Creating an import model

After completing this lesson, you will be able to:

After completing this lesson, you will be able to:

  • Create an import model

The basics of creating an import model

Scenario: Create import models

Create import model workflow

Main steps for creating an import model:

  1. Create public dimensions
  2. Create the new type import model
  3. Switch to analytic type or leave as planning type
  4. Add existing public dimensions
  5. Create at least one measure
  6. Set model preferences if needed
  7. Select the time range
  8. Save the model
  9. The model is ready for data imports

A completed analytic model

Completed model:

  1. Model structure workspace: where you can view the meta data
  2. Measures: Where the measure values are stored in the model
  3. Public dimensions: Represented by a globe icon
A completed analytic model.
Measure values are stored in the model however calculated and converted measures are refreshed on-the-fly and are not stored.


Most preferences are made when the model is created and never changed.

Model preferences overview

  • General settings: select the model type
  • Language: request language translation
  • Access and privacy: set data access and other controls
  • Date settings: enable weekly based if needed, set date to calendar year or fiscal year
  • Planning: set disaggregation behavior for planning models
  • Currency: enable currency conversion
  • Structure priority: set the tie breaker for data intersections for account and measure formulas
  • Data and performance: settings to optimize performance for analytics and planning
Accessing model preferences

Viewing options

The structure and data foundation views

When creating a model, SAP Analytics Cloud has two viewing options:

  • Structure view
  • Data foundation view
Access the structure and data foundation views from the menu
Structure and data view icons in the modeler ribbon.

Structure view

The structure view shows you a star schema diagram representing the contents of your model. This view helps you visualize how your fact data, attributes, and properties all relate to each other.

At a glance, you can see the model's dimensions surrounding the data foundation.

Additionally the dimension box shows you more information based on the dimension type. For example, the version dimensions shows how many public versions there are, and whether there are any private versions. For date dimensions, the information about granularity and default hierarchy, and (if added) whether the Fiscal Year setting has been applied.

If you want to add a new dimension or existing dimension to your model, you can do that from the toolbar or from the schema diagram in the structure view.

Structure view
Analytic model structure view.

Data foundation view

The data foundation view shows you the fact table containing the raw, non-aggregated transactional data loaded into your model. The total number of results is the total number of rows of data, not including any filtering you have applied, across all versions.

In planning models, you can also switch between the different public versions of your data, for example, to see if data exists for a selected version.

Data foundation view in the model structure workspace
Data foundation view in the model structure workspace.

The structure and data foundation views work together with the dimension list and details panel to give you a consistent picture of your data. For example, if you select a dimension in the schema diagram, the other views all focus on that dimension.

Create an analytic model

Task 1:

Business example

You need to create a new model to be used for weekly universe data. The weekly data should be organized in a time hierarchy with a 4-4-5 pattern. The data only needs to be analyzed, therefore, a planning model is not needed.

In this practice exercise, you will complete the following tasks:

  • Create an analytic new model
  • Enable a week-based data pattern
  • Allow data for a week granularity

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