How data visualization can be used: 10 use cases and examples

Data silos can cripple an organization. Information needs to flow freely between departments and employees. When you know how to visualize data, making it understandable to various stakeholders, you eliminate confusion and alternative interpretations, creating a single source of truth. This shared understanding helps staff cooperate better, align their interpretations of facts, improve communication, and foster teamwork.

Top-notch data visualization examples are interactive at their core. Users can manipulate them, explore various data points, and view the numbers from different angles. This interactive capability of data visualization techniques turns routine data processing into exploratory analysis, allowing for a deeper understanding of available information.

With raw data, pinpointing significant information can be challenging. Data visualization brings critical information to the forefront, enabling personnel to focus on it instead of sifting through numerous figures in search of crucial insights.

The best data visualization examples make it easy to compare data points. Line graphs and bar charts for visualization are among the most common methods, simplifying comparisons and helping employees see the big picture behind individual numbers and metrics.

Data visualization helps form a comprehensive view of an organization's activity. Specialized tools can reveal connections between various numbers (like order values and average sales cycle), helping you understand reasons for underperformance or, on the contrary, revenue growth.

Raw data scattered across a lengthy text can obscure general patterns behind this multitude of figures. Visualizing data helps to identify trends and spot anomalies. Both trends and outliers provide valuable insights for further actions.

Employees and managers often need to persuade others to take action (e.g., invest in a startup, change marketing strategy, overhaul recruitment policies, review resource allocation, etc.). Including visuals in their presentations or pitches can capture the audience's attention, making the narrative more convincing and engaging.

Data analysis is a means to an end. The major answer to the question "How is data visualization used?" lies in its ability to help decision-makers take informed, data-driven steps.  Clear, visualized information allows for identifying growth opportunities, mitigating risks, optimizing expenditures, and more.

Given the variety of data visualization types, it's worth exploring data visualization real-life examples used by well-known brands and organizations.

Column charts are simple and intuitive. They display numerical data in columns varying in height or width, making it easy to compare values at a glance.

As a rule, column charts are employed to exhibit changes that occur over time or compare frequencies, percentages, and similar metrics across several groups or categories side by side. They help you spot differences and visualize rankings.

A line graph can also display trends tracked over a certain period, but here, the changes are shown by a line that goes up and down along the time axis.

Such graphs offer a valuable comparative perspective when showing progress across multiple indices. Various types of line charts for data visualization can be utilized in the same use cases as column charts. Still, lines are considered to be more likely to be misinterpreted than columns, mainly when the chart contains multiple lines.

Scatter plots depict multiple data points as circles or bubbles, revealing relationships between two variables represented by the circles' size and/or color.

Moreover, this chart is exceptional at visualizing distribution patterns, enabling analysts to assess summary data quickly.

As the name implies, bubble graphs represent data using bubbles, a variation of the scatter plot model. The distinction between the two lies in the number of values depicted in a single image (multiple for bubble charts) and the number of measurements per category (one per category in bubble charts).

Bubble charts highlight the significance of a limited number of categories amidst a larger number of less significant ones. While commonly used in non-business fields like administration or ecology to advocate for political points, enterprise managers can also utilize them to emphasize areas of high spending or reveal misplaced priorities.

Like any network model, this diagram represents relationships between entities using nodes and edges (or relationships). These elements expose connections, dependencies, and interactions between the nodes.

Commonly used in analyzing transportation networks, these graphs have one significant drawback: their complexity. Multiple connections and the absence of apparent patterns can make them cluttered and difficult to interpret.

A heat map illustrates the correlation between two variables on a gradable scale from high to low. This correlation is represented by a color code, or gradients explained beneath the map.

Such diagrams provide a broader overview of numerics and values without delving into nuances and details. They excel at visualizing the analysis of large datasets such as population density, customer base distribution, or website traffic indices.

Similar to the previous example, color-code values are also important in stacked bar charts, but their variations are displayed within bars.

This technique is often employed in commerce to track sales performance over a specific timeline, segmented by the sales team.

True to its name, this chart resembles a circular graph with values represented as pie slices.

This diagram is an excellent visualization for data segmentation and percentage distribution across various categories, where the total sum equals 100%. It provides a much clearer understanding of their proportions compared to simple percentage listings. However, to ensure effectiveness and readability, the number of slices in such a pie should be at most 6-8, which imposes severe limitations on its usage.

Polar charts serve the same purpose of displaying data distribution within a single category, resembling the previous technique in appearance. However, here, each value's share is represented not by the angle size (which remains equal for all categories) but by the length of each sector, starting from the circle's center.

Each sector can feature color code variations, allowing this chart to be combined with the stacked bar graph.

Typically, funnel charts are used to illustrate a progression through a path consisting of a series of steps.

Since it's modeled on the universally known conversion funnel, this chart is perfect for representing website conversion indices, with the sequential data displayed at each stage.

As you can see, there is a wide range of visualization methods to choose from. Understanding when to use data visualization and what type to opt for is crucial to leverage them efficiently. Given the importance of the task, it's recommended to delegate it to experts in the domain.