Data modeling is the backbone of any successful analytics project. In SAP Analytics Cloud (SAC), effective data modeling ensures that your reports, dashboards, and planning applications deliver accurate, timely, and actionable insights. Given SAC’s powerful blend of analytics and planning capabilities, following best practices in data modeling is essential to maximize performance, scalability, and user experience.
This article outlines key best practices to consider when building data models in SAC.
¶ 1. Understand Your Business Requirements First
Before building your data model, engage with business stakeholders to understand their needs:
- What are the key metrics and KPIs?
- Which dimensions are critical (e.g., customer, product, time)?
- What is the expected data volume and refresh frequency?
- Is the model intended for reporting, planning, or both?
Clear requirements guide your modeling decisions, helping you design a model that is fit for purpose without unnecessary complexity.
SAC supports two main ways to bring data in:
- Live Connections: Real-time data access without data duplication, ideal for large enterprise systems like SAP BW or SAP HANA.
- Data Import Models: Data is imported and stored in SAC’s in-memory engine, allowing advanced blending and transformations.
Select the connection type based on use case, data volume, and performance needs. For complex planning scenarios or data blending, imported models may be preferable. For operational reporting on live transactional data, live connections are optimal.
¶ 3. Design Clear and Consistent Dimensions
- Define master data dimensions (e.g., customer, product, region) in SAC to ensure consistency across models.
- Use hierarchies within dimensions (e.g., Product Category → Product Subcategory → Product) to enable drill-downs.
- Manage attribute relationships carefully to optimize query performance.
- Leverage SAC’s linked dimensions feature to reuse dimensions across multiple models.
Well-structured dimensions improve user navigation and maintain model integrity.
¶ 4. Simplify Measures and Calculations
- Keep base measures straightforward and offload complex calculations to the data source when possible.
- Use calculated measures in SAC sparingly and test their performance impact.
- Consider creating restricted measures or variables to allow flexible filtering and slicing.
- For planning models, design input-ready measures clearly distinguishing between actuals, forecasts, and targets.
Balanced calculation strategies optimize both model performance and usability.
¶ 5. Optimize Data Volume and Model Size
- Limit the number of records by filtering data during import or restricting data retrieval in live connections.
- Archive historical data if not required frequently or use aggregated summaries.
- Avoid importing unnecessary columns; only include relevant attributes and measures.
- Regularly monitor model size and refresh durations to detect bottlenecks.
Efficient data volume management leads to faster load times and smoother user experiences.
- Use SAC’s built-in time dimension with standard hierarchies (Year, Quarter, Month, Day).
- Customize fiscal calendars if needed to align with business periods.
- Design models to support time-based calculations like Year-over-Year growth, moving averages, or rolling forecasts.
- Enable drill-down on time hierarchies to empower detailed temporal analysis.
Time dimensions are crucial for trend analysis and planning accuracy.
¶ 7. Implement Robust Security and Access Controls
- Define data access controls at the model level based on user roles.
- Use section access for row-level security to restrict sensitive data.
- Configure dimension security to limit visibility of certain dimension members.
- Regularly review and test security settings to prevent unauthorized access.
Security best practices protect your data and comply with governance policies.
- Maintain clear documentation of model structure, data sources, and business logic.
- Use SAC’s model description fields to capture purpose and design notes.
- Version control your models, especially in planning scenarios, to track changes and revert if needed.
- Share documentation with stakeholders to foster transparency.
Good documentation accelerates onboarding and eases future model maintenance.
¶ 9. Test and Validate Models Rigorously
- Validate data accuracy against source systems regularly.
- Test model performance with typical user queries.
- Solicit feedback from end users during development to ensure usability.
- Monitor models post-deployment and refine based on usage patterns.
Continuous testing ensures models remain reliable and relevant.
Mastering data modeling in SAP Analytics Cloud is key to unlocking the platform’s full potential. By aligning with business needs, optimizing dimensions and measures, managing data volumes, and enforcing security, you can build scalable, performant, and user-friendly models. These best practices will empower your organization to derive meaningful insights and make smarter data-driven decisions.