Building a data platform is a crucial step toward becoming a data-driven company or an Autonomous Enterprise. This is because high-quality data with high availability is a prerequisite for the productive use of artificial intelligence (AI). SAP Business Data Cloud (BDