21 Chapter 12. Best Practices in Data Visualization
Visual communication is just as crucial as verbal communication, yet many researchers overlook this fact. They pour tremendous effort into collecting and analyzing data, uncover compelling insights, and then undermine their work by failing to present it effectively. Instead of leveraging visuals to enhance comprehension, they rely solely on lengthy blocks of text, which not only tires the reader’s eye but also creates a monotonous reading experience. This is a problem, especially in an era where digital content is consumed in short bursts, on social media, in emails, and through bite-sized news updates. If your research lacks visual appeal, it risks being skimmed over or, worse, ignored altogether.
And don’t just take our word for it, cognitive science backs this up. Allan Paivio’s dual-coding theory (1971) suggests that our brains process verbal and visual information separately. When key findings are presented both as text and as visuals, comprehension is reinforced through two distinct cognitive channels, improving retention (Paivio, 1971, 1986, 1991, see also Kanellopoulou et al., 2019). This is why infographics, graphs, and well-designed figures help audiences grasp complex ideas more effectively than text alone.
Cognitive load theory further supports this argument demonstrating that well-structured visuals reduce the strain on working memory thus making information easier to process (Perra & Brinkman, 2021). This aligns with earlier studies by Houts et al. (2006), which found that images significantly enhance attention, comprehension, and recall. The principle is simple: when you pair an idea with a well-designed visual, the brain processes it faster and retains it longer. Dunlap and Lowenthal (2016) take this even further showing that structured scientific visualizations lower cognitive load and are therefore ideal for communicating complex ideas to diverse audiences.
The impact of visuals isn’t just theoretical. A wealth of research confirms that graphics improve engagement, clarity, and learning outcomes. For example, Khoury et al. (2019) highlight how collaborations between scientists and graphic designers produce infographics that improve knowledge dissemination and enhance public engagement with research. Similarly, Franzblau and Chung (2012) stress that figures and tables save readers time and allow them to interpret data quickly and efficiently provided they are designed with clarity and accuracy in mind.
Yet, despite all this evidence, many researchers still treat visual communication as an afterthought rather than an integral part of their work. Rodríguez Estrada and Davis (2015) argue that the issue stems from a lack of visual literacy among researchers. Too often, visuals are either overcomplicated, lacking coherence, or misaligned with the audience’s needs. This is where adopting principles from graphic design can make a significant difference. Effective scientific visuals should be simple, clear, and self-explanatory. They should function independently conveying key messages even if separated from the main text (Franzblau & Chung, 2012). Moreover, the strategic use of color, contrast, and composition can enhance not only readability but also engagement. Divecha et al., (2023) emphasize that well-crafted tables, graphs, and charts break the monotony of text and guide the reader’s eye to the most critical information.
Recognizing the cognitive and communicative power of visuals is only the first step. The next is learning how to design them effectively. Even the most compelling dataset can fall flat if presented through poorly structured or visually confusing graphics. That’s why it’s essential to follow evidence-based design principles that enhance clarity, improve comprehension, and minimize distortion. In the following section, we’ll explore a series of best practices drawn from research in cognitive science, visual literacy, and academic publishing that will help you create visuals that look professional and serve their intended purpose with precision and integrity.
Once you’re familiar with these foundational techniques, the next chapter will introduce you to a range of AI-powered tools that can support and streamline the whole visualization process. You will be introduced to AI tools that will enable you to create various types of visualizations using simple prompts.
Best Practices for Creating Effective Visuals in Academic Research
Over the years, we’ve come to realize that designing effective visuals is as much an essential research skill as writing clearly or analyzing data accurately. In this section, we share some best practices we’ve learned both from the research literature and from trial and error in our own work. These are guidelines that will help you create visuals that are clear, accurate and ethically sound.
1. Choose the Right Format
Not all visuals serve the same purpose, so selecting the right one is crucial. If you’re dealing with structured data that requires precision, a table is your best bet (Franzblau & Chung, 2012). However, if you want to illustrate trends, relationships, or patterns over time, a graph or figure will communicate that information far more effectively. The goal is to match the visual representation to the type of insight you’re trying to convey.
2. Prioritize Simplicity and Clarity
A cluttered or overly complex visual does more harm than good. Your goal is to make your research more accessible, not to overwhelm your audience with unnecessary details (Franzblau & Chung, 2012; Houts et al., 2006). Keep your graphics clean, focus on essential elements, and use labels that enhance clarity rather than confuse. Simplicity ensures that your audience immediately grasps the key message.
