Key Design Principles Now That We Know What We Mean by Data Visualization, Let's Talk About What Makes a Visualization Useful For good reason, data visualization has grown to be an essential component of data analysis. The need to swiftly and effectively share insights has never been stronger due to the ever-increasing amount of data. Nevertheless, not every type of data visualization is the same. Some are deceptive, while others are reliable and wise. What then makes a visualization effective? We'll look at the three characteristics of effective data visualization in this article: reliability, usability, and elegance. Trustworthy When we state that a visualization is reliable, we imply that the information it presents is accurately represented. For instance, there should be evidence for a relationship in the data if you are displaying anything in a way that suggests a relationship, trend, or correlation. If not, you will only be misrepresenting your readers. Let's look at an example of a potentially misleading visualization. Suppose you're at a business meeting and shown a figure that displays revenue growth for a company over the course of a year. The title of the figure is "Revenue Surge in 2016." However, when you look at the graph, you notice that the y-axis is zoomed in on just a small part of the graph, showing only a roughly 2% increase between Q1 and Q4. This is hardly a surge. In fact, it's possible that the author of this graph is trying to be dishonest or, at the very least, misleading. They're trying to convince you of something that isn't true. A more accurate representation would plot previous years' profits for comparison and set up the y-axis honestly. This would give a clearer picture and a better understanding of how revenue growth has changed over time. Accessible Accessibility is another feature of good data visualization. It should be simple to grasp and not require a lot of prior knowledge to use the visualization. Without spending a lot of time figuring out the visualization, the target audience should be able to comprehend the facts and the message being presented. One way to ensure accessibility is to use clear and concise labels, titles, and legends. The colors used in the visualization should be chosen with care to ensure that they are easily distinguishable and do not create confusion. In addition, the visualization should be free of clutter, with unnecessary elements removed. Elegant Elegant design is the ultimate characteristic of good data display. A beautiful representation that also effectively and succinctly communicates information is called an elegant visualization. Visually appealing visualizations should have a unified, appealing design that serves to focus the viewer's attention on the most crucial components. To achieve elegance, it's important to choose the right type of visualization for the data being presented. A scatter plot, for example, might be more appropriate for showing the relationship between two continuous variables, while a bar chart might be better for showing the frequency of categorical data. Conclusion In conclusion, efficient data analysis requires effective data visualization. It's crucial to make sure that your data visualizations are reliable, usable, and beautiful if you want them to be effective. You may produce visualizations that properly and effectively convey insights to your audience by adhering to these three criteria. Last but not least, we suggest Andy Kirk's book as a fantastic resource for all things data visualization. In-depth instructions on how to produce excellent representations are given in the book, along with suggestions on how to pick the best style of visualization for your data, choose the proper colors and fonts, and arrange your data in a way that makes it simple to interpret. If you want to learn more about this field of study, we highly recommend it.