Data & Cloud

SAP Business Data Cloud: why your data strategy now decides your AI results

Frontino IT 3 min read

Every AI conversation in SAP eventually reaches the same point: data. An agent that adjusts orders or answers finance questions needs reliable data and the business context around it. That is the role SAP gives to SAP Business Data Cloud (BDC).

What SAP Business Data Cloud is

SAP Business Data Cloud brings SAP data together as governed data products that keep their business meaning, so analytics and AI can use them without rebuilding the semantics of every table. It builds on SAP Datasphere and SAP Analytics Cloud and, since Sapphire 2026, it is one of the three pillars of the new SAP Business AI Platform, alongside SAP BTP and SAP Business AI.

Recent developments

  • Databricks. BDC Connect for Databricks arrived in 2025, alongside SAP Databricks as part of BDC.
  • Snowflake. SAP Snowflake and BDC Connect for Snowflake became generally available in 2026, with bidirectional data sharing that keeps business semantics and governance intact.
  • Google BigQuery. BDC Connect for Google BigQuery is generally available as well.
  • More to come. SAP HANA Cloud is becoming a core component of BDC, and BDC Connect for Amazon Athena is planned for the second half of 2026.

The direction is clear: instead of copying SAP data into every platform, data is shared with its meaning attached, often without physically duplicating it.

Why this matters for AI

AI agents act on business context: which supplier, which contract, which open order, which tolerance. When that context lives in clean, governed data products, agents make better decisions and their actions are easier to explain. When it lives in spreadsheets and unofficial extracts, no model can compensate.

No AI strategy without a data strategy. The companies that will benefit first from SAP's agents are the ones that already know where their critical data lives, who owns it and how good it is.

How to get your data ready

  1. Start from use cases, not from tables: which decisions do you want to improve, and which data do they need?
  2. Fix data quality at the source. Master data in SAP is the foundation; cleaning it during an ERP project pays off twice.
  3. Replace unofficial extracts with governed data products and clear ownership.
  4. Connect, do not copy, wherever your analytics platform supports semantic, zero-copy sharing.

Sources

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