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Tree Map charts are often undervalued and underused for data analysis. If used as is they do not always provide an optimal results. In my blog I will explain how to cleans data to get the most out of your Tree Map visualizations.

In my example I use Tree Map to analyze Product Sales $ and Returns %. Sales $ measure is represented by chart block size and Returns % is represented by block color. If analyzed data have outlier Products (with unusual high Return % within 10 – 1000% range), then products with normal Return % (within 0 – 10% range) can not be differentiated by their Return %. This way we kind of “loosing” one of the measures e.g. Return %

What it takes to “bring back” Return % is to filter data excluding outlier Products

This is a kind of visualization that users would like to see. It gives a number advantages over tabular presentation or scattered chart:

  • Focus on what is important (in our case, Product with high Sales $ have bigger blocks) in contrast with scattered chart;
  • Analyze two measures at the same time(in our case, Sales $ and Return %) in contrast with tabular form where you can sort by one measure or another

Data cleansing is implemented by means of applying BEx Query conditions. You can find more information how to do it in following blog: Switch BEx Conditions on demand from SAP BusinessObjects Design Studio

 

 

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