New potential of SAP Analytics Cloud (SAC)

Greater agility, faster decisions, and consistent data

The requirements for data analytics and corporate planning have changed significantly in recent years. In order to be able to respond to changes and new market situations as quickly as possible, departments today need much faster analyses, more flexible planning scenarios, and, above all, a consistent database. The problems usually lie less in the analysis or planning tool used and more in the underlying data architecture: rigid, historically grown models, complex data flows, and technical breaks prevent speed and flexibility. This is exactly where SAP comes in with its strategic realignment through the SAP Business Data Cloud (BDC). In this blog post, we take a closer look at this. In particular, we examine the new role of SAP Analytics Cloud (SAC) within the platform – and the advantages this brings for specialist departments.

Review: Why SAP BW is no longer sufficient as a proven database

For many years, SAP BW was the central system for reporting and planning in the SAP environment. It provided companies with a stable and standardized basis for analysis and was the undisputed standard for a long time. However, requirements have evolved: self-service analytics, real-time capabilities, flexible planning, and easy integration of external data sources are now part of the expectations of business departments. Traditional BW architectures are increasingly reaching their limits in this regard—particularly due to their rigid modeling logic, long development cycles, and close coupling to SAP systems, which makes the integration of external data sources significantly more complex. SAP is responding to these new requirements with a cloud-based platform that builds on existing strengths while opening up new possibilities.  

The new SAP approach: Business Data Cloud as a logical next step

With the Business Data Cloud (BDC), SAP provides a cloud-based database that brings together company data in a centralized and consistent manner. This creates a strategic framework for modern data and analytics architectures and enables a uniform database without classic ETL breaks – for faster analyses, reliable planning, and well-founded decisions.

The core components of the BDC approach are:

• SAP Datasphere as a central data platform for integrating, modeling, and harmonizing SAP and non-SAP data
• SAP Analytics Cloud (SAC) as an analysis and planning layer for specialist departments
• Optional components such as Databricks for advanced analytics, data science, and machine learning

The interaction of these building blocks enables an open, flexible approach with zero-copy and federated access scenarios. Data no longer has to be replicated redundantly, but can be made available consistently and up-to-date for analysis and planning. The big advantage: even non-SAP data can be incorporated into analysis and planning processes much more easily.

The new role of SAP Analytics Cloud: Central front end for analysis and planning

This new architecture also changes the role of SAP Analytics Cloud. SAC is no longer just a reporting tool, but the central front end for analysis and planning within the SAP Business Data Cloud architecture. Thanks to close integration with SAP Datasphere, users work directly on a harmonized database—without complex technical dependencies or lengthy IT projects. The concept of seamless planning shows how this close integration of analysis and planning is implemented in practice.

Seamless Planning: Analysis and planning based on the same data

With Seamless Planning , analysis, reporting, and planning processes take place on the same database—without media breaks or manual transfers. SAP Datasphere acts as a central data platform that integrates SAP and non-SAP data and provides it in a uniform semantic context. Building on this, close integration with SAP Analytics Cloud enables planning models to be created directly on the data source and plan values to be written back directly without any time delay. This eliminates many traditional ETL processes as well as exports and imports. The data is consistently available for planning, reporting, and analysis at all times.

This leads to:

• shorter planning cycles
• Consistent actual, planned, and forecast data
• lower technical complexity
• reduced susceptibility to errors

For specialist departments, this means faster iterations, more reliable results, and significantly greater transparency across actual, planned, and forecast data—providing a basis for well-founded and traceable decisions.

New division of roles between departments and IT

The new architecture also changes the way business departments and IT work together. Business departments gain more autonomy in analysis, modeling, and planning. At the same time, IT remains responsible for overarching issues such as governance, security, data quality, and uniform standards. The result is not a loss of control, but a clear separation of tasks: IT creates a stable framework, and the business departments use it flexibly and independently.

Conclusion & Outlook

The combination of SAP Datasphere and SAP Analytics Cloud opens up completely new possibilities for analysis and integrated planning, as it provides a consistent, cloud-based database for central analysis and planning use cases for the first time. Data from SAP and non-SAP systems is harmonized centrally and can be made available immediately without the need for redundant copies – both for analysis and planning scenarios. And it is precisely this architecture that is the prerequisite for further developments in the direction of AI and advanced analytics:

• AI models require consistent, up-to-date, and contextualized data.
• Datasphere provides this data in a uniform manner, regardless of the underlying source.
• SAC uses this data directly for forecasts, simulations, and automated planning scenarios.

By reducing traditional ETL breaks and batch jobs, AI-supported analyses and planning can be based directly on operational and historical data. At the same time, advanced analytics and data science use cases can be seamlessly connected via open integrations (e.g., Databricks). SAC acts as a business front end that makes complex models and AI results usable for specialist departments—even without specialized technical knowledge. This means that AI does not become an isolated experiment, but rather an integrated part of analysis and decision-making processes.

Would you like to benefit from the new possibilities? We would be happy to support you in switching to the modern SAP architecture. Just get in touch with us —together we will develop the right roadmap for your specific requirements.

A little spoiler: The next part of our blog series will focus on SAP BW modernization —and the opportunities that the end of maintenance presents for redesigning data and analytics architecture. Enjoy reading.