How To Guides
1 min read
Last updated:
Data visualisations allow users to organise and present log data in a practical, usable, and sensible manner. Data visualisation software ensure that the data collected communicates real-time, actionable insights that will support timely and informed decision-making. Knowing which types of visualisation best suits a particular data set is critical in giving data visualisation optimal business value.
Here is how to pick the right type of log data visualisation.
Pie charts
Use them when you want to compare parts to the whole. They can help the audience to understand the relative importance of specific figures. However, they are not recommended when comparing more than five sections. Too many sections can mean that the segments may be too narrow, making the chart difficult to interpret.
Stacked pie chart
This can be used to see how two sets of data relate to each other. The first range of data falls in the inner ring and compares with the data in the second range, which is in the outer ring of the pie.
Tabular format
Consider this format when you want to know exact numbers or quantities. It uses columns and rows to present the figures, and a summary may be contained in PivotTables. Please note that this method is not ideal for comparing data sets or finding trends.
Bar charts
Use this when you want to compare various categories. One axis can represent categories being compared and the other one shows discrete values. The bars can be plotted vertically or horizontally, and are proportional to the values they represent.
Line charts
Consider this when trying to visualise continuous data over time. It is usually set against a common scale to show trends in data over a particular period. You can add a goal line or trend line to compare the actual performance against a set benchmark for the period.
If you enjoyed this post on how to pick the right type of data visualisation then why not check out our guide on hidden vulnerabilities on your IIS servers or everything you need to know about DevOps.