data visualization

6 Best Practices for Data Visualization

Over the past few years, capabilities for visualizing data have expanded greatly – and just as the Internet has democratized the exchange of information, cutting-edge visualization tools are democratizing the ability to represent data graphically.  But democratization doesn’t guarantee quality; creating a good visualization that conveys information clearly, concisely, and in context requires a lot more than the ability to create a graph with a few clicks.

So, what differentiates a great visualization from a mediocre one?  The measure of a good visualization is how well it communicates information quickly to someone who hasn’t previously seen the viz.  Here are six visualization best practices to help you take your presentation of information up a notch.

  1. Fulfill the Purpose

Ultimately the most important aspect of any visualization is that it achieves its purpose.  At the most general level, visualizations accomplish one of three things:  1) they enable exploration of data to allow new discoveries; 2) they enable a better understanding of complex or large amounts of data; or 3) they communicate information to others.  Define the purpose of your visualization and you can focus on achieving that objective.

  1. Consider Your Audience

Knowing who will be using your visualization is just as critical as knowing its purpose. You should consider your audience and their degree of familiarity with the subject, the underlying data, and their comfort level with charts and graphs.  The presentation of the information must be readily understood by those who will use it.

  1. Provide Context

A friend of mine has a great definition of truth: facts in context.  Facts alone aren’t enough.  This applies to visualizations as well.  If your audience doesn’t know the source of the data, when it was collected, or how it relates to them, it will lack meaning and impact.  Ensure that your visualization contains supporting information that provides answers to all the questions that your audience might have.

  1. Show the Numbers

One of the benefits of visualization over spreadsheets is showing a lot more information in less space and without having to show every number.  Nonetheless, the numbers are still important and should be shown for all critical components of the viz.

  1. Present the Most Important First

Make sure you present the most critical information first.  This applies both to displaying the overall layout as well as to sorting data that is shown within each element of the viz.

  1. Pay Attention to Aesthetics

Overall aesthetics are also very important for good visualizations.  They make vizzes more appealing and increase their receptivity.  Make sure the overall presentation of your visualization is neat and crisp – with effective use of color, aligned components, and consistent use of fonts, size, and placement.

There is a lot to creating a great visualization – and the tools available today make it a lot easier to produce eye-catching vizzes.  But even with these tools, careful design and implementation is required to ensure that your viz hits its mark.  Applying the tips outlined above will put you on the path to creating visualizations that not only inform but do so quickly and easily – and are appealing too.

What are your best practices for data viz? Share your thoughts in the comments!

One thought on “6 Best Practices for Data Visualization”

  1. One of the biggest challenges for effective and actionable data visualization is accurate data management by expert data processing teams. The accuracy of data plays a critical role, as if it is not accurate, any and every visualization derived would be biased or skewed. Data visualization or business dashboards are usually created on the move, with data getting added just because different individuals in a company want different numbers to be displayed. At times it is done because there is some white space left, which is not being used. All this put together invites a situation where reading the visual dashboards becomes cumbersome, as they would be full of meaningless non-relevant information, ultimately making the dashboard useless.

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