SAC - Model - a brief

Building a robust data model in SAP Analytics Cloud (SAC) is essential for effective data analysis and reporting. Here's an outline of best practices for data modeling in SAC:

1. Define the Purpose and Scope of the Model

• Identify Key Objectives: Clearly outline what you want to achieve with the model (e.g., reporting, analysis, forecasting).
• Target Audience: Understand who will use the model and what their data requirements are.

2. Understand Your Data Sources

• Data Quality Assessment: Evaluate the quality of data from various sources (e.g., SAP BW, HANA, flat files, etc.).
• Data Integration: Choose the appropriate method for data integration, such as Live Data Connection or Import Data Connection.

3. Data Preparation

• Clean and Transform Data: Use data preparation tools to clean, transform, and enrich data before modeling.
• Data Enrichment: Integrate additional data sources for a comprehensive view (e.g., financial, operational, and market data).

4. Model Type Selection

• Choose the Right Model Type: Decide between:
  • Acquired Model for importing data.
  • Live Data Model for real-time reporting.
  • Calculation Model for advanced calculations.
• Plan for Future Scalability: Ensure the model can accommodate future data needs and growth.

5. Data Structure Design

• Hierarchical Structure: Design a logical hierarchy for dimensions (e.g., Time, Geography, Products) to enable drill-down capabilities.
• Fact and Dimension Tables: Clearly define measures (facts) and attributes (dimensions) for analytical queries.
• Star Schema Design: Consider using a star schema for simplified querying and reporting.

6. Performance Optimization

• Limit Data Volume: Avoid importing unnecessary data to enhance performance.
• Aggregation: Pre-calculate aggregations where possible to improve query performance.
• Indexing: Utilize indexes for frequently accessed fields to speed up data retrieval.

7. Security and Governance

• Access Controls: Implement security measures to restrict data access based on user roles and responsibilities.
• Data Governance Policies: Establish policies for data accuracy, consistency, and usage.

8. Model Testing and Validation

• Test for Accuracy: Validate the model with actual data to ensure accuracy in calculations and aggregations.
• User Feedback: Involve end users in testing to ensure the model meets their requirements.

9. Documentation

• Model Documentation: Maintain comprehensive documentation outlining the model structure, data sources, calculations, and user guidelines.
• Change Log: Keep a record of changes made to the model for future reference.

10. Continuous Improvement

• Monitor Performance: Regularly assess model performance and user satisfaction.
• Iterate Based on Feedback: Be open to refining and enhancing the model based on user feedback and changing business needs.

11. Training and Support

• User Training: Provide training sessions for users to maximize the effective use of the data model.
• Support Channels: Establish support mechanisms for users to report issues or seek assistance.

Conclusion

Building a robust data model in SAP Analytics Cloud involves careful planning, design, and execution. By following these best practices, you can create a model that not only meets current needs but is also adaptable for future requirements.