SAP Datasphere (DSP) is the core component for data management, semantic modeling, and data virtualization. It is where everything comes together, from the replication of operational data to the definition of reusable business objects.
After the discontinuation of SAP BW: Data strategies for the future
Time is running out for SAP BW 7.5. With mainstream maintenance ending in 2027 and extended maintenance in 2030, one of the most important data platforms in the SAP ecosystem is coming to an end. For many companies, this means more than just a technology change. It is the starting signal for a fundamental realignment of their data strategy.
While SAP BW/4HANA will continue to be maintained until at least 2040, these users should not fall into passivity. The future belongs to cloud-based, AI-enabled data platforms that go far beyond the capabilities of traditional data warehouse systems. The SAP Business Data Cloud offers a strategic way out without having to leave the familiar SAP ecosystem.
Why migration is more than just a platform change
The challenge lies not only in expiring maintenance contracts. Over the years, many companies have built up complex, monolithic BW structures: ABAP-based data flows, specific object types such as InfoCubes and CompositeProviders, and tightly coupled BEx tools. These mature structures cannot simply be transferred to the cloud at the push of a button.
Migrating SAP BW therefore means strategically restructuring the entire data architecture. Companies are faced with a decision: Should they adopt their existing models with as few changes as possible, or take the opportunity to fundamentally modernize their data landscape?
The good news is
that SAP offers a well-designed migration path with the Business Data Cloud that enables both approaches. Existing models can be transferred to SAP Datasphere via the "BW Bridge," while at the same time enabling the development of a modern, semantic data architecture.
SAP Business Data Cloud: More than just a BW replacement
The SAP Business Data Cloud is not a single product, but a strategic combination of coordinated services. It combines data integration, governance, analysis, and artificial intelligence under a consistent architectural concept – completely cloud-based and scalable.
The central components at a glance:
SAP Analytics Cloud (SAC) enables self-service reporting, integrated planning, and AI-powered analytics. The platform brings business departments and IT departments together by offering both ready-made dashboards and flexible analysis options.
SAP Databricks extends the platform with a full-fledged lakehouse and ML engine. The OEM-licensed solution is seamlessly integrated into the Business Data Cloud and enables advanced analytics without media breaks.
SAP Data Intelligence Cloud orchestrates complex data integration processes and connects various data sources – from SAP systems to external APIs and file formats.
SAP Joule and SAP Insight Apps bring generative AI directly into the analysis processes. Natural language questions are turned into meaningful visualizations, and proactive insights support decision-making.
Business Data Fabric: The conceptual framework
SAP Business Data Fabric is more than just a buzzword – it is the conceptual framework that brings together all components of the Business Data Cloud. The goal: company-wide access to distributed data sources without the need for physical replication.
The fabric integrates data virtualization, semantic harmonization, and real-time analysis in a common model. KPIs, authorization concepts, and data quality standards are defined once and reused across systems.
The result: consistent data models that can be flexibly adapted to changing business requirements.
For companies, this represents a paradigm shift: instead of collecting data in central warehouses, it is left where it is created and made accessible via semantic layers. This reduces latency, simplifies governance, and enables true real-time analytics.
AI power through SAP Databricks
SAP Databricks brings the power of modern data science directly into the SAP ecosystem. The integrated platform offers scalable engines for data lakes, streaming analytics, and the development of AI/ML models. Unlike externally operated Databricks instances, SAP data can be accessed without complex interfaces or media breaks.
The combination with SAP Analytics Cloud opens up completely new use cases: predictive maintenance based on IoT data from SAP systems, intelligent demand forecasts by linking sales data with external market information, or automated anomaly detection in financial processes.
Joule, SAP's generative AI assistant, makes these advanced analytics accessible to business users. Natural language questions such as "Show me the sales trend for the last three quarters and forecast the next one" are automatically converted into meaningful visualizations and recommendations for action.
Four scenarios for your migration strategy
Depending on your starting point and strategic goals, there are different migration paths:
Scenario A: Classic BW replacement
For companies with SAP BW 7.5, time stands still. The BW Bridge enables existing models to be transferred to SAP Datasphere in stages. At the same time, new use cases can be implemented directly in the modern architecture. It is crucial to train the specialist departments in SAC at an early stage in order to build up self-service capabilities.
Scenario B: Soft modernization by 2040
BW/4HANA users have more time, but they should use it wisely. The parallel development of a Business Data Fabric alongside the existing architecture enables step-by-step modernization. AI and advanced analytics scenarios can already be piloted today with Databricks and SAC.
Scenario C: Focus on self-service
Companies that want to strengthen their departments rely on SAC as a central platform for reporting and planning. Direct modeling in SAP Datasphere with a focus on the business layer enables agile data provision. Decentralized data expertise becomes a competitive advantage.
Scenario D: AI-driven transformation
Pioneers are using migration as an opportunity for AI-driven data architecture. SAP Databricks is becoming the development platform for ML models, while Joule and SAC enable predictive scenarios. The connection of operational SAP data with unstructured data in the data lake opens up completely new insights.
Conclusion: Opportunity instead of crisis
The discontinuation of SAP BW 7.5 is less of a technological problem than a strategic opportunity. With the SAP Business Data Cloud, companies can build a future-proof, scalable, and AI-enabled data platform without having to leave the familiar SAP ecosystem.
The clear separation and simultaneous integration of data management, analysis, and AI functionality is a decisive advantage. Instead of isolated island solutions, a harmonized data landscape emerges that can be flexibly adapted to changing business requirements.
The key to success lies in strategic planning: Which data is critical to your business? Which analysis scenarios should be possible in the future? How can departments be empowered to work independently with data? These questions should be answered before technical implementation.
At abat, we are happy to support you in the analysis, planning, and implementation of your data strategy. With our many years of SAP expertise, we accompany you through all phases of your transformation project, from the strategic roadmap to technical implementation.
FAQs
1. When does maintenance for SAP BW 7.5 expire?
Mainstream maintenance will end at the end of 2027, and extended maintenance will end at the end of 2030. Companies should define their migration strategy by 2025 at the latest.
2. Can I transfer my existing BW models to the cloud?
3. How does SAP Datasphere differ from classic BW?
4. What role does AI play in the new data architecture?
5. Do I have to renew my entire SAP system?
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