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Figure 1: Data Preview - Quick Tour


The SAP Datasphere Analytic Model Series is intended to provide you with useful guidance on how to utilize the new Analytic Model to leverage the potential of your data landscape. The Analytic Model allows for rich analytical modelling in a targeted modelling environment and will be THE go-to analytic consumption entity for SAP Datasphere.

This article is the 8th in the blog post series and introduces the Data Preview functionality. So far, the following blogs have been published:

In this blog, we will briefly walk through the most relevant features and benefits of the new analytical Data Previewer. In a nutshell, the Analytic Model data can now be previewed without the need to create an own SAP Analytics Cloud (SAC) story. This increases the user experience, but also saves valuable time when modeling. Some of the features presented might be known from SAC’s Data Analyzer – this shows once again that the solutions are technically moving together.


Data Preview

The new Analytic Model is a rich analysis environment that allows to constantly check the modelling outcome and see exactly how SAP Analytics Cloud users will see your model.

It basically brings in the following functions:

  • Flexibly choose relevant attributes & measures to place them in columns or rows; drag & drop to re-arrange

  • Set flexible filters on any attribute or measure incl. value help

  • Display of hierarchies or flat presentation

  • Change sorting, filtering, totals, unbooked values & display behavior (e.g. ID and/or description, number formatting, etc.)

  • Preview without need to deploy first



The following figure serves as structured orientation. The underlying Analytic Model is detailed explained in this blog.

Figure 2: Rough Overview

Initial step to get from Analytic Model to Data Preview is to switch the toggle on the upper right (1). On the middle you then see the analytical result grid (7), which changes dynamically when configuring the available measures and dimensions on the right-hand side (2).

In case loading takes too long when conducting several adjustments, the Pause Refresh feature (8) on the top left might be time saving for you. It avoids the automatic refresh.

Via the blue bar on the right side, you can navigate between Builder, Filter and Styling panel. Even if the Builder is displayed by default and represents the main component, the styling component (4) may be worth a visit for tiny styling adjustments (e.g., scale, decimal places etc.) of the measures.


Hierarchical Display

Within the Previewer, flat representation is set by default, but hierarchies can be easily activated for dimensions of choice. In the following example, we add the dimensional hierarchy to the Product Manager.

Figure 3: Organizational Hierarchy Example

After switching to organizational hierarchy, further settings become available. It offers the opportunity to adjust details like node level structure or the position of child nodes.

Figure 4: Advanced Hierarchy Settings

As already mentioned in the beginning, it is not required anymore to build a SAC Story for the analytical Data Preview. So how does our hierarchy example look like in SAC?

The following graphic illustrates the similar look and feel.

Figure 5: Data Analyzer View in SAP Analytics Cloud

However, there are still tiny differences. SAC’s Data Analyzer uses the repository to save and share views. These functionalities aren’t available yet in SAP Datasphere.


Rich Filtering

Filtering is another powerful feature to investigate the data outcome. Therefore, you have a wide variety to create filters by Measures or Dimensions. But be careful, it requires the correct application.

In the following I introduce a quick example to clarify how to do so. Starting point is an analytical Data Preview with horizontal dimension on Product ID and vertical dimension on Year. The preview also underlies a Product Group Hierarchy (see Fig. 6). Aim is to filter all products which have a value of more than 1.000.000 € in year 2021.

Figure 6: Start View for the filtering example

Filters can be created on the top left (6). In our case we want to filter by measure Value. Threshold is greater than or equal to 1.000.000.

Figure 7: Creation of Value Filter with Threshold

Applying the filter worked successfully so we move forward with the restriction on year 2021.

Figure 8: Filtering Year

Therefore, we select year 2014.

Figure 9: Year Selection (1st way)

The following result may surprise – S/4 On Premise, Analytics Cloud Planning and Mobile Services have a value lower than 1.000.000 but are still displayed.

Figure 10: Result (1st way)

This leads to the question – why? Well, the dimensional context for Year is missing. In other words, the filter has no technical impact as it only affects the visibility in the Data Preview. So, let’s adjust our first filter as the grouping works by Dimension Context.

Figure 11: Adding Year to Dimension Context (2nd way)

Voilà - the result now looks exactly as we wanted it.

Figure 12: Final Data Preview of Filtering Example



This blog gave a brief introduction how to use the most relevant features of Analytic Model’s Data Previewer. Analytics Cloud & Data Warehouse Cloud professionals will surely be happy about this new feature, as it serves as a powerful bridge between Data Modeling and SAP Analytics Cloud Monitoring.

Thanks for reading! I hope this blog is helpful for you. For any questions or feedback just leave a comment below this post.

Many thanks to jan.fetzer for the collaboration on this blog post.


Further Links

Find more information and related blog posts on the topic page for SAP Datasphere.

If you have questions about SAP Analytics Cloud you can submit them in the Q&A area for SAP Datasphere in the SAP Community.