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lalitmohan
Product and Topic Expert
Product and Topic Expert
With SAP Datasphere's unified experience for data integration, data catalog, semantic modeling, data warehousing, data federation, and data virtualization, professionals can now distribute mission-critical business data across their organization's data landscape with ease and with business context and logic preserved.

By introducing the SAP Datasphere Analytic Model, SAP Datasphere improves the business semantic layer to provide advanced multi-dimensional capabilities. Complex aggregations, time dependencies, data previews, business hierarchies, business KPIs, and more are all included in this modelling experience. The basis for data consumption in SAP Analytics Cloud are the  analytical models which harmonise data from various sources and accelerate access to contextual, relevant business data for faster insights and decisions.

Analytical models are the foundation for preparing data for consumption in SAP Analytics Cloud. You can build and develop multi-dimensional models using them to supply data for analytical needs and respond to various business questions. Analytical datasets, which may include dimensions, texts, and hierarchies, are the sources for analytical models.



Comparison with Analytical Dataset


The analytical model is currently the object for building stories in SAP Analytics Cloud. The existing analytical datasets is replaced by the analytical model. Despite the fact that analytical datasets will still be available, you must now use the analytic model.

You can limit the contents of your object to display the information you wish to reveal in order to reduce unnecessary calculations and increase efficiency. Additionally, it offers limited and calculated metrics as well as analytical previews.

Analytical datasets will still be accessible even though new features will only be developed for the analytical model. On top of analytical datasets, analytical models are straightforward to build.






Blog post series for our learning Journey with SAP Datasphere and SAP Analytics Cloud together to handle complex analytics scenarios. 

  1. Connecting SAP SuccessFactors and SAP Datasphere (click here)

  2. Bring SAP SuccessFactor Data into SAP Datasphere (click here)

  3. Modeling SAP SuccessFactors Data in SAP Datasphere (click here)

  4. Building Analytic Models for SAP SuccessFactors KPI (this blog)

  5. Build SAP Analytics Cloud’s Dashboard for SAP SuccessFactors KPI (click here)



Prerequisite



You need to have:

  1. SAP BTP global account setup and to entitled the SAP Datasphere Cloud Foundry environment please follow the excellent blog How to create SAP Datasphere service instance in the SAP BTP Platform?

  2.  A SAP SuccessFactors instance.


Let’s proceed Step By Step


Before we start, ensure that you have followed and completed the steps mentioned in my first blog post(Connecting SAP SuccessFactors and SAP Datasphere), which will help you to connect SAP SuccessFactors and SAP Datasphere. Please follow the second blog post( Bring SAP SuccessFactors Data into SAP Datasphere) to import the SAP SuccessFactors tables and replicate their data in SAP Datasphere.

Also follow the third blog post(Modeling SAP SuccessFactors Data in SAP Datasphere)to create an Analytical Dataset that contains information about a simple KPI or a metric for Talent Analysis.

Creating an Analytic Model


From the side navigation, choose Data Builder, select a space if you have not selected space earlier.

Choose New Analytic Model to open the editor.

Drag a table or view from the Repository panel and drop it on the canvas.

Select the properties you want to copy from the source. You can still take over properties into your analytic model later.

The source of your analytic model can contain associations to dimensions, texts, and hierarchies. Dimensions contain attributes that can be used to analyze and categorize measures defined in other entities. Attributes are used as dimensions in the analytic model.

When you take over associated dimensions from your source, the object type is also taken over. Thus, you can easily build a multidimensional model in a star schema with the fact source in the centre and the dimensions as direct associations.

Click on the Associated Dimension V_PerPersonal

When you click on a dimension on the canvas, you can select associated dimensions of the dimension in the properties panel on the right.

Add and Deselect Associated Dimension and Attributes.

To edit the properties of a dimension: Click on the dimension of the canvas to show its properties in the side panel. You can make the following changes here:

  • You can change the alias of this item in the properties panel. The alias is the name that is shown in the story in SAP Analytics Cloud.

