BDC adds three things standalone Datasphere and SAC do not have: pre-built SAP data products (governed extractors maintained by SAP through every release), natively embedded Databricks for AI/ML, and BDC Connect for zero-copy data sharing to external platforms. Migration from standalone Datasphere to BDC is designed to be non-disruptive: SAP provides a space cloning feature (available from mid-2025) that transfers your existing Datasphere spaces into BDC without rebuilding them. Your SAC stories, models and user training carry forward unchanged. The primary work is licensing transition, data product activation (leveraging the curated extractors instead of custom ones) and Databricks enablement — not a rip-and-replace. Our assessment engagement quantifies the effort and produces a migration plan specific to your landscape.

SAP data products are pre-built, semantically consistent datasets that SAP delivers and maintains within BDC. They extract data from SAP source applications (S/4HANA Finance, Supply Chain, Sales, SuccessFactors HR, SAP CX) using SAP-defined extraction logic, apply SAP's semantic model (standard KPI definitions, consistent master data alignment) and present them as ready-to-consume datasets in Datasphere. The critical difference from custom extractors: SAP maintains the data products through every source system release. When S/4HANA releases a new version and table structures change, SAP updates the data product — you do not rebuild your extractor. For organisations that have spent years maintaining custom BW extractors that break on every EHP or system upgrade, this is the single biggest operational benefit of BDC.

Yes — and this is the ideal timing. BDC data products are designed to align with S/4HANA's data model. Implementing both simultaneously means the S/4HANA functional team and the BDC team coordinate data structures, master data definitions and key figure logic from the start — rather than building the analytics layer after go-live against a source system configuration they did not influence. Having one partner deliver both (which is what 2iSolutions does) eliminates the coordination overhead and the most common analytics failure: a data model designed for a source system that does not match how the source was actually configured.

No. BDC's Capacity Unit model allows flexible allocation across components. A typical first phase activates Datasphere (for data product management and semantic modelling) and SAC (for dashboards and planning) — these are the components that deliver immediate analytical value. Databricks is added when the organisation is ready for AI/ML workloads on SAP data — which may be phase 1 for data-mature organisations or phase 2–3 for others. Object Store is activated when data volumes warrant cost-tier optimisation rather than holding everything in-memory. We design the phasing during the assessment based on your organisation's priorities, data maturity and immediate use cases — not on a one-size-fits-all rollout template.

BDC is a managed SaaS platform — SAP handles infrastructure, updates and availability. But the platform still requires ongoing management: data product monitoring (are loads running successfully?), Datasphere space administration (storage management, replication flow health), SAC content governance (story lifecycle, role management), Databricks cluster monitoring and user support. Additionally, SAP releases new data products and features quarterly — someone needs to evaluate, test and adopt relevant updates. Our BDC Managed Services engagement covers all of this with a dedicated team, SLA-backed response and quarterly enhancement delivery. This is also available as a standalone engagement for organisations that implemented BDC with another partner but need ongoing operational support.