SAC Dimension - after the fact

To make a model dimension as a defined dimension to load data separately in SAP Analytics Cloud (SAC), follow these steps:

Steps to Define Dimensions Separately in SAC:

1. Access the Existing Model:

   • Open SAP Analytics Cloud (SAC).
   • Go to the Modeler and open the model you initially built.

2. Create Separate Dimensions:

   • In the Modeler, navigate to the Dimensions tab.
   • Click on the + Create New Dimension button to add a new dimension.
   • Define the necessary attributes and properties for the dimension (e.g., ID, Description, etc.).
   • You can create dimensions for items like time, organization, product, or custom dimensions specific to your dataset.

3. Edit Existing Dimensions:

   • If your model already has embedded dimensions (created during the initial data load), you can convert these to separate defined dimensions:
     • Select the dimension from the model.
     • Click on the Convert to Public Dimension option. This will separate the dimension from the model.

4. Associate the Defined Dimensions with the Model:

   • Once your dimensions are defined and created, you can associate them with your model.
   • In the model editor, under the Dimensions section, click Add Dimension.
   • Select the predefined dimension that you created in the previous step.

5. Prepare and Load Data:

   • After associating the dimension, proceed to load the data separately for the model.
   • When importing new data (either from a CSV file, a data connection, or another source), ensure the data is mapped to the correct predefined dimensions.
   • Load the dimension data separately if needed, then load the fact data (metrics or measures).

6. Validate and Save:

   • After the data has been loaded, validate the model by ensuring that the data is correctly mapped to the defined dimensions.
   • Save the model.

Key Considerations:

• Public vs. Private Dimensions: Predefined (public) dimensions can be reused across multiple models, providing consistency in dimension values.
• Dimension Structure: Define hierarchies or levels within the dimensions as needed to structure data better (e.g., regional hierarchies for geographic data).
• Data Import: When importing new data, map the new dataset to the defined dimensions properly to ensure data integrity.

By separating and defining dimensions in this way, you can streamline future data loads, ensuring consistent dimension structures across different models and datasets.