualization is the bridge between information and understanding. It turns spreadsheets into stories and numbers into narratives. Whether you’re building a dashboard, presenting to leadership, or exploring data patterns yourself, choosing the right visualization is key to driving action.

Great visualizations communicate and inspire. They align teams, clarify priorities, and help people see what needs to happen next.

As data continues to grow in volume and complexity, the ability to distill it into clear, compelling visuals will become a critical business skill. The most effective data leaders are those who can translate raw numbers into visual stories that prompt meaningful decisions. Mastering these techniques will sharpen your analytical edge and elevate your ability to lead, influence, and innovate.

Looking to level up your visual storytelling? Explore how Domo enables fast, dynamic, and intelligent visualizations that bring clarity to your business. Watch a demo today.