One of the most common challenges businesses face is the inability to analyze and use data well. There are many reasons why this happens, including lack of strategic alignment, poor data quality, and detached data workflows. But for many companies, the problem lies in their inadequate data visualization techniques.
It’s one thing to collect complex data—it’s quite another to display it in a way that enhances understanding, facilitates decision-making, and drives insights. In this article, we’ll cover what data visualization is, why it matters, and some best practices for displaying data.
What is Data Visualization?
Data visualization is the graphical representation of information and data through charts, graphs, maps, and other visual elements. Its primary purpose is to make complex datasets more accessible so that users can interpret them and use them as the base of decision-making. By translating raw data into visual formats, data visualization enables individuals to identify patterns, trends, and insights that might be difficult to discern from raw numerical data alone.
Why Does Data Visualization Matter?
Data visualization matters because it facilitates the effective communication of information. It leverages the capacity of the human brain to process visual information more rapidly than text- or number-based information. Visualization bridges the gap between data and decision-making, allowing users across various teams—such as marketing, customer service, and product—to make informed choices based on a clear understanding of collated data.
Data visualization also plays a pivotal role in storytelling as it helps convey narratives, emphasize correlations, and communicate new findings. No matter the industry, data visualization transforms complexity into clarity, which helps users understand data better and implement innovative solutions.
Good vs. Bad Data Visualization: The Impact Difference
Clear, effective visualizations distill complex datasets into understandable insights, providing a roadmap for decision-makers across all industries. When data is presented visually, trends, patterns, and outliers become more apparent and obvious—which is important in business where quick and informed decision-making is critical.
On the flip side, the impact of data diminishes significantly when users struggle to decipher the meaning of the visualizations. Poorly designed or overly complex visuals can lead to confusion, misinterpretation, and flawed decision-making. If people find it difficult to discern patterns or draw meaningful conclusions, the value of the data is lost.
To prevent this, visualization tools use graphs, charts, and maps to bring statistics to life. Whether it’s machine learning engineers explaining self-running AI systems or conversion rate optimizers performing A/B tests on marketing assets, data visualization helps represent the data and ensure that users can easily interpret it.
Innovative Data Visualization Examples
Many data visualization techniques exist, but here are the most common ones:
1. Line plots
Line plots or charts display data points connected by straight lines, which makes them excellent at illustrating how trends evolve. They’re usually created by putting a time variable on the X-axis (horizontal axis) and the subject variable on the Y-axis (vertical axis).
For example, below is the line chart created to explain the evolution of The Boeing Company’s stock price over the last six months.
On the X-axis are the time points from July 2023 to January 2024. On the Y-axis, there are price points ranging from $0 to $266. The green line signifies how the stock price has risen and fallen over the past few months, per the values on the line graph.
2. Bar charts
Bar charts present categorical data with rectangular bars, whose lengths are proportional to the values they represent. Bar charts are commonly used to display and compare the values of different categories or groups.
They either have horizontal or vertical bars. In a vertical bar chart, categories are typically represented on the X-axis, while the values are shown on the Y-axis. In a horizontal bar chart, these roles are reversed. Each category on the chart has a corresponding bar, and the length of the bar represents the value or quantity associated with that category.
Above is a vertical bar chart that shows the traffic sources of a website from March 2015 to February 2016. On the horizontal axis, we have the months and years listed, each one with a corresponding bar (which is divided into sections with different colors to depict the traffic sources). On the vertical axis, the number of site visitors is listed in intervals of 250,000.
Here’s a horizontal bar chart from HubSpot’s 2024 Sales Report that details the challenges that sales teams faced in 2023.
On the vertical axis, we have the categories of challenges listed out, with the corresponding bars. On the horizontal axis, each bar is matched with the corresponding percentages of sales teams that faced the challenge.
While bar charts are great for comparing the values of different categories and showing trends over time, they may not be sufficient for visualizing complex data sets with many categories.
3. Pie charts
A pie chart is a circular statistical graphic that is divided into slices (hence the pie) to illustrate numerical proportions. Each slice represents a proportion of the whole, and the size or angle of each slice is proportional to the quantity it represents. Each slice is typically assigned a unique color to make it easier for viewers to identify each category.
Pie charts are commonly used in data visualization to display the distribution of a set of categories or the relative sizes of different parts compared to the whole (part-to-whole relationships).
Here’s a pie chart showing the availability of fluid milk per capita in the U.S. in 2013.
It’s easy to deduce that 35% of the population drank plain 2% milk, 27% drank plain whole milk, 14% drank plain 1% milk, and so on. If you add up all the percentages, you get 100%, which represents the entire U.S. population in 2013.
Because pie charts are easy to read and interpret, they’re great for audiences that are interested in the key takeaways, rather than the nitty-gritty of the information. For technical audiences who need a more in-depth explanation of the data, pie charts may not be adequate for displaying complex information.
