5 Types of Data Visualization (With Examples)

Every day, companies gather enormous amounts of data, organizing it in vast repositories that — without proper presentation — can be overwhelming and underutilized. Data visualization offers a solution to this problem by translating large, complex data sets into intuitive visuals, revealing patterns, trends and anomalies that can users make more informed decisions.

 

What is Data Visualization?

Data visualization is the practice of presenting information in the form of graphs, charts, maps, dashboards and other graphical formats, bridging the gap between raw data and actionable insights.

Data visualization does more than just simplify information — it enables data-driven decision-making. For example, a heatmap displaying real-time server performance metrics can immediately highlight problem areas, allowing teams to take corrective action. Or a scatter plot of customer engagement data may reveal hidden correlations that inform a company’s marketing strategy.  

“We’re used to thinking about data showing up in tables like Excel or CSV files, but data visualization — geospatial maps for example — ties all of that information together to the geometry of data,” said Parker Ziegler, a computer science PhD candidate at the University of California, Berkeley. “Visuals are the best tools we have for understanding and communicating large-scale phenomena.”

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5 Types of Data Visualization

Different data visualization techniques serve diverse analytical and operational needs:

 

1. Charts and Graphs

Charts and graphs are typically used to categorize information, identify trends and draw comparisons.

Bar Charts

Bar charts display categorical data in the form of rectangular bars, with the length or height of each bar corresponding to its value. These are used to compare quantities across different categories, making it easy to spot differences in size or frequency. 

Example: A bar chart might be used to compare bug distributions across different software modules, with the length of each bar representing the amount of bugs found in each specific module.

Line Graphs

Line graphs display data using points connected by straight lines. They are often used to show changes or patterns over time, such as increases, decreases or fluctuations in value. 

Example: A line graph might be used to track a website’s traffic over the course of a year, with each point representing the number of visitors to the site in a specific month and lines connecting them to illustrate the trend.

Pie Charts

Pie charts display data in a circular graph divided into slices, where each slice represents a portion of a whole. The size of each slice corresponds to its percentage or proportion, making it easy to see how different parts relate to the total. 

Example: A company might use a pie chart to understand the distribution of its budget, with each slice representing the percentage allocated to areas like salaries, marketing, operations and so on.

Scatter Plots

Scatter plots display data as a collection of points on a graph to show the relationship between two variables, with one variable determining the point’s position on the horizontal axis (x) and the other on the vertical axis (y). Additional variables can be represented by adjusting the color, shape or size of each point. Scatter plots help identify trends or clusters within a data set, making it easier to spot correlations or outliers. 

Example: A business might use a scatter plot to analyze the relationship between advertising spend and sales revenue, with each point plotted based on the amount of money spent on an ad campaign and the amount of money earned.

 

2. Maps and Geospatial Visualizations

Maps and geospatial visualizations display data in a geospatial context, making it easier to analyze spatial patterns and relationships based on location — whether be on Earth or within a digital environment like a website.

Heat Maps

Heat maps display data on a grid, using variations in a color’s hue or vibrancy to represent the magnitude of individual values within a data set. 

Example: A heat map might be used to visualize website traffic, with color intensity varying based on the number of visitors to different pages. This can help to identify the most- and least- popular pages on the site.

Choropleth Maps

Choropleth Maps display geographical variations in data by shading or coloring areas on a map according to the value of a particular variable, making it easier to identify patterns.

Example: A choropleth map might be used to display population density across different regions, with darker shades representing higher population densities and and lighter shades indicating lower population densities.

Dot Maps

Dot maps display dots on a map to show the distribution or frequency of a specific variable across a geographic area, with each dot representing a particular quantity or value. They are especially good at identifying differences in density or concentrations.

Example: A business might use a dot map to visualize the location of its retail stores throughout the country — with each dot representing one store — to help assess its market coverage and spot potential locations for new stores.

 

3. Diagrams

Diagrams illustrate processes, hierarchies and relationships using shapes, lines, arrows and other visuals. 

Node Link Diagrams

Node link diagrams depict relationships between variables, where the nodes represent the variables themselves and the links represent the connections between them — essentially forming a network structure. Also known as network diagrams, node link diagrams are commonly used to represent system architectures or data flows.

Example: A company might use a node link diagram to understand the components of a software system and how they interact, where each node represents a system component (server, database, application etc.) and the links between them show data flow or communication pathways.

Tree Maps

Tree maps use nested rectangles to represent the specific categories. The size of each rectangle is proportional to the value of the data in each category, making it easy to compare different categories and figure out how they relate to the whole. Colors can also be used to differentiate between categories within the hierarchy.

Example: A sales team might use a tree map to organize revenue into different product categories, with each rectangle representing a product and its size indicating the proportion of total revenue it contributes. Each product category may also be organized into sub-categories using different colors, such as breaking down “tech products” into cellphones, laptops, headphones and so on. 

Flow Charts

Flow charts show the sequential steps of a process using symbols connected by arrows. Symbols represent the steps in the process and are often categorized by shape, with labels written inside. Arrows indicate the direction of flow from one step in the process to another. Sometimes instructions are written in, too. Flow charts offer a useful way to explain complex procedures, systems or concepts, and can aid in the development, planning or improvement of processes.

Example: A business might use a flow chart to visualize their customer service process, from receiving a complaint to resolving an issue, to help identify bottlenecks and improve efficiency.

 

4. Dashboards

Dashboards combine multiple visualization types into one interactive interface, offering a comprehensive overview of complex systems. They can be used to monitor cloud infrastructure, track DevOps pipelines,analyze web traffic and much more.

Operational Dashboards

Operational dashboards display real-time data using visual elements like charts and graphs to track the status of a process or workflow, making it easy to assess progress and identify any issues.

