How can you visualize R&D data better?

1 Choose the right type of visualization

The first step to visualize R&D data better is to choose the most suitable type of visualization for your purpose and audience. Depending on the nature and complexity of your data, you may need to use different kinds of charts, graphs, or diagrams to show relationships, patterns, trends, or distributions. For example, you can use line charts to show changes over time, bar charts to compare categories, scatter plots to show correlations, or pie charts to show proportions. You can also use more advanced types of visualization, such as heat maps, network diagrams, or treemaps, to display multidimensional or hierarchical data. The key is to select the type of visualization that best matches your data and your message.

2 Use clear and consistent design principles

To visualize R&D data better, it's important to use clear and consistent design principles that enhance the readability and aesthetics of the visualization. This includes using appropriate colors, fonts, and symbols that are easy to distinguish and interpret, as well as labels, legends, titles, and captions to provide context and explanation. Additionally, scales, axes, and grids should be used to show measurements and reference points. To create a harmonious layout, white space, alignment, and balance should be used while contrast, emphasis, and hierarchy can be used to highlight the most important information. To avoid confusion or misleading viewers, clutter, noise, and distortion should be avoided.

3 Incorporate interactivity and storytelling

The third step to visualize R&D data better is to incorporate interactivity and storytelling elements that engage and inform the viewer. Interactivity allows the viewer to explore, manipulate, or customize the visualization according to their needs and interests. For example, you can use filters, sliders, buttons, or menus to enable the viewer to select, zoom, or drill down into the data. You can also use animations, transitions, or annotations to show changes, comparisons, or causations in the data. Storytelling allows you to convey a narrative, a message, or a recommendation based on the data. For example, you can use headlines, subtitles, or summaries to provide an overview or a conclusion. You can also use scenarios, examples, or questions to illustrate or provoke the viewer's curiosity or action.

4 Use the right tools and platforms

When it comes to visualizing R&D data, the fourth step is to choose the right tools and platforms that suit your needs and goals. R&D data visualization tools and platforms range from simple to sophisticated, from free to paid, from desktop to web-based. Excel is a widely used spreadsheet software that offers basic to intermediate visualization features, while Tableau is a powerful and user-friendly software that enables advanced visualization, interactivity, and storytelling. Power BI is a cloud-based service that integrates with various data sources and provides comprehensive visualization, analysis, and collaboration features. For programming language enthusiasts, R offers extensive visualization, statistical, and machine learning capabilities while Python also offers rich visualization, data science, and artificial intelligence features. Depending on your data, skillset, and budget, you can choose the tool or platform that best fits your needs. You can also use multiple tools or platforms to complement each other and create more diverse and dynamic visualizations.

5 Test and improve your visualization

The fifth step to visualize R&D data better is to test and improve your visualization based on feedback and evaluation. You can ask yourself or others questions such as if the visualization is clear, accurate, and relevant; does it convey the intended purpose and message; does it appeal to the target audience and context; does it invite exploration and interaction; and does it inspire insights and actions? To improve your visualization, make adjustments or enhancements based on the answers to these questions. Additionally, use metrics or indicators such as views, clicks, or shares; comprehension, retention, or recall; satisfaction, engagement, or enjoyment; and decision-making, problem-solving, or innovation to measure the effectiveness and impact of your visualization. This will help you compare different versions or alternatives of your visualization and identify the best practices and opportunities for improvement.

6 Keep learning and experimenting

The sixth and final step to visualize R&D data better is to keep learning and experimenting with new ideas and techniques. R&D data visualization is a dynamic and evolving field that requires constant curiosity and creativity, so it's important to stay up-to-date on the latest trends. You can do this by reading books, blogs, or articles; taking courses, workshops, or webinars; joining communities, forums, or events; following experts, influencers, or practitioners; reviewing examples, cases, or trends; trying new tools, platforms, or features; and challenging yourself, your team, or your organization. By keeping learning and experimenting with R&D data visualization, you can discover new ways to create more value and impact from it.

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?