  • You can add or deselect associated dimensions.

  • You can add or deselect attributes.


When you click on your fact source on the canvas, you can select associated dimensions and attributes in the properties panel on the right. The attributes are also treated as dimensions in the analytic model.

Choose Save and Deploy.

Data Preview


The Data Preview feature of Analytic Models enables you to continuously monitor the modelling results and see exactly how users of SAP Analytics Cloud will view your model.

It includes the following features:

  • Choose relevant Dimensions & Measures flexibly, then arrange them in columns or rows.

  • Apply different kinds of filters to any Dimensions or Measures.

  • Change the ID and/or description, number formatting, and display behaviour.

  • Preview without first deploying


The Data Preview helps you to determine whether your data is correct.

Change the toggle in the upper right from Model to Preview for the Data Preview environment.

In Data Preview, you may navigate through the various dimensions and view the aggregated data. This means, you can see how the data will look like in an SAP Analytics Cloud story.

You can drill down by rows and columns by clicking on the icons at the different objects.

Setting Filters


Another effective tool for examining the results of the data is filtering. As a result, there are multiple ways for you to design filters by Measures or Dimensions.

For example, the Form Template Name Dimension represents the performance Review in different years. We aim to filter it for a particular year, e.g. 2016 Performance Review, which gives us the number of ratings for 2016.

If you want to set filters, choose  Add Filter and select Measures or Dimensions. Click on the Dimension for e.g. Form Template Name.

In our case we will filter it for Form Template Name field for e.g. we will select 2016 Performance Review. we have multi-selection feature available as well. 

The filter affects the visibility of the Data Preview. The filter is successfully applied, and the outcome is as expected. In our case it is Number of Ratings for 2016 Performance Review.

Using the Builder Panel


The Builder Panel is displayed at the right side of the application. You can show it or hide it by choosing Query Builder Designer Panel.

When you choose Available Objects, you get a list of all available dimensions and measures in the analytic model. Here you can select dimensions and measures and assign them directly to the table's rows or columns by clicking Column or Row.

You can also drag and drop dimensions from the Available Objects panel into the Builder panel.

Some features of the builder panel that makes it easier for you to work during build are:

  • You can search for items.

  • You can change the items' display between ID and Description as well as their sort order by clicking More.

  • You can resize the width of the left side panel for Available Objects. This way you can display long dimension names.


Under Rows and Columns, you see all measures and dimensions that are displayed in the table.

In my previous blog we have talk about a simple KPI or a metric for Talent Analysis to Check the team’s potential by giving insights into employee performance metrics based on locations, business units and departments.

It is not required anymore to build a SAC Story for the analytical Data Preview. So now use the Query Builder Designer Panel to check the analytical data before start building SAC Story.

For Employee performance metrics based on business unit, choose Business Unit Name’s row under Dimensions from the Available Objects.

Similarly, you can select respective department or location row for Employee performance metrics based on Department or Location.

Let’s make the employee performance metrics more complex to capture data based on Business Unit per Region per Department together.

Summary


So, now you have seen how to build an Analytic Model to enhance the business semantic layer to provide advanced multi-dimensional capabilities and how to use the most relevant Data preview features of the Analytic Model for SAP SuccessFactors Data.

What’s next?


In the next blog article, we'll look at how we can use  this analytical model in SAP Analytics Cloud to meet the need for data visualization that provides analytical capabilities to address various business metrics; for us,It relates to the SAP SuccessFactors Talent Analysis KPI.

Build SAP Analytics Cloud’s Dashboard for SAP SuccessFactors KPI

Keep your anticipation high for the upcoming blog posts. Stay curious!

Reference & Further Reading


SAP Datasphere business data fabric
Introducing the Analytic Model in SAP Datasphere
SAP Datasphere Analytic Model Series – Data Preview
Creating an Analytic Model in SAP Help Documentation

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

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