3. Avoid Redundancy
One of the biggest mistakes researchers make is creating visuals that merely repeat what they’ve already said in the text (Rodríguez Estrada & Davis, 2015). A well-designed graphic should add value, not duplicate content. Ask yourself: Does this visual provide a new perspective or clarify something complex? If the answer is no, rethink how you’re using it. Keep labels brief and to the point, your audience doesn’t need an entire paragraph inside a figure.
4. Captions Are Essential
A well-designed figure should not stand alone without an accompanying caption that provides context and guidance. Readers should not have to guess what they are looking at or how to interpret the visual elements. A strong caption explains what the figure represents, highlights key takeaways, and, if necessary, describes how the data was collected or analyzed. Think of a caption as the explanation you would give if you were presenting the figure in person, it should anticipate the questions a reader might ask and provide the necessary clarification. Without a well-crafted caption, even a well-designed figure can fail to communicate its intended message effectively (Rougier et al., 2014).
5. Ensure Visuals Stand Alone
Your figures and charts should be self-explanatory (Perra & Brinkman, 2021). Imagine a reader skimming your research, would they understand your visuals without diving into the full text? If not, consider adding clearer labels, captions, or annotations. A strong visual should communicate its message even when viewed in isolation.
6. Consider the Audience’s Context
Who is reading your work? Fellow researchers, policymakers, or the general public? The answer should shape your visuals (Rodríguez Estrada & Davis, 2015; Houts et al., 2006). Avoid highly technical graphs if your audience is outside your field. Similarly, use culturally relevant and inclusive images that resonate with your readers rather than alienating them.
7. Eliminate Unnecessary Details
Just because you can add decorative elements doesn’t mean you should (Houts et al., 2006). Every part of your visual should serve a purpose. If an element isn’t helping convey your message, it’s a distraction. Streamlining your design makes it easier for the audience to focus on what truly matters.
8. Prioritize Message Over Aesthetics
While a visually appealing figure can be engaging, clarity should always be the top priority. The purpose of a research visual is to convey information effectively, not just to look impressive. If a figure is too artistic or complex, it risks distracting from the core message. Researchers should focus on structuring figures in a way that makes data easy to interpret at a glance. This means using clear labels, logical organization, and straightforward formatting. A figure that is simple but effective will always be more valuable than one that is visually striking but difficult to decipher (Rougier et al., 2014).
9. Test for Effectiveness
You might think your graphic is crystal clear but does your audience feel the same? Testing your visuals with colleagues or sample readers can reveal whether they’re intuitive or need improvement (Houts et al., 2006). Small tweaks, like adjusting labels or reordering elements, can significantly improve comprehension.
10. Plan Data Presentation Early
Don’t wait until the final stages of writing to think about visuals (Khoury et al., 2019; Divecha, 2023). If you integrate visual planning early, your research paper will have a stronger, more cohesive structure. Plus, mapping out your data presentation from the beginning helps you identify the best ways to simplify complex information.
11. Eliminate Chartjunk
Chartjunk refers to any unnecessary visual clutter that does not add value to a figure and, in many cases, actually makes it harder to read. This includes excessive labels, overly complex gridlines, flashy colors, unnecessary 3D effects, and decorative elements that serve no real purpose. Every element in a figure should serve a functional role in improving comprehension. Removing distractions allows the essential data to stand out, making it easier for readers to extract key insights quickly. A cleaner, more focused design improves the effectiveness of scientific visuals (Rougier et al., 2014).
12. Ensure accessibility
Not all readers have perfect vision or technical expertise. Use colorblind-friendly palettes, readable fonts, and contrast to improve legibility. Studies show that high-contrast, well-labeled figures significantly improve comprehension, especially for interdisciplinary or public audiences (Pamplona, 2023). If publishing online, add alternative text descriptions for visually impaired users (Viborc, 2025).
13. Acknowledge limitation
If a graph doesn’t tell the full story, say so. Perhaps you’re showing only a subset of data to highlight a key trend just be transparent about it (Shahazad, 2024). Ethical visuals inform without misleading.
Conclusion
Visuals are core components of scholarly storytelling that help readers grasp complex information quickly, intuitively, and accurately. When thoughtfully designed, they reduce cognitive load, reinforce key messages, and open your work to broader and more diverse audiences.
Throughout this chapter, we’ve emphasized why visual literacy matters and shared practical strategies to help you design visuals that are clear, meaningful, and ethically sound. From choosing the right format and simplifying design, to ensuring accessibility and transparency, these best practices lay the foundation for impactful visual communication.
In the next chapter, we’ll build on this foundation by exploring how AI can support and streamline the process of creating effective research visuals. You’ll discover how tools like ChatGPT, Claude, and other AI platforms can help you generate charts, graphs, and infographics using simple prompts thus making high-quality visual design faster, easier, and more accessible than ever before.
