In a data model within SAP Analytics Cloud, Properties are used to further describe dimensions. Properties provide additional context or metadata for dimension members, such as descriptions, classifications, or other attributes that help to better understand and analyze the data within the dimension. This makes properties essential for detailed data analysis and reporting.
Reference:
SAP Analytics Cloud Help Documentation: Dimension Properties
SAP Analytics Cloud User Guide: Enhancing Dimensions with Properties

Connections and data preparation
When using a live connection in SAP Analytics Cloud, the data remains stored in the source system. This means that no data is imported or replicated into SAP Analytics Cloud; instead, it is accessed and analyzed in real-time directly from the source system. This approach ensures that the most current data is always used for analysis and that data governance and security policies of the source system remain in control.
Reference:
Live Data Connections to SAP S/4HANA | SAP Help Portal1
SAP Analytics Cloud Connection Guide2
SAP Analytics Cloud Data Connections – InsightCubes
In the context of SAP Analytics Cloud, when using a live connection to connect to a data source, the data remains stored in the source system. This setup means that SAP Analytics Cloud directly queries the data in its original location, without importing or copying it into the SAP Analytics Cloud environment. This approach is advantageous for several reasons, including maintaining a single source of truth, reducing data redundancy, and ensuring data is always up-to-date without the need for synchronization processes. Live connections are particularly useful for real-time or near-real-time data analysis and reporting, providing insights based on the most current data available without the overhead of data replication.
SAP Analytics Cloud documentation and user guides typically emphasize the benefits and use cases of live connections, highlighting how they maintain data in the source system to ensure real-time data access and analysis.
SAP training materials for Data Analysts using SAP Analytics Cloud, including study guides and official certification resources, explain the technical and practical aspects of live connections, including where data is stored and how it is accessed.
Best practice guides for SAP Analytics Cloud, often available through the SAP Community or SAP Knowledge Base, provide insights and recommendations on setting up and using live connections, reinforcing the concept that data stays in the source system.

A blank planning version in SAP Analytics Cloud can be created within the Version Management feature. This area of the platform allows users to manage different versions of their data, such as budgets, forecasts, and what-if scenarios. Creating a blank version provides a clean slate for planning activities, without pre-existing data, enabling users to start fresh with their assumptions and inputs.
Reference:
SAP Analytics Cloud Help Documentation: Version Management in Planning
SAP Analytics Cloud User Guide: Creating New Versions for Planning

When the SAP Analytics Cloud (SAC) modeler removes the first three characters from a public dimension imported from a source system, this change impacts Stories that use this dimension. Specifically, any visualizations, calculations, or filters within those stories that rely on the original dimension values may need to be adjusted to account for the change. This modification does not affect the source system or public datasets directly, but it can impact how the data appears and behaves in stories that use the modified dimension.
Reference:
SAP Analytics Cloud Help Documentation: Modifying Dimensions
SAP Analytics Cloud User Guide: Impact of Dimension Changes on Stories

In SAP Analytics Cloud, the Account dimension can be used similarly to a measure. This dimension is specifically designed for financial data and can hold various types of financial metrics, such as revenues, expenses, assets, and liabilities. It allows for the application of financial calculations and aggregations, which is why it can function similarly to measures in the context of financial reporting and analysis.
Reference:
SAP Analytics Cloud Help Documentation: Understanding Dimensions and Measures SAP Analytics Cloud User Guide: Working with Account Dimensions

When inputting new data for a private version in a story in SAP Analytics Cloud, it is necessary to “Publish” the data to ensure it is added to the model. Publishing the private version commits the changes to the underlying model, making the new data visible and accessible to other users according to their permissions. This step is crucial for ensuring that the updated data is incorporated into the shared model for further analysis and decision-making.
Reference:
SAP Analytics Cloud Help Documentation: Private Versions and Publishing SAP Analytics Cloud User Guide: Working with Private Versions in Stories

In an analytic data model within SAP Analytics Cloud, the automatically created dimension type that you can delete is the Generic dimension. This type of dimension is typically used for custom or ad-hoc categorizations and, unlike system-generated dimensions like Date or Version, can be modified or removed as per the specific needs of your data model and analysis requirements.
Reference:
SAP Analytics Cloud Help Documentation: Data Model Dimensions
SAP Analytics Cloud User Guide: Managing Dimensions in Analytic Models

If SAP Analytics Cloud interprets a ‘Year’ column as a measure instead of a dimension, it should be changed to a dimension to ensure it is treated as a calendar year. This adjustment can be made within the model or dataset settings, where the column’s role can be switched from a measure (quantitative value) to a dimension (qualitative value). Treating ‘Year’ as a dimension allows it to be used appropriately in time-based analyses, such as trends over time, without being aggregated like a numerical measure.