Data visualizations are powerful tools to communicate complex information, reveal patterns, and tell stories. But how can you design them in a way that is user-friendly, intuitive, and engaging? In this article, you will learn some basic principles and best practices for creating effective data visualizations in your software.
The first step to design user-friendly data visualizations is to understand who your audience is, what they need, and how they will use your software. Different users may have different levels of familiarity with data, different goals, and different preferences. For example, a business analyst may want to explore and interact with the data, while a manager may want to see a summary and insights. You should tailor your visualizations to suit your audience's needs, expectations, and context.
It is important to ask people what they want too, rather than making assumptions about what would be useful to them - especially if tailoring visualisations to multiple audiences. If it is not possible to ask individuals, then build relationships with key stakeholders who represent different groups of people who will interact with your visualisationsz
The next step is to choose the most appropriate type of visualization for your data and message. There are many types of visualizations, such as charts, graphs, maps, tables, dashboards, and infographics. Each one has its own strengths and weaknesses, and can convey different aspects of the data. For example, a bar chart can show comparisons, a line chart can show trends, and a pie chart can show proportions. You should select the type of visualization that best matches your data type, dimensionality, and purpose.
Keeping it simple and to the point is really important when working with non technical audiences. It may be fun to create complicated charts and graphics to represent data, but if they are too complicated or unfamiliar then they risk being rejected by the intended audience.
The third step is to use clear and consistent design elements, such as colors, shapes, fonts, labels, legends, and axes. These elements can enhance or hinder the readability, comprehension, and aesthetics of your visualizations. You should use them wisely and sparingly, avoiding clutter, confusion, and distraction. For example, you should use colors to highlight important data points, shapes to distinguish categories, fonts to emphasize titles and labels, and legends and axes to explain the data.
This is very important - you don't want to distract someone by inconsistencies in graphics. Also colour can be really important to, but it is important to take into consideration things like colour blindness and different cultural associations of colours.
The fourth step is to apply some general data visualization best practices, such as using accurate and relevant data sources and citing them whenever possible. Make sure to use appropriate scales and units, and avoid misrepresentations of the data. Titles, captions, annotations, and tooltips can provide context and explanation for your visualizations. Interactive features like filters, sliders, zooms, and drill-downs can enable user exploration and customization of the data. Animation and transitions can be used to show changes and dynamics in the data. Finally, it is important to test and evaluate your visualizations with your audience, collecting feedback and suggestions for improvement.
Always ensure your visualisations present an honest depiction of the data that you are presenting. People misusing data to make a point undermine people's trust in data and this can impact on the efforts of others.
The fifth step is to learn from examples and resources that can inspire and guide you in designing user-friendly data visualizations. There are many online platforms, blogs, books, courses, and podcasts available for this purpose. Tableau Public is a platform that allows you to create and share interactive data visualizations online. FlowingData is a blog that covers topics and projects related to data visualization and analysis. Edward Tufte's classic book, The Visual Display of Quantitative Information, presents principles and examples of effective data graphics. Kieran Healy's book, Data Visualization: A Practical Introduction, teaches how to use R and ggplot2 to create data visualizations. Finally, the Data Stories podcast features interviews and discussions with data visualization experts and practitioners.
The final step is to keep learning and improving your data visualization skills and knowledge. Data visualization is an evolving and interdisciplinary field that combines art, science, and technology. You should always be curious, creative, and critical when designing and evaluating your data visualizations. You should also seek feedback, collaboration, and inspiration from other data visualization enthusiasts and professionals. By doing so, you will be able to design user-friendly data visualizations that can inform, persuade, and delight your software users.
Apply a continuous improvement cycle to your visualisations by inviting feedback, both positive (so you know where to focus future efforts) and constructive feedback to help you know where to focus your improvements.