Working with Analytic Models

Analytic models are the analytical foundation to make data ready for consumption in SAP Analytic Cloud. It allows users to create and define multidimensional and multi-fact models to serve data for analytical purposes to answer different business questions. Predefined measures, hierarchies, filters, parameters and associations will provide a flexible and easy to use navigation through the underlying data.

The analytic model is the go-to analytic consumption entity with rich analytical modeling. It offers:

• Analytical capabilities to answer complex business questions, using features like:

  • Time-dependent dimensions, texts and hierarchies
  • Structures with calculated and restricted measures
  • Exception aggregation
  • Currency conversion
  • Variables

• Consumption of data layer objects (semantic usage fact) with its nested dimensions.

• Re-use of source objects, reducing efforts and the number of objects to maintain.

• Control of output due to the modeling and pruning of attributes and measures to optimize performance.

• Unlimited stacking of analytic models.

• Repository integration (for impact and lineage analysis, CSN export/import & ACN transports).

• Analytical preview.

• Support of data democratization and self-service analytics strategies as business oriented user can easily create models.

• Sharing option to another space, and even multiple spaces. You can then create a new analytic model on top of that in order to consume this model from other spaces.

Analytic models allow for sophisticated measure modeling including calculations after aggregation, restricted measures, exception aggregation and currency conversion.

With the Create Analytic Model button, you can easily create as many analytic models on top of an existing analytic model or fact entity as you like to reuse its measures and attributes.

Associated dimensions of the underlying models are automatically added, but modelers of analytic models can select ("prune") which dimensions and attributes to expose. All nested dimensions are offered on the top-level of the SAP Analytics Cloud dimension dialog. Their node name is taken from the respective source field.

By dragging a different source object on top of the fact source node in the graphical editor, you can replace the fact source. To replace a dimension, you must remove it, adjust the association in the underlying model, and add the dimension again in the analytic model.

Variables are defined as standard variable, restriction variable or filter variable. Their values are determined at run-time by one of the following options:

• Direct user input
• Derived from another model
• User input with a default value from another model

A global filter is static. For a dynamic filter, create a filter variable. Filter variables are directly used as a global filter. Users will be prompted for the variable values on the launch of an SAP Analytics Cloud story. A value help is provided. This variable can be defined as single, multiple, range or interval.

Calculated measures (CM) in analytic models are calculated after aggregation, for example after all aggregations have been done on the current drill-down. This is particularly useful in case of fractions (for example average). Standard variables can be used to determine a parameter value at run-time.

Restricted measures (RM) restrict values of a measure based on a filter condition:

• A filter condition can be static or dynamic via a restriction variable. This variable can be defined as single, multiple, range or free selection.
• Restricted measures can be built on any other measure, not only fact source measure ("measure stacking").

Data preview: Relational datasets, facts, or analytical datasets only offer a relational data viewer (for example by record), forcing users to build a SAP Analytics Cloud story to check their data & modeling. Analytic models have a built-in analytical Data Viewer with rich attribute/measure selection, filtering & pivoting, hierarchy support and many more features.

In SAP Datasphere, together with SAP Analytics Cloud, you can combine your data sources and extract insights from them based on stories. A story is where your data comes to life with powerful data visualizations. Charts, graphs, tables, and other visual elements are arranged to tell the story of your business or organization. Stories are the analyzed results of combining different data sources and models. Here, you begin visualizing the valuable insights you got from your data and prepare those insights for sharing with your stakeholders.

These are the ways you can use SAP Datasphere data in SAP Analytics Cloud:

SAP Analytics Cloud :

• Direct consumption of SAP Datasphere data models in SAP Analytics Cloud using a live connection.
• Multiple SAP Datasphere tenants can be connected to any number of SAP Analytics Cloud systems.

Microsoft Office:

• SAP Analytics Cloud, add-in for Microsoft Office 365 with live connection for online or desktop version.
• SAP Datasphere with SAP Analysis for Microsoft Office 2.8 SP14+ (limited).

SAP Analytics Cloud, for planning:

• Bi-directional integration of SAP Datasphere and SAP Analytics Cloud for planning.
• Use OData API to pull data from SAP Datasphere into planning.
• Use data export service API to pull data into SAP Datasphere.

You have the following options and limitations for the consumption of data models:

• Direct consumption of data models in SAP Analytics Cloud via live connection.
• Ability to connect live to source data sets in SAP Datasphere and create stories in SAP Analytics Cloud.
• SAP Datasphere tenants can be connected to any number of SAP Analytics Cloud systems, and vice versa.
• The live connectivity needs to be set up for each SAP Analytics Cloud system.
• Metadata translation can be enabled for SAP Analytics Cloud stories.
• SAP Note 2832606 - Limitations with live connections

When building stories in SAP Analytics Cloud, the analytic model is now the preferred object.

The analytic model will replace the analytical dataset (defined as "Exposed for Consumption"). The analytical dataset will continue to exist, but you should now use the analytic model instead. New features will only be developed for the analytic model.

The analytic model offers more calculations and greater functionality. You can prune what you want to expose in your object, thus avoiding unnecessary calculations and in turn achieving a better performance.

As the analytic model is the object for consumption, and is therefore related to the SAP Analytics Cloud story, the terminology of the story is used for the analytic model:

• Variables: Comprise what are known as "analytical parameters" and "input parameters" in the analytical dataset.

• Dimensions : Comprise what are known as "attributes" in the analytical dataset.

Watch this video to see how to consume an SAP Datasphere analytic model in SAP Analytics Cloud.