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What are the essential skills and tools for data visualization for R&D professionals?

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Data visualization is the art and science of presenting complex data in a clear, engaging, and meaningful way. For R&D professionals, data visualization can help communicate insights, identify patterns, and support decision-making. But how can you master this skill and what tools do you need? In this article, we will explore some of the essential skills and tools for data visualization for R&D professionals.

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Yuki Nishimura

Associate Principal Scientist
Caio Vaz Rimoli, PhD

Research Engineer @ Institut Curie - INSERM | Biophysical Imaging & Spectroscopy | Data Science Enthusiast

1 Understand your data

Before you can visualize your data, you need to understand what it represents, how it was collected, and what questions it can answer. You need to be able to analyze, clean, and transform your data to make it suitable for visualization. This may involve using statistical methods, coding languages, or software tools to manipulate your data. You also need to be aware of the limitations, biases, and uncertainties of your data and how they affect your interpretation and presentation.

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Shashikala Sasidharan, PhD

Translational Biologist, Biomarker Discovery, Biopharmaceuticals, Neurobiology,Immunology, Oncology,StemCell, CRISPR, Clinical Trials,Regulatory, Microscopy, Automation, High-throughput assay development, DoE, Scaleup
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Always review you data a few times, be aware of bigger picture that the data signifies as well as the finer details of it to come to a more insightful conclusion from the data

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2 Choose the right format

Once you have your data ready, you need to choose the most appropriate format to display it. There are many types of data visualizations, such as charts, graphs, maps, tables, dashboards, and infographics. Each one has its own advantages and disadvantages, depending on the purpose, audience, and message of your visualization. You need to consider factors such as the amount, type, and complexity of your data, the level of detail and interactivity required, and the tone and style of your visualization.

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Caio Vaz Rimoli, PhD

Research Engineer @ Institut Curie - INSERM | Biophysical Imaging & Spectroscopy | Data Science Enthusiast
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The right format in general is the simplest one you can choose that can be promptly understood by the target audience (readers). A good approach is to think in terms of the time needed to have an honest understanding of the key message of the data. From my experience, I always ask myself the following question: "How can I save the time of the target audience while understanding the key message of the data?". For any detail displayed of the data, I ask myself "So what?". In other words, "What is the key message?". Ideally, the presented data should "speak by itself" and should not need an intermediate to explain it.

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3 Design with clarity and impact

The next step is to design your data visualization with clarity and impact. You need to use visual elements, such as colors, shapes, sizes, fonts, and icons, to highlight the key points, trends, and relationships in your data. You also need to use labels, titles, legends, and annotations to provide context and explanation for your data. You need to balance the aesthetics and functionality of your visualization, avoiding clutter, distortion, and confusion. You need to follow the best practices and principles of data visualization design, such as simplicity, consistency, accuracy, and relevance.

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Caio Vaz Rimoli, PhD

Research Engineer @ Institut Curie - INSERM | Biophysical Imaging & Spectroscopy | Data Science Enthusiast
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Data should be displayed in figures in an honest clear way. For that, key information should be unambiguous and not redundant (appear only 1 time). Organize the data aiming at delivering key messages. The data presentation should be as simple as possible, following the "Tufte Principle" (see Valiela 2009), where one should maximize the data information and minimize the details to present them. Avoid data decoration if they don't deliver a message. Data points and data texts should be easily readable. If clusters of points are overlapped, choose empty circles to display them or semi-transparent circles so that you can see a single data point and also many overlapped points (darker color for denser overlapped regions).

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4 Use the right tools

To create your data visualization, you need to use the right tools that suit your needs and preferences. There are many tools available, ranging from simple to advanced, from free to paid, from online to offline. Some of the popular tools include Excel, Tableau, Power BI, R, Python, D3.js, and Adobe Illustrator. Each tool has its own features, benefits, and limitations, depending on the type, format, and complexity of your data visualization. You need to experiment with different tools and find the ones that work best for you.

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5 Test and refine your visualization

The final step is to test and refine your visualization before you share it with others. You need to check your visualization for errors, inconsistencies, and ambiguities. You need to solicit feedback from your peers, colleagues, or clients, and see how they interpret and react to your visualization. You need to make adjustments and improvements based on the feedback and your own evaluation. You need to ensure that your visualization is clear, accurate, and effective in conveying your message.

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Caio Vaz Rimoli, PhD

Research Engineer @ Institut Curie - INSERM | Biophysical Imaging & Spectroscopy | Data Science Enthusiast
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Integrity is the key. Depending on how you display the data, you might convey extra information that can confuse the target public (readers). The public should not waste time in understanding the key message that the data is displaying. If all your information can be represented in a 2D plot, for example, don't force it to make it a 3D plot, because the extra dimension dilutes the importance of the key messages that need to be delivered. Any extra detail will also be noticed by the target public (readers), and they will naturally need more time to process the irrelevant information added before reaching a conclusion on whether that is important or not. Therefore, keep it simple and clear.

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6 Keep learning and improving

Data visualization is a dynamic and evolving field that requires constant learning and improving. You need to keep up with the latest trends, techniques, and tools in data visualization. You need to explore different sources of inspiration and examples of good data visualization practice. You need to challenge yourself and experiment with new ideas and approaches. You need to develop a critical eye and a creative mind for data visualization.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Yuki Nishimura

Associate Principal Scientist
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-understand the difference between control limits and specification limits -determine how much variation is coming from your operators and measurement tools to understand your manufacturing variations -there is no such a thing as a fluke. Determine the assignable cause -always label your units -always write out the acronyms at least once -5 why’s don’t have to stop at step 5

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What are the essential skills and tools for data visualization for R&D professionals?

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1 Understand your data

2 Choose the right format

3 Design with clarity and impact

4 Use the right tools

5 Test and refine your visualization

6 Keep learning and improving

7 Here’s what else to consider

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