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Best Practices in Data Visualization for Effective Communication

We have discussed here on previous occasions how data visualization has proven to be an effective method of communicating key ideas and critical messages to a wider audience. However, some have this notion that putting in visual elements at any part of a presentation or other materials will make such material more engaging and compelling to read at the very least. The truth is that data visualization is more than just adding visual elements. It is about knowing what elements to choose and where to put it that makes the message more engaging and compelling to its audience.


In essence, data visualization should answer vital strategic questions, provide real value, and help solve real problems. It should be presented in a way that its target audience can relate to very well, thus not only understanding the idea presented by data but also encouraging them to act in a manner that the data may envision.


To achieve this, the data visualization to be employed needs to be visually appealing. Here are some best practices that will ensure this goal.


Use the right visuals


This not just refers to the images to be used in the presentation but more importantly, the charts. There are different types of charts that serve different purposes in terms of data visualization. For instance, line charts should be used to compare values over time and are excellent for displaying both large and small changes, bar charts should be used to compare quantitative data from several categories and excellent for displaying more significant changes, scatter plots should be used to display values for two variables for a set of data and are excellent for exploring the relationships between the two sets, and pie charts should be used to show parts of a whole such as a share of an entire item or subject. It is important that the charts to be used perfectly illustrate the story that the data conveys.


Make it organized and coherent


Coherence is especially important when compiling a big data set into a visualization, enabling users to understand and process information more easily while not being “in-your-face” about it. Creating a hierarchy of data can also help in data visualization as it better shows the various data points in a relevant way for decision-makers. You can sort from highest to lowest to emphasize the largest values or display a category that is more important to users in a prominent way. Presenters can also arrange visual elements in order such as by size or color shade to help users interpret data more easily


Inclusive visualization is the key


Color is used extensively in data visualization that it is often taken for granted. As such, presenters would often choose colors at random without realizing intricacies such as how different shades of color might not be distinguishable for certain users, especially those who are color-blind,  and thus would not be able to comprehend the ideas the data seeks to impart. Presenters should ensure that the colors used are easily distinguishable for all users viewing data. Legible text is also helpful in providing additional context to the visuals


Avoid distortions


There is a danger for data visualization to lead its audience towards certain conclusions, especially if certain visual elements are emphasized more prominently than others or the use of visual elements that are not appropriate to effectively convey the data’s message. This in turn can hurt the credibility of the presenter and the data itself. Presenters need to strike a balance in highlighting certain information without distorting the data overall and ensure its accuracy.


Good data visualization makes effective use of graphics to communicate data clearly and effectively. They take complex information and break it down in a way that makes it simple for the target audience to understand and on which to base their decisions. Data visualization done right simplifies and improves the process of designing infographics that are genuinely useful to their audience.


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