In the digital age, data is a strategic asset, and businesses are increasingly relying on business intelligence (BI) systems to turn raw data into actionable insights. SAP Business Intelligence (SAP-BI), part of the larger SAP Business Warehouse (SAP BW) ecosystem, is a powerful tool that enables organizations to collect, model, and analyze data. As business complexities grow, traditional data modeling methods often fall short. This has given rise to advanced data modeling techniques—essential for creating scalable, efficient, and intelligent BI solutions.
This article explores some of the most effective advanced data modeling strategies used in SAP-BI to optimize performance and improve data accuracy and usability.
The Layered Scalable Architecture (LSA) is a best practice architectural model introduced by SAP for building scalable and maintainable BI systems. LSA++ is the evolved version tailored for SAP BW on HANA.
LSA separates data into layers, such as acquisition, transformation, and reporting, which helps in isolating data changes and ensuring a clear lineage.
InfoObjects are the building blocks of all data in SAP BW. They represent business entities such as Customer, Product, or Region.
With the introduction of SAP BW on HANA, Advanced DSOs replace older data modeling objects like standard DSOs and InfoCubes, offering more flexibility and better performance.
Hybrid modeling involves using both SAP BW and SAP HANA-native models, such as Calculation Views, to capitalize on the strengths of each platform.
Allow the integration of external data without physical data storage in SAP BW.
Used to combine data from aDSOs, InfoProviders, or HANA views, supporting both Union and Join operations.
DTO is a method to optimize the storage of data in HANA-based BW systems. It allows classification of data based on usage frequency and importance.
Partitioning large datasets into smaller, manageable segments based on logical criteria (e.g., time, region).
With SAP BW/4HANA and SAP Data Intelligence, organizations can integrate machine learning models directly into data pipelines.
As SAP-BI evolves, so must the methods used to model and manage its data. Advanced data modeling techniques—such as LSA++, aDSOs, hybrid and virtual modeling, DTO, semantic partitioning, and predictive modeling—empower organizations to build intelligent, agile, and high-performance BI systems. By adopting these strategies, businesses can unlock deeper insights, drive innovation, and remain competitive in the ever-changing digital landscape.