In today’s data-driven enterprises, decision-makers often need to analyze information drawn from diverse systems and databases. SAP BusinessObjects (BO) provides a comprehensive business intelligence platform that supports integration of data from multiple heterogeneous sources, enabling consolidated reporting and holistic insights. This article explores how SAP BusinessObjects facilitates data integration from multiple sources and highlights best practices to effectively combine and analyze such data.
Modern businesses typically operate with a variety of data repositories:
- SAP ERP systems (e.g., SAP S/4HANA, SAP ECC)
- Non-SAP relational databases (Oracle, SQL Server, MySQL)
- Cloud applications (Salesforce, ServiceNow)
- Flat files, spreadsheets, and third-party APIs
Integrating these sources allows organizations to:
- Break down data silos
- Create unified reports and dashboards
- Improve data accuracy and completeness
- Support cross-functional analysis and strategic planning
SAP BusinessObjects Universes provide a semantic abstraction over underlying data sources, enabling users to query complex data without needing deep technical knowledge.
- Universes can connect to multiple data sources using various connectivity options.
- Designers can create multi-source universes that combine tables from different databases or schemas.
- Semantic layers translate complex joins and relationships into user-friendly objects.
Web Intelligence (WebI), SAP BusinessObjects’ ad-hoc reporting tool, supports combining data from multiple universes or data providers in a single document.
- Users can create multiple data providers within one WebI report.
- Data providers can be merged using merged dimensions to align related data from different sources.
- This enables cross-source calculations, comparisons, and visualizations.
¶ 3. Data Federation and Virtualization
Through SAP BusinessObjects and SAP’s broader data management tools (like SAP HANA Smart Data Access), virtual data federation can be achieved, allowing real-time querying across multiple sources without data duplication.
- This reduces ETL complexity.
- Enables up-to-date and consistent reporting.
¶ 4. SAP Data Services and ETL Integration
For scenarios requiring data consolidation and transformation, SAP Data Services can be used alongside BusinessObjects to extract, transform, and load (ETL) data from diverse sources into a data warehouse.
- Once consolidated, BusinessObjects reports can connect to the warehouse for integrated analysis.
- Define Clear Business Requirements: Understand reporting needs and key data elements before integration.
- Design Robust Universes: Ensure proper joins, contexts, and hierarchies to prevent ambiguous results.
- Use Merged Dimensions Wisely: Merge only dimensions with consistent data and granularity to maintain accuracy.
- Monitor Performance: Multi-source queries can be resource-intensive; optimize queries and consider caching strategies.
- Maintain Data Security: Ensure proper authorization and masking across different data sources.
- Document Data Lineage: Keep clear documentation of data origins and transformation logic for auditability.
¶ Example Scenario: Sales and Marketing Analysis
- Connect SAP S/4HANA for sales transaction data.
- Connect Salesforce CRM for marketing campaign data.
- Create a multi-source universe combining sales and campaign information.
- Build a WebI report merging sales regions (from SAP) with campaign segments (from Salesforce).
- Analyze campaign effectiveness by comparing marketing spend and sales revenue in one dashboard.
Integrating data from multiple sources within SAP BusinessObjects empowers organizations to break data silos and unlock comprehensive insights critical for strategic decision-making. Leveraging multi-source universes, Web Intelligence multi-provider reports, and data federation techniques enables seamless, flexible, and performant analysis across diverse systems. As businesses grow increasingly complex, mastering multi-source data integration in SAP BusinessObjects will remain a key differentiator in achieving data-driven success.