A histogram is a graphical representation of the distribution of a dataset. It displays the frequencies of different values (or range of values) within the dataset. The vertical axis represents the distribution frequency of a variable (amount, firmographic data), and the horizontal axis represents the value of the variable (ages, minutes, years).
While histograms look a bit like bar graphs, the obvious difference between the two is that histograms have no gaps between the rectangle bars. This is because the data that histograms represent are continuous and the bins (the intervals between the data ranges) are adjacent.
Histograms are great for visualizing the shape of the distribution, whether it’s symmetric, leans to the left or right, or has multiple peaks. This helps users understand the central tendency and variability of the data. You can also use histograms to identify outliers, which often appear as individual bars or isolated groups of bars that deviate significantly from the overall pattern.
A heatmap is a kind of visualization that uses color variations to represent the intensity of values within a matrix. Heatmaps are particularly effective for visualizing complex datasets and patterns, especially when dealing with huge amounts of raw data.
The colors in a heatmap often range from low-intensity color (light or cool colors) to high-intensity color (dark or warm colors)—a scale that allows users to understand the magnitude of each value.
There are many use cases for heatmaps. For example, if you want to analyze the parts of your website that your audience focuses on the most, you can use a heatmap to show the level of activity on sections of your site. Or if you want to figure out which day of the week you get the most sales, a heatmap can show you that.
Because they apply to various domains and are effective at breaking down large datasets, heatmaps make it easier to identify trends, correlations, and areas of interest.
The five data visualization techniques are not the only ones that exist—they’re just the most popular. Other data visualization techniques include:
- Gantt charts
- Bullet graphs
- Bubble plots
- Scatter plots
- Box-and-whisper plots
- Correlation matrices
- Network diagrams
- Sankey diagrams
Data Visualization Best Practices
The goal of data visualization is to make raw and complex data easier to understand. While choosing the right data visualization tool and technique is crucial for success, there are some other best practices you need to implement. Here are some of them:
- Consider your audience. Before visualizing data, learn your audience’s level of technical knowledge and area of expertise. If your audience is technical, you can use techniques like Gantt charts and treemaps. But if they’re not, they’ll understand bar and pie charts much more easily.
- Label effectively. Clearly label axes, data points, variables, and any relevant elements in your visualization. Proper labeling provides context and helps viewers understand the scale and significance of the data, which makes the visualization more effective.
- Use color strategically. Most visualization techniques need color(s). In this case, use color purposefully to highlight key points, trends, or categories. Be careful not to overwhelm your audience with excessive colors or confusing schemes.
- Use fonts sparingly. It’s tempting to use different kinds of fonts and sizes in your data visualization. But to avoid confusing your audience, stick to one font and three different sizes. Follow the font hierarchy, e.g.,H1s should be larger than H2s, and H2s should be larger than H3s.
- Provide context. Your audience shouldn’t have to work too hard to understand your data visualization. So, offer meaningful context, and insights, or explanations to guide viewers through the information. Connecting the dots and providing a narrative context ensures that the data is not just presented but is easily understood and remembered.
Data Visualization Trends
Data visualization, like all aspects of tech, is evolving every day. Here are some data visualization trends:
- Augmented Reality (AR) visualizations. AR visualizations seek to integrate virtual elements with the real world, providing an immersive and surreal experience. Users can explore data in a three-dimensional space, improving engagement and understanding.
- Interactive dashboards. Interactive (or dynamic) dashboards allow users to manipulate and explore real-time data, facilitating a personalized experience. Users can drill down into details, filter information, and gain insights tailored to their specific needs.
- Spatial data visualization. Most data graphs are one- or two-dimensional. Spatial data visualization, like AR, introduces a third dimension, which gives data more depth and nuance. Whether it’s exploring location-based data or visualizing convoluted datasets, spatial visualization can help you uncover patterns and insights that aren’t as obvious in traditional visualizations.
- AI-driven visualization. As artificial intelligence and machine learning play a huge role in data analysis, it’s only fitting to integrate AI algorithms into visualizations, while ensuring that the decision-making processes are comprehensive and interpretable for users.
- Collaborative data visualization. Just like with Google Suite, we may see data visualization platforms adopt a collaborative feature that allows multiple people to interact with and contribute to data visualizations in real-time. These collaborative features will be accompanied by social features that allow users to communicate and swap ideas throughout the visualization process.
Unlock the Transformative Power of Data Visualization
From traditional charts to more advanced techniques, data visualization aims to transform raw data into meaningful insights that companies can use to make good decisions. Data visualization methods allow you to uncover patterns, trends, and relationships within datasets.
About the Author
Bernard Aguila is a brand ambassador and SEO Outreach Specialist at Omniscient Digital, a premium content marketing & SEO agency.