Example: A sales team might use an operational dashboard to monitor key performance indicators (KPIs) such as monthly sales, conversion rates and average deal sizes, allowing the company to monitor performance and make adjustments as needed.

Strategic Dashboards

Strategic dashboards measure the overall progress of strategic goals, displaying high-level, long-term data using visual elements like charts and graphs. Company executives and managers often look to strategic dashboards to monitor the progress of ongoing projects.

Examples: A human resources manager might use a strategic dashboard to track things like employee turnover rates, employee engagement scores and diversity and inclusion metrics to hone their recruitment and retention strategies.

Tactical Dashboards

Tactical dashboards bridge the gap between strategic planning and operational tasks, tracking the progress of specific teams initiatives against overall goals. While operational dashboards are used to monitor day-to-day activities and strategic dashboards look long-term, tactical dashboards focus on medium-term execution, helping managers optimize their team’s performance to ensure it aligns with the big-picture objectives.

Example: A software development team might use a tactical dashboard to track milestones reached while building a new product, such as completed coding sprints, bug fixes resolved and testing phases completed. This can help to ensure that the project is progressing according to schedule.

Analytical Dashboards

Analytical dashboards help make sense of large volumes of data, allowing users to make predictions, identify trends and set targets based on historical data.

Example: A digital marketing team might use an analytical dashboard to analyze web traffic data, such as page views, bounce rates and user demographics to help them refine their marketing strategies and set targets for the following quarter.

 

5.Statistical Visualizations

Statistical visualizations display data in a way that highlights its statistical properties, such as distributions, averages, medians and variability. They help users identify trends, make predictions and draw conclusions.

Histograms

Histograms are graphical representations of data distributions that use adjacent, rectangular bars to show the frequency of data points within specified intervals, known as bins. They are commonly used to visualize the shape, spread and central tendency of a data set, making them ideal for understanding patterns in numerical data.

Example: A company might use a histogram to visualize the spread of salaries across the organization, with each rectangular bar showing the number of employees whose salary falls within a certain range.  

Box Plots

Box plots display numerical data in a way that shows how it is spread out and where most of it falls. A box is used to represent the middle 50 percent of the data, with a line inside indicating the medium, while lines (or whiskers) extending above and below the box indicate the variability outside the upper and lower quartile.

Example: A box plot might be used to help analyze different delivery times across different shipping methods, showing the typical range, the median time and any unusually slow or fast deliveries.

Candlestick Charts

Candlestick charts are specifically used in the stock trading world to visualize and analyze price fluctuations over time. The rectangular body of each candle displays the range between the open and close price of a given stock or commodity during a specific time period, while the lines (or wicks) extending from the top and bottom of the candle represent the highest and lowest price traded during that time period. When the market is bullish (meaning the closing price is higher than when it opened) the body of the candle is colored white or green, but if the market is bearish (meaning the closing price is lower than when it opened) it is colored black or red.

Example: A financial analyst or day trader might use a candlestick chart to monitor the price of a given stock or commodity over the course of a day, helping them make more informed decisions about whether to buy or sell. 

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Data Visualization Steps

Crafting impactful data visualizations requires a systematic process:

1. Define Objectives

The visualization process begins with clearly defining its purpose, whether the goal is monitoring system performance, analyzing user behavior or communicating project outcomes. 

2. Data Collection and Preprocessing

High-quality visualizations depend on high-quality data. This step involves sourcing data from reliable systems, cleaning it to remove noise or inconsistencies and transforming it into a structured format that is ready for analysis. For tech applications, this often includes data from APIs, databases or log files.

3. Selection of Visualization Techniques

Choosing the right visualization format is critical. For instance:

• Bar charts work well for discrete comparisons, such as sales figures across different regions.
• Line charts are ideal for illustrating trends, such as web traffic over time.
• Heatmaps can efficiently represent data related to location or intensity, such as areas of network congestion or frequency of errors. 

4. Utilize Visualization Tools

Modern tools and libraries simplify the creation of visualizations while providing powerful customization options. Popular tools among tech professionals include:

• Python libraries: Plotly, Bokeh, Matplotlib and Seaborn
• Business intelligence tools: Tableau, Power BI.
• Frameworks for interactivity: D3.js for web-based dynamic visualizations.

5. Validation and Iteration

Visualizations should be evaluated for accuracy and utility to make sure they correctly represent the data and serve their intended purpose. Iteration is also important, as it ensures that the visual representation aligns with technical goals and communicates insights effectively.

Related ReadingHere’s How AI Is Upgrading Data Visualization Techniques

 

Examples of Data Visualization

The application of data visualization is virtually limitless. These are a few creative examples: 

Broad Street Cholera Outbreak Map

The 1854 John Snow Cholera map is an early example of dot map visualization. Using bar graphs on city blocks, it presents the number of cholera deaths in a London neighborhood. The map ultimately revealed a concentration of deaths linked to a single contaminated well. 

Visualizing the History of Pandemics

Visualizing the History of Pandemics is a custom graphic created by Nicholas LePan. It

chronicles known pandemics throughout human history, detailing each disease’s name, death toll and approximate timeline. It combines scaled 3D illustrations with research data from sources such as the CDC, WHO, BBC, Wikipedia, Historical Records, Encyclopedia Britannica and Johns Hopkins University. 

NASA’s Eyes on Asteroids

NASA’s Eyes on Asteroids is a map that showcases the asteroid belt and real-time positions of asteroids within an interactive 3D solar system model. It offers up-to-date insights into asteroid trajectories and potential hazards. 

Bar Chart of Gender Disparity in Disney Films

Polygraph (The Pudding) visualized gender disparity in Disney films with a dynamic bar chart. 

Key features include an interactive gradient bar for exploring genres. This visualization is part of a broader project analyzing gender disparities in popular films. 

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