Data in its raw form is often convoluted, making it very difficult to draw meaningful conclusions. Since there is a huge amount of data to look through it can be difficult to immediately see the important information that you are searching for. Good data formatting will allow your visualisation to be easy to interpret and understand.  

Choosing Charts

Choosing the chart best suited to what you want to represent. Choosing the right chart type can make all the difference. Simple is often most effective, sometimes all you need is a simple bar chart!

Bubble charts are an example of a chart that may look interesting at first glance but can be difficult to draw significant conclusions from.

Here, we do not have much information. All that is known is the names of the sub-categories since there isn't context behind the size of the bubbles. In this case the size of the bubbles represent total profit for each bubble, however, it is difficult to decipher which sub-category actually has the largest profit based on this chart. Similarly, sub-categories which made a loss instead of a profit could not be represented in this chart since there is not an obvious way to represent negative numbers in this type of chart.

On the other hand, using a bar chart in this scenario easily displays highest to lowest profit. It is easy to immediately tell that 'Bookcases' have the highest profit of all sub-categories despite it being very close at the top. This will also allow the sub-categories with a loss to be displayed as bar charts are able to clearly display negative values.  

Choosing the correct chart makes reading the information you want to display much easier for your audience.

Highlighting Key Information

Highlight the information you want your audience to focus on, whether this is profit from a particular year or the products that are causing the biggest losses. Highlighting can be done with colour. The aim is to draw your audience to the information you want them to know.

In this example 'Tables' have made a loss and in order to make this information clear as opposed to the other values it is coloured red whilst the others are grey.

Filters can also be used to remove any information that may not be important in your analysis. In the example below a filter has been applied to retain only the sub-categories within the 'furniture' category.

With less bars to focus on, it is clear that there is a large difference in profit within the 'furniture' category. This is useful to know if your audience wished to know specifically about the profit within the sub-categories of 'Furniture'.

Cleaning and Finishing

Finally cleaning your visualisation is vital. Remove any grid lines which don't aid the audience in understanding your data. Ensure colours don't clash and have a purpose such as to highlight important information. Change font sizes to ensure all information is clear to your audience. Focus on making your chart as easy to understand as possible by adding all necessary information including titles and labels.

Now the bars have been labelled it is clear exactly how much of a profit or a loss each sub-category made. There is also a title which helps to explain what the chart is showing.

This gives us the final chart. A simple and clean yet effective analysis on profits within the sub-categories of 'Furniture'.