This is part of the SAP Lumira Discovery blog series.
Custom Grouping allows users to create a new dimension by combining existing dimensions. SAP Lumira Discovery allows users to perform Custom groupings on the fly to group data for ease of analysis and reporting.
How to create custom groupings?
In the Data View, right-click on the Column on which you want to create the Custom Grouping and select Group by.
There are two kinds of groupings supported in Lumira Discovery,
- Group by Selection: used mainly for text fields
- Group by Range: used mainly for numeric and date fields
Group by Selection
When you are grouping by Selection, the Group by Selection window will appear as below.
In the above example, a Custom group is created on State based on its location. The custom group that you create will get added as a new dimension. In the Group Selection window, you can specify the name of your custom grouping in Dimension Name. You can then specify the Group Names and add members from the list on the left. You can add new groups by clicking on the plus icon in the top-right. Groups can also be deleted by clicking on the Delete icon next to the group name. There is also an option available to group all the remaining values.
Below highlighted column is the custom group that has been create –
Group by Range
Custom Groups can also be created based on Range. The below window appears when Group by Range is clicked.
Here again, a new dimension column is created for the grouped values. We can specify the name of the new dimension. For the interval definitions shown in the screenshot, 5 intervals will be created consisting an equal number of alphabets from A to R. The state names beginning with the remaining alphabets will be grouped under ‘Others’ as defined. Below is the resulting column.
We can then replace these range values with the dimensions name as desired.
We can now create groups in SAP Lumira Discovery without having to rely on changes in the backend systems to categorize our data for better analysis. This functionality will be very useful while creating buckets to group a set of dimension values together or while grouping locations according to their geography. Users can then drill down on required groups to view pertaining data in more detail. Creating groups makes analysis easier and our visualizations cleaner. This can then later be leveraged in Custom Hierarchies.