by Sooter Saalu
As big data and its sources become more commonplace, there is a growing need to interpret and analyze this data. Data visualization is an interdisciplinary field concerned with the visual representation of data in easily interpreted forms such as charts, graphs, maps, and diagrams.
Data visualization plays an important role in fields ranging from the social sciences to technology to health and human services. By breaking down large, complex pieces of information and reframing them as clear illustrations, data visualization makes data more accessible.
In this article, you’ll familiarize yourself with some of the common data visualization best practices. They’ll help you simplify and streamline your visualization process, create engaging and easily understood visualizations, and avoid confused audiences.
Visualizing and interpreting data sits at an intersection of science and art, with aesthetic and design considerations often mattering as much as mathematical and statistical considerations. Everyone has a unique way of approaching visualizations, and there are often multiple ways to visualize the same data, with no objectively “right” answer. Best practices and guidelines for data visualization help standardize the visualization process, simplifying the interpretation of the data visualizations and making their meaning clearer to the audience.
Identify and Understand Your Target Audience. Before making a visualization, the first thing to think about should always be who your target audience is. Knowing this helps you narrow the content of the visualization, figuring out exactly what it needs to say and the tone it should take.
Be as specific as possible about who you’re making the visualization for and what you want the audience to take from it. An audience can be technical or non-technical, knowledgeable or ignorant about data, interested in mostly exploratory (big picture) or explanatory (in depth) analyses, and many other variables. In order to get the most effective results, all of these factors should influence your visualization process.
The better you understand your audience, the more confident you can be that the visualization you create will resonate with them.
Choose an Effective Visual for Your Purpose. Depending on your purpose of the visualization, the nature of your data, and your target audience, there are several types of visuals you could use.
Most visualizations are used for one or more common purposes: to understand the distribution and composition of the data, to explore comparisons between data categories or over time, and to examine the relationships between two or more data variables.
Your choice of visualization type should reflect your data so as to convey it in the most effective way. Some of the most common visualization types include tables, line graphs, area charts, scatter plots, pie charts, and heatmaps. Unique visualization types, such as treemaps, may also effectively communicate your data while captivating audience interest.
Provide Context Clues. In making your visualization simple and easy to understand by your audience, it’s important to give them context clues about the background information needed to accurately interpret the visualization. Graphs and charts enable audiences to see patterns in data quickly, but you can’t rely solely on the chart elements to convey your meaning.
Labels and colors can be used to make it easier for audiences to differentiate between values or categories, and captions can be added to provide context or other information that can’t be graphically represented. In some instances, like if you’re presenting the visualization to a live audience, oral clarifications can supplement or replace captions.
Emphasize Important Points. Your data visualization often has to speak for itself, and its success as a messenger is dependent on the audience’s understanding of what they’re looking at. To influence how they interpret the chart, you can choose techniques that focus their attention on key areas of your visualization.
This can be done using pre-attentive attributes, which are visual cues and patterns that your brain recognizes without conscious effort. They include things such as color, size, and added marks.
Size is a particularly useful tool here, as it makes it easy for audiences to immediately grasp the difference between values. Other tools include reference lines to make it easier to compare data, contrasting colors to emphasize differences, using a linear gradient to highlight trends, and strategic use of whitespace. These all help ensure that the most important aspects of your visualization — like values that are significantly above or below average, or dramatic changes in trends — will be the first thing your audience sees.
Having the important details clear at a glance allows the audience to absorb the most significant information without having to think about it.
Avoid Distractions. While highlighting key elements of a visualization is important, it’s just as important to eliminate visual distractions and clutter. These are elements that could unnecessarily add to the overall complexity of the visual, drawing the focus of the audience away from the salient values and figures.
Common chart elements that add to a visualization’s complexity and increase distractions include chart borders, grid lines, markers, unnecessary axis labels, and contrasting elements that don’t serve a specific purpose. Complex or busy fonts can also be distracting.
A good practice for avoiding distractions is iteratively exploring all chart elements for their influence, both positive and negative, on the most important aspects of the visualization. Eliminating distractions in visualizations requires you to explore your visual top-down with the purpose and audience in mind, determining what message is critical and what elements or charts are irrelevant to your purposes. If an element isn’t actively advancing the message of the visualization, it should be removed.
Be specific about the data you’re visualizing — not all data is equally important, and you want to manage the audience’s attention carefully. Data that’s secondary to your purpose can be summarized or removed.
Have a Visual Hierarchy. Having a workflow in mind for your visualization gives you structure and helps you exhibit the message or story being communicated. This is especially important in larger visualizations such as dashboards or infographics.
Having a visual hierarchy of the information you’re showcasing gives your visualization a clear narrative. You can start off by generalizing and explaining average values, go in depth with charts in the middle, and finish with a concise summary. By using things like highlights and context clues, you can direct your audience’s attention through the entire story of your visualization.
Have an Aesthetic. The primary purpose of a visualization is to convey information, but part of holding your audience’s attention is offering something that’s visually pleasing as well as informative. Aesthetics are an often overlooked aspect of visualizations, but strong aesthetic can make your visuals easier to understand — not to mention more eye catching!
The overall aesthetic of a visualization is the product of its elements and their positions, shapes, sizes, fonts, and colors. Color is often the most obvious element of a visualization, making it especially important to choose colors that are harmonious, but visually distinct.
Color can be used to emphasize meaning with the use of highlighted contrasts or linear gradients, classify or categorize differences between values, or associate the visualization with your brand by matching the color schemes. Randomly colored visualizations are often unnecessarily complex and full of distractions that compete for the audience’s attention. Color in visualizations should be used deliberately and strategically.
Other aesthetic considerations are the alignment of elements, the use of white space to provide a visual break, and a sense of balance in the visual aspects of the chart.
Iterate from Feedback. It’s common to get tunnel vision about aspects of your visualization, which can lead to mistakes that will be invisible to you, but glaring to other people. It’s always advisable to include a step for soliciting and integrating feedback in your projects.
Encourage your beta viewers to give their first impressions of the visuals, and ask what they believe the message of the visualization to be. Make sure that they’re taking away the message you intend, and that the most important points are highlighted. Any feedback — from comments about color and alignment issues to complaints about unnecessarily complex analyses — helps you to build a visualization that functions as a strong communication tool.
In this article, you explored eight best practices for creating data visualizations. Data visualization is a crucial aspect of the modern usage of data, and effective visualizations can transform dry, dense data into engaging, accessible messaging.
About the Author
Sooter Saalu is a data professional with over 4 years of experience across Data Analyst, Data Scientist, and Data Engineer roles. With his educational background in Clinical Psychology, he is committed to writing engaging data stories for technical and non-technical audiences.