Which models are primarily used to prepare data for analysis within SAP?

The choice of semantically rich models is particularly significant in preparing data for analysis within SAP. These models are designed to provide a clear and meaningful representation of the data, allowing users to understand the underlying structure and relationships within their datasets. By incorporating semantics, these models help to improve data quality and enhance the user experience when it comes to data exploration and analysis.

Semantically rich models often include metadata and context that clarify the meaning of data elements, which is essential when integrating data from various sources. This facilitates more accurate query results and insights, making the analytical processes more effective. The emphasis on semantics ensures that business users can leverage data without needing extensive technical expertise, empowering them to make informed decisions based on a comprehensive understanding of the data.

Data lakes, virtualized data sources, and historical data models, while valuable in their own contexts, do not specifically focus on creating a rich semantic layer that enhances data comprehension and usability in analysis to the same degree as semantically rich models. Data lakes, for instance, store massive amounts of raw data, but may not provide the structure required for seamless analysis. Virtualized data sources emphasize data access without necessarily providing contextual clarity. Historical data models concentrate on past data trends, which may lack the flexibility and semantic richness needed for