With the end of mainstream maintenance for SAP BW 7.5 in 2027, many companies are entering a phase of strategic realignment. At our “Future of SAP Data & Analytics” event, around 150 data and finance professionals from SAP customer organizations recently exchanged views. One message was clear: The end of SAP BW is not just a technical milestone. It is a trigger for rethinking data architecture, analytics, and AI capabilities.
Why companies need to act now
The pressure to act is both urgent and strategic.
- By 2027 / 2030 (extended maintenance), core reporting and management processes will be at risk.
- AI ambitions and use cases become difficult to execute as companies almost always have to manage a hybrid setup (SAP and other data platforms).
- S/4HANA programs: In many organizations, ERP teams often treat BW as secondary, yet it remains a critical risk for leadership—reporting and management typically depend on it.
Cost pressures are mounting now and will intensify by 2027. Companies report very different cost profiles:
- BW landscapes (including test systems, operations, and maintenance) can be very cost-intensive.
- BDC can be cheaper, but only with clear governance, clean usage patterns, and controlled workloads.
- Hybrid can be powerful, but without clear responsibilities it quickly becomes expensive (parallel stacks, duplicated semantics, duplicated pipelines).
Competitive advantage doesn’t come from technology alone—it comes from architecture and the operating model.
The message from SAP users was clear: Automation, AI, and data products require a stable semantic foundation (e.g. definitions, KPIs, calculation logic, roles, access, change process) backed by an operating model that sustains it.
The three strategic architecture options
1) SAP-centric (BW/4HANA or Business Data Cloud)
For companies that want to stay primarily in the SAP ecosystem (integration, SAP-aligned governance). This delivers stability and coherence but limits flexibility. What matters most is the SAP target architecture and the maturity level.
2) Non-SAP architecture (for example, Databricks, Snowflake, Microsoft Fabric)
For companies with a strong focus on advanced analytics and AI workloads, or a low share of SAP data. This enables faster innovation but increases integration complexity and requires ownership of semantics and access management outside SAP.
3) Hybrid (SAP plus partner platforms). A trend, but still full of open questions
In practice, SAP analytics systems typically handle reporting and analysis on SAP data, while partner platforms cover diverse data source integration, faster AI workloads, and more complex data engineering. That combination can play to the strengths of both approaches, but it can also create extra overhead and confusion. Many companies are now evaluating whether BDC actually supports this setup, particularly whether SAP data products and “zero copy” integration with other platforms go far enough.
Key Takeaways
- The biggest risk isn’t technology, it’s organizational alignment. Without end-to-end ownership, teams create backend silos that generate friction and delay.
- “Zero copy” is a relevant promise, but it must prove itself in practice. Success depends on workload, governance, performance requirements, security setup, and tooling choices. The central questions are: What do we replicate? What do we virtualize? Where is the source of truth (semantics)?
- AI creates value only where roles, data logic, and usage are clearly defined. Joule, SAC, Copilot, Databricks. Without a shared semantic foundation and clear ownership, these are just tools, not value drivers.
- CxOs need to synchronize three roadmaps: S/4HANA, data architecture, and AI strategy. Siloed decisions create years of friction and often lead to parallel stacks.
What decision-makers should do now (12–24 months)
- Choose your target architecture deliberately: SAP-centric, hybrid, or non-SAP. ‘Wait and see’ is not a strategy.
- Clarify semantics and governance: Who owns definitions and KPIs, models, data products, and access?
- Assess your current BW landscape: What’s mission-critical? What can be retired? What should move to operational systems vs. new data products?
- Run modernization pilots to create fast value, learn early, and control costs.
- Synchronize the transformation: Treat S/4HANA together with data architecture and AI as one jointly governed program.
Conclusion
The end of SAP BW is a strategic checkpoint for every data organization. Organizations that act deliberately now will gain:
- Cost control and predictability (no more surprises from parallel stacks)
- Faster AI and automation deployment
- Greater organizational clarity (ownership, decision rights)
- Lower integration and lock-in risks
- Stronger execution on data-driven initiatives
Next Steps for Executives
If you’re evaluating your SAP options, we can support you in an executive dialogue. We provide independent guidance across implementation partners and platform ecosystems.
We offer:
- Orientation on architecture paths (including pros and cons depending on context)
- Assessment of costs, risks, and options
- Governance and semantics assessment
- Dependencies across S/4HANA and AI strategy
- A decision template for the executive board and management
- Comparison of implementation partners, hyperscalers, and relevant platform partners
