Data visualization is the art and science of presenting data in a clear, engaging, and meaningful way. It can help you communicate insights, tell stories, and persuade audiences. But how do you ensure that your data visualization is accurate, relevant, and ethical? Here are some strategies to guide you.
Not all charts are created equal. Depending on your data and your goal, you need to select the most appropriate type of chart that showcases your data without distorting or misleading it. For example, if you want to compare proportions, a pie chart or a stacked bar chart might be suitable. If you want to show trends over time, a line chart or an area chart might be better. If you want to explore relationships, a scatter plot or a bubble chart might work. Always consider the context, the audience, and the message of your data visualization.
The best tool I know for selecting the right type of chart is the Financial Times Visual Vocabulary. It organizes 74 common charts, maps, and diagrams across 9 main activities (e.g. correlation, deviation, ranking, etc.). But if you want to dive deeper into the science of data visualization, consider reading Principia Pictura (disclaimer: I am the author).
I believe the Financial Times Visual Vocabulary offers an excellent method for selecting appropriate charts. Summarizing the most common choices: -Line Chart: Depict trends over time. -Bar Chart: Compare data across categories. -Pie Chart: Illustrate parts of a whole. -Scatter Plot: Visualize variable correlations. -Histogram: Represent data distribution. -Heatmap: Display matrix data patterns. -Gantt Chart: Manage project timelines. -Radar Chart: Compare attributes across categories. -Box Plot: Portray distribution characteristics. -Stacked Area Chart: Illustrate cumulative data effects. -Bubble Chart: Visualize three-dimensional data. Choose based on your data and desired insights. I also recommend book (Storytelling With Data)
Color and design can make or break your data visualization. They can enhance or detract from your data, convey or confuse your message, and attract or repel your audience. You need to use color and design wisely to create a data visualization that is aesthetically pleasing, easy to understand, and consistent with your brand and purpose. For example, you can use color to highlight key points, create contrast, or show categories. You can use design elements such as fonts, icons, labels, legends, and titles to add clarity, hierarchy, and emphasis.
When discussing design, I lean towards favoring 2D charts over 3D charts. 3D charts often have the potential to misrepresent data, leading to challenges in accurately comparing values. Moreover, they can demand more cognitive effort from viewers as they grapple with the additional dimension. However, there are scenarios where 3D charts can be effective, especially when illustrating multidimensional data that can't be adequately conveyed in 2D. While employing 3D charts. The choice of chart colors depends on the context and purpose of your data visualization. Generally, it's important to choose colors that are easily distinguishable and do not cause confusion.
Consistency is key when it comes to color and design in data visualization. If you use a specific color for a category, stick with it throughout your dashboard or even across multiple dashboards. This helps your audience quickly recognize and understand what they're looking at. And be cautious with colors that typically signal alerts, like red and yellow. Use them only when you really need to draw attention to issues or problems.
Data visualization carries certain challenges and risks, such as using 3D effects, shadows, or gradients that add complexity and distortion, as well as too many colors, fonts, or elements that create clutter. Inaccurate scales, axes, or baselines can alter the perception of the data, while inappropriate or inaccurate data sources, methods, or calculations can result in errors or biases. Furthermore, using charts that do not match the data type can be misleading; for example, using a pie chart for continuous data or a line chart for categorical data. It is important to be aware of these pitfalls to ensure the accuracy and credibility of your data visualization.
Avoid using 3D charts; they're as outdated as fancy fonts in Word. Stick to clean, simple designs that make your message clear and easy for your audience to understand. This way, you eliminate any distractions and focus solely on the data.
The final step in creating an accurate data visualization is to test and refine it. You need to check your data visualization for errors, inconsistencies, and gaps. You need to ask yourself if your data visualization is clear, relevant, and ethical. You need to solicit feedback from your colleagues, clients, or users. You need to make adjustments and improvements based on your findings and suggestions. You need to test and refine your data visualization until you are confident that it meets your standards and expectations.
Data visualization is a powerful tool for data analysis, but it requires careful planning, execution, and evaluation. By following these strategies, you can create accurate data visualizations that deliver your message effectively and efficiently.
When creating data visualizations, clarity is key. Let's take an example of a performance indicator. Ensure that all displayed numbers in different sections add up accurately to the overall value. This step is often overlooked but is crucial for user understanding. People naturally add up numbers to match visuals. Mismatched numbers lead to confusion. Developers must double-check to align all numbers across visuals. This prevents report and visual failures, promoting accurate insights.
Storytelling Data visualisation is story telling in essence. It’s making your data attractive and informative. Data visualisation if done effectively can enhance the analytical objective which goes EVS amazing insights. Choice of charts color n sequencing decides the efficiency . Most important aspect is to make it interactive it should be able to change at the different filter values at click of a button