What Is Data Visualization?

Data visualization is a powerful way to transform raw data into a chart or graph format, allowing viewers to understand complex datasets more intuitively.

In most cases, raw values in a dataset are not as important or actionable as the trends and comparisons buried in the data. Graphs and other data visualization tools are a way of "seeing" your data, enabling you to identify patterns, trends, and correlations that might go unnoticed in text-based formats.

Think of a good data graphic like a translator, taking the complex language of numbers and statistics and deciphering it into a visual story that's easier to digest, interpret, and action upon.

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Create clear, beautiful visualizations of your research data with BioRender Graph. Learn more today.

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Advantages and Disadvantages of Data Visualization

‍Humans evolved a keen ability to identify patterns and colors easily and rapidly. Our ability to work with and interpret numbers came much later in our evolutionary history, thus requiring much more cognitive bandwidth. It’s no surprise that much of culture and entertainment is based in visual storytelling, from art and advertisement to TV and movies.

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Advantages of Data Visualization

‍The advantages of data visualizations are intuitively obvious:

• They simplify and condense complex datasets, making them easier and faster to interpret
• They highlight key differences, similarities, patterns, trends, and outliers
• They enable accessible democratization of information to wider audiences
• They enable engaging and memorable storytelling with data
• With modern tools, they can be interactive and dynamic

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Disadvantages of Data Visualization

‍The potential disadvantages of data visualizations are a lot less obvious. Importantly, these disadvantages are mostly due to poor design choices or incorrect graphic selection. As we breakdown data visualization best practices in the following blog posts, keep these potential pitfalls in mind:

• They are liable to misinterpretation or bias if poorly designed
• They can cause more confusion that clarity if overcomplicated or oversimplified
• The core message can be lost if not carefully highlighted in graphic

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Why Is Data Visualization Important?

‍Good practices in data visualization are essential, especially in the modern research landscape where datasets are often large and complex. A well-designed visualization can make the difference between clear, actionable insights and a missed opportunity for discovery. It can help researchers identify patterns or anomalies that could lead to breakthroughs, and it can make research findings more accessible and understandable to a broader audience.

From presentations, to posters, to journal articles, to grant applications, the importance of good data visualization can’t be overstated. While technical writing skills are often a key area of professional training for scientists, building strong visualization skills is often neglected. Rest assured, you’ve come to the right place! At BioRender, we’re passionate about enabling scientists to effectively communicate their research through visuals, whether scientific figures or graphs.

In the chapters to follow, we will outline key steps in ensuring effective and beautiful data visualizations. We’ll consider selecting the right type of visualization for your data, ensuring that your stands out and drives the story, fine-tuning elements (i.e. text, colors, lines, etc.) for optimal clarity and accuracy, and other topics!

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Read the next chapter  |  Return to the table of contents

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References

1. Data Visualization: a practical guide to producing effective visualizations for research communication by Rebecca Wolfe (2014. RESYST Consortium)
2. Tableau article: What Is Data Visualization? Definition, Examples, And Learning Resources