This includes using AI and machine learning, as well as creating your charts with your target audience in mind.

Create with your target audience in mind
When deciding on how to improve data visualization, consider what your audience will be most interested in. This will increase the likelihood that they will act on the information presented. This will also help you avoid making a mistake, which can be costly to your company.

Using color in your visualization will make your point more clear to your target audience. Use intuitive color combinations and match the colors to the subject of the information. For example, if you are presenting a state map, choose red for hot and blue for cold. This will help viewers identify the state in a visual chart.

For more information on the topic, read about LiftedViz CTO John Bremer's best practices for data visualization. His tips for creating a good data visualization will show you what to include, what to remove and what to do with the data you've collected.

The most effective data visualizations tell a story. This includes using arrows to guide readers' eyes to key insights and using text to clarify what is being displayed. However, this should be done only after considering the context of the presentation.

Order your data logically and consistently
If you are lucky enough to be a data geek you may have heard of data viz, a data visualization software suite. The dataviz is the brainchild of data and analytics guru, ahem, Darren O'Grady, a name well known to data junkies, and geek worthy aficionados alike. The resulting suite of products and services is a veritable juggernaut, with the aforementioned Darren being the glue that binds them all. Fortunately for the dataviz improving data visualization, it is a one stop shop for data viz and data management needs. As such, it is only natural that O'Grady and his crew would be able to assemble a dataviz data viz squad on the fly. Thankfully, O'Grady has a plethora of talented savants, from dataviz to business viz to tech viz a-z to boot.

Don't truncate axis baselines
If you are looking to improve your data visualization, one of the most important things you can do is to avoid truncating axis baselines. By doing so, you can highlight differences and create a more effective message. This can make your graphs more efficient and understandable.

In order to use a truncated y-axis effectively, you need to be sure that it doesn't distract the reader from the story. This isn't always easy to achieve, however.

When you truncate the y-axis, you're essentially tricking the viewer into thinking that the difference between the two lines is greater than it actually is. This is because it eliminates any white space and creates an optical illusion. It can also be confusing to the reader because of the way the y-axis labels are set.

If you want to avoid truncating the y-axis, you can use a trend line chart instead. This is a more effective visual metaphor for truncated axes. You can also make it clear that the y-axis has been truncated by drawing a break symbol in the middle.

Use AI and machine learning tools
If you're looking for ways to improve data visualization, then you may want to consider using AI and machine learning tools. These powerful tools help you draw more insights from your data, resulting in better business decisions.

Machine learning algorithms speed up the data discovery process, and enable more accurate predictions. These tools also help you build more targeted visualization tools.

Besides the basics, a machine learning component can teach your data visualization software to search for deeper connections. By running through millions of data points in seconds, a machine can uncover more profound insights than humans could.

For example, retailers analyze their data to identify patterns and implement marketing campaigns. They can then personalize the shopping experience for each customer. In addition, predictive analytics can also be used to increase customer satisfaction, reduce risks, and manage work.

Getting granular with user data can give you a more detailed understanding of a product chain or logistics system. Combined with machine learning frameworks, granular data can create a competitive edge and boost your predictive modeling.