SAC - Dataset vs Model - a comparison study

In SAP Analytics Cloud (SAC), both Datasets and Models play crucial roles in data management and analysis, but they serve different purposes and have distinct characteristics. Understanding the key differences between them helps in selecting the appropriate approach based on your reporting and analysis needs.

Here's a breakdown of the key differences between Datasets and Models in SAC:

1. Purpose and Use Case

• Dataset:

  • Datasets are simple, flat data collections mainly used for ad hoc analysis, quick data exploration, and visualizations. They are suitable for one-off reporting or scenarios where you don't need complex data relationships or advanced modeling.
  • Use Case: Best for users who need to quickly load, analyze, and visualize raw data without the overhead of creating a structured model.

• Model:

  • Models in SAC represent a more structured approach to data management. They are designed for enterprise-level reporting and support multi-dimensional analysis, calculations, and more sophisticated data transformations. Models typically include measures, dimensions, and hierarchies, and they integrate well with planning and predictive analytics.
  • Use Case: Ideal for users who require repeatable, formalized reports and dashboards, complex data relationships, advanced planning, and forecasting.

2. Structure and Complexity

• Dataset:

  • Datasets are flat and tabular, meaning they don't support complex hierarchies or multiple relationships between dimensions.
  • They are more flexible and less structured, allowing users to quickly input raw data from files like CSV or Excel and immediately start building charts.
  • Data Manipulation: Limited transformations, calculations, and formatting options compared to models.

• Model:

  • Models are multi-dimensional and structured, supporting hierarchies, measures, and dimensions. They are more powerful for use in planning scenarios, performance management, and financial analysis.
  • Models can also contain time dimensions (e.g., fiscal year, calendar year), custom hierarchies, and aggregation logic.
  • Data Manipulation: Supports more complex calculations, data transformations, and metadata enrichments, such as currency conversions or advanced time-based functions.

3. Data Management and Integration

• Dataset:

  • Datasets are typically used for local data uploads (e.g., CSV, Excel) and do not integrate deeply with external systems. The data is uploaded and stored in a lightweight form within SAC.
  • Connection: Limited to basic connections and not intended for continuous, real-time updates.
  • Maintenance: More manual; changes to the dataset often require re-uploading or manual refreshes.

• Model:

  • Models can be linked to various live data connections or imported from external systems (such as SAP BW, SAP HANA, or other databases). They support both live and acquired data models.
  • Integration: Supports seamless integration with other SAP systems, allowing for automated data updates, scheduling, and synchronization.
  • Maintenance: More robust, as models can be updated and refreshed dynamically, with built-in data management capabilities like versioning and permissions.

4. Planning and Predictive Analytics

• Dataset:

  • Datasets are not typically used for planning or predictive scenarios. They lack the necessary structure (such as dimensions and measures) to support complex calculations, forecasts, or plans.
  • Planning Features: Not available.

• Model:

  • Models support advanced planning and predictive analytics. They allow for what-if scenarios, versioning of plans, budget tracking, forecasting, and more.
  • Planning Features: Full support for planning functions, allowing users to input and adjust forecasts, budgets, and plans directly in SAC.

5. Data Governance and Security

• Dataset:

  • Datasets have basic governance features. Since they are typically uploaded manually, there is less focus on data lineage, version control, or security.
  • Permissions: Basic access control, usually dependent on who has access to the SAC story or workspace.

• Model:

  • Models offer strong data governance with features like role-based security, permissions, and version control. You can manage who has access to the data, set up specific roles, and enforce security at various levels (e.g., row-level security).
  • Permissions: More granular access controls can be applied, ensuring the right users see the right data.

6. Performance and Scalability

• Dataset:

  • Datasets are lightweight and designed for smaller, simpler datasets. Performance may degrade with large datasets, as they are not optimized for handling huge amounts of data or complex calculations.
  • Scalability: Limited, especially with large datasets or complex queries.

• Model:

  • Models are built to handle larger datasets and complex multi-dimensional analysis. They offer better performance and scalability, especially when connected to high-performance data sources like SAP HANA or BW.
  • Scalability: Highly scalable and optimized for enterprise-level reporting.

Summary Table:

Aspect	Dataset	Model
Purpose	Ad hoc analysis and visualization	Enterprise reporting, planning, and analysis
Structure	Flat, tabular	Multi-dimensional, structured
Data Sources	Local file uploads (e.g., CSV, Excel)	Live connections, SAP systems, external DBs
Complexity	Simple	Complex with measures, dimensions, hierarchies
Planning Support	Not supported	Full planning and predictive analytics
Governance and Security	Basic	Advanced with permissions, data lineage
Performance	Best for small datasets	Optimized for large datasets
Maintenance	Manual refresh	Automated updates and scheduling

Choosing between a Dataset or a Model in SAC depends on the complexity and long-term needs of your analysis. Datasets are great for quick, simple tasks, while Models offer a structured, scalable solution for comprehensive reporting and planning.