End of Maintenance for SAP BW: What’s Next?

What architectural idea do SAP BW and SAP Datasphere share?

For years, SAP Business Warehouse (SAP BW) was SAP’s all-in-one solution for business intelligence, used by tens of thousands of customers worldwide. Originally developed to complement SAP ERP systems, it provided native connectors, predefined business content, and a central data warehouse with subject-specific data marts. The solution also included integrated reporting and analytics through SAP Business Explorer (BEx) and planning functions (BW-IP).

The goal was clear: establish a harmonized semantic data layer and run analytics on top of it. This end-to-end solution integrated data storage, logic, and reporting. SAP pursues this same core idea today with the Business Data Cloud (BDC).The difference is not the concept, but the architecture. BDC is cloud-based, modular, and service-oriented rather than monolithic.

What strategic choices do organizations face after SAP BW 7.5?

Mainstream maintenance for SAP BW 7.5 ends in 2027, with extended maintenance available until 2030 at additional cost. This timeline forces organizations to define a future target architecture. The established options are SAP and non-SAP variants (see Figure 1). With the introduction of SAP Datasphere, hybrid architectures combining SAP with partner platforms such as Databricks, Snowflake, Microsoft Fabric, or Google BigQuery have also become viable.

What does an SAP-only modernization path look like?

This approach keeps organizations entirely within the SAP ecosystem and provides integration with S/4HANA and other Business Suite components. It also enables access to standardized data products in SAP BDC. Two options exist: a migration to SAP BW/4HANA, which receives maintenance until 2040, or the adoption of SAP BDC.

Both approaches allow the exclusive use of SAP-managed data, but bind organizations more tightly to the SAP ecosystem and reduce architectural flexibility.

What are the implications of choosing a non-SAP architecture?

This path offers more flexibility and can be designed according to specific requirements. It supports all deployment variants and the use of diverse tool components. A primary challenge is accessing SAP data. The changed certification and usage conditions for the ODP interface create new obstacles that must be considered in the target architecture.

How does a hybrid SAP–non-SAP approach work in practice?

This mixed approach uses SAP BDC to map central semantics and for most BI, analytics, and planning scenarios. For selected use cases, the solution is complemented by partner platforms. With this approach, organizations gradually build a semantic layer in BDC. In parallel, they can use complementary technologies for data engineering, machine learning, or advanced analytics in partner environments.

This concept is new and its technical maturity is still developing. A technical proof of concept and a pilot project are advisable to validate functional maturity, cost implications, and governance compliance at an early stage.

How does SAP recommend transitioning from BW to BDC?

For customers choosing SAP BDC, SAP recommends a three-stage approach to modernizing BW:

• Lift: Move the existing BW system to a private cloud edition (PCE) to ensure operational stability and modernize the infrastructure. This step extends maintenance until 2030 and provides time for the next stages.
• Shift: Gradually migrate existing BW artifacts into customer-managed data products. SAP plans to provide a “Data Product Generator” starting in 2026 to support this transition.
• Innovate: Modernize business-critical or strategically relevant data flows and switch them to SAP-managed data products. This approach enables the use of functions like AI, planning, or self-service. The lift and shift phases create time until the S/4HANA migration is complete and the BDC data products are mature.

What challenges arise with open, non-SAP data architectures?

Many organizations choose open architectures that use a variety of tools for data integration, data products, data storage, and consumption.

The main challenge remains access to SAP data. The previously common ODP interface is no longer certified by SAP as a recommended access path. SAP now promotes certified connectors to guide customers toward BDC.

Third-party vendors are therefore developing alternative, often more complex, extraction methods (for example, based on CDS views or open APIs). Organizations choosing a non-SAP analytics path must deploy proven, SAP-tested integration solutions and clarify technical and licensing aspects early. Once extraction paths are defined, they can build a flexible landscape for current and future use cases. They must then validate which components meet requirements and are compatible with each other.

Are hybrid approaches realistic or still experimental?

A mixed scenario is also conceivable: SAP BDC serves as the source for SAP-managed data products that are delivered to partner systems via zero-copy or extraction. However, partner contracts vary and clear guidelines on technical and licensing frameworks are sometimes missing. These initiatives should be carefully planned.

Why is the end of BW maintenance more than a technical issue?

The approaching end of mainstream maintenance for SAP BW 7.5 is a clear signal for organizations to define their strategy.

Systematic modernization can reduce technical debt and increase the long-term value of data, laying the foundation for self-service analytics, artificial intelligence, and real-time analytics.

The decision involves more than a technical data migration. The fundamental choice is where semantics, governance, and business logic will be anchored in the future data architecture. While SAP is more open to partner technologies on the data side, its approach for the semantic layer is to keep it within the SAP ecosystem. Organizations should evaluate this strategic decision consciously.

The situation is complicated by the fact that many BW systems still draw data from the SAP Business Suite, which is also nearing its end of maintenance. Many organizations are already undertaking migration projects to SAP S/4HANA. A future-proof data, analytics, and AI strategy requires a coordinated roadmap that synchronizes the modernization of the source system (S/4HANA) with the target architecture (data products or data and analytics platform).

A further consideration is that SAP does not yet offer fully configured data products for all applications. Organizations choosing the SAP path must therefore implement a phased approach based on strategic priorities.

The challenge also extends beyond technology to organizational structure. In many companies, ERP and data and analytics teams still operate in separate silos. A successful data, analytics, and AI strategy requires these teams to coordinate closely to define responsibilities, data models, and integration paths.

Practical steps help organizations get started

Define Target Architecture and Strategy

• Define future analytics, planning and AI scenarios.
• Establish organizational and technical placement of data management, semantics and reporting.
• Design data architecture considering modern approaches like data products.
• Make strategic decision on modernization path: SAP, non-SAP or hybrid.

Anchor Governance

• Clarify roles and responsibilities early (e.g., data ownership, stewardship).
• Build a committee that integrates ERP and analytics teams.
• Establish processes for data contracts, lineage, security and monitoring.

Conduct BW Inventory

• Analyze currently implemented scenarios and their usage intensity.
• Identify content that can be transferred to operational systems or specialized analytics and CPM solutions.
• Evaluate critical, redundant or outdated BW objects, processes and data flows.
• Define required data products as part of modernization and identify missing products needed to achieve target architecture.
• For SAP strategy: Review available SAP-managed data products and plan necessary additions.

Modernize Iteratively

• Identify quick wins, such as critical reports or data flows.
• Set up a roadmap for modernization that considers all necessary aspects.
• Implement modernization step by step with clear prioritization and transparent progress.
• Use pilot projects before scaling the solution.