Data visualization is the better way to represent huge amounts of data in a simple and intuitive fashion.The drag-and-drop features of BI tools like Power BI and Tableau have made data visualization simpler and possible to use for different types of users. But, it has also given birth to many bad data visualization examples.

To avoid data visualization errors, it’s required that you strike the right chord between data points and presentation. Using too many colors, incorrect charts, and presenting too much information through one graph are some of the mistakes that data analysts often make.

Another very bad example of representing graphs is missing out on labels. Evident labels make the graph easy to understand, and as a viewer, you will have no scope of ambiguity. For instance, in Power BI, after preparing a chart, you need to turn on the data labels option from the “format” section. Since it can be easily missed out, it’s advisable to double-check on data labels.

Just implementing data labels also isn’t enough; they should be decipherable. For instance, if labels are inside the visuals, using contrast colors help. Imagine while presenting your retention rate; if the percentage is visible as 8 instead of 80, what impression it’ll have on viewers’ minds!