Let’s suppose for a moment that you need to create a monthly report in SAP Analytics Cloud. For the first month, you import your data, spend time preparing your data for analysis, and create your story. Once finished, you analyze your data, share your findings with the executive team, and pat yourself on the back. Mission accomplished.
Next month rolls around and you need to create the same report. However, updating your model with the latest data, preparing it for analysis, and creating your story all over again requires more time than you have.
Fortunately, the mapping capabilities in SAP Analytics Cloud allow you to update your existing story with new data, even from multiple datasources. If you set up a schedule, you can even have this update happen automatically.
Getting started — data acquisition
To start mapping, you first need to import your data and create your model. Since this model is going to be the basis for all future models, ensure that you spend some time wrangling it properly. Wrangling your data entails fixing typos, setting up hierarchies, adding calculations, and so on.
Once you’re satisfied with your model, you can create your story. Since this is your first story, you may want to spend some time with it by adding images, shapes, and text, and formatting a visually pleasing layout. This initial time investment will pay off since all future stories will not require any additional (or minimal) formatting.
When it comes time to upload new data, you can import the new datasource to your existing model. You need to ensure all the measures and dimensions from the new datasource map correctly to your existing model.
Adding a new datasource to an existing model
One of the benefits of mapping is that you can combine multiple datasources into one model. The advantage of doing this is as opposed to blending data at the story level is that you only need to do it once. Blending data at the story level only applies to that one story, and therefore it’s inefficient if you need to tell multiple stories.
To map multiple datasources, all your datasources must have similar dimensions. If necessary, you can add dimensions to accommodate any new fields from a new datasource. For example, perhaps this month you have a new product category, or serviced a new region. You can append your existing model to include these new dimensions.
The process of mapping your data in SAP Analytics Cloud is quite straightforward. Once you’ve created your model, select it from the model list, and click on the ‘Import Data’ icon from the menu.
Next, choose the new source of data you want to import. In this example, we’ll choose ‘Import Data From File’.
When you import your new datasource, SAP Analytics Cloud automatically maps it to your existing model. However, you may need to manually make some adjustments.
In the modeler, you’ll see a panel on the right side. Here you can see all the ‘Mapping Options’ as well as the ‘Import Method’ options.
- Update — Updates the existing data and adds new entries to the target model
- Append — Keeps the existing data as is and adds new entries to the target model
- Clean and Replace — Deletes the existing data and adds new entries to the target model
Note: This option removes any changes you made to the model after it was originally created. If you choose this option, you need to redo any changes
In the example below, the dimension names are slightly different, so we need to map them manually. We will map ‘Calendar Year’ to the ‘Calendar Year/Month’ dimension in our Model.
When we apply any changes, the affected columns become highlighted in green.
Once all your dimensions and measures have been mapped, the proper attributes have been assigned, and there are no data quality issues, your model mapping is complete. You’ll see a notification in the right-side panel.
Once you click ‘Finish Mapping’ and save your model, any stories created using this model will automatically be updated. Model mapping in SAP Analytics Cloud is a great way to save you time, especially when importing data from multiple datasources.