4 Best Practices in Data Visualization

4 Best Practices in Data Visualization

There are many times when data visualizations can prove useful for simplifying information. While there are some clear benefits to presenting data through visualizations, it’s important to gain an understanding of how to think about data visualization in order to convey ideas more effectively. 

Here are four best practices in data visualization

Ensure Universal Usability

Data visualization tools are some of the most useful types of business intelligence. This is dependent, however, on whether or not people are really deriving value from the visualizations themselves. 

Not all data visualizations are created equal. There are two underlying elements that need to be considered in order to create impactful data visualization tools:

  • The visualization needs to convey some kind of information that couldn’t have been expressed more efficiently through text alone.
  • The audience needs to be able to grasp the content and purpose of the visualization without needing to make an extra effort. 

These two concepts, when taken together, can help individuals create far more effective data visualizations. Make sure you’re thinking about these before you even start putting together a data visualization. If you don’t come from a data-specific background, it’s a good idea to familiarize yourself with the different kinds of data analysis and types of visualizations. This will help you find a visualization that better fits your needs.   

Take Care in Choosing Charts

As mentioned in the previous section, the presentation of data in visualizations plays a major role in its effectiveness. Therefore, it’s crucial to understand how presentation can affect comprehension. 

The main distinction between types of data is qualitative versus quantitative. This is also sometimes referred to as categorical versus numerical. From here, numerical variables can be broken down into discrete versus continuous. Discrete means there can only be certain, set numbers, while continuous variables can fall anywhere on a spectrum as long as it’s a real number. 

Additionally, categorical variables can be separated into nominal groups, which don’t have any particular pattern,. However, they do exist along with ordinal values, which have a more distinct grouping dynamic. 

By fully understanding these different types of variables, it becomes easier to match data analysis with an appropriate visualization. 

Allow for Deeper Discovery

It should go without saying that, while important, data visualization is only a starting point for making business decisions. Charts and graphs are great, but they aren’t going to be actionable unless they allow for deeper discovery. This is where interactive data visualization can come into play. 

Analytics platform providers such as ThoughtSpot have created interactive data visualization tools that allow non-experts to extract useful insights in far less time. Part of this is the ability to automatically generate visualizations based on search-based analytic queries. With this, individuals can think less about whether a chart makes sense, and more about addressing the actual problem at-hand. 

Additionally, interactive data visualization can help guide users toward other important questions. The smart query generator can help you think about problems in new ways, while automatically spitting out best-fit visualizations. It’s important to consider how data visualizations are going to allow you to drill down to achieve greater insights, not just how they look on paper or a screen. 

Carefully Consider the Audience

Never forget your audience when creating data visualizations. At the end of the day, this is one of the most important considerations. This goes beyond just whether or not people are able to understand the information being presented to them. It needs to address why the information being presented needs to be in the form of a data visualization. 

Data visualizations are about taking ideas and making them more digestible. This is only going to work if comprehension is truly improved by the visual format. Otherwise, data visualizations aren’t providing any added value to the audience.

There are many instances where data visualizations can be an effective tool. It’s important, however, to understand the best practices for getting the most out of data visualizations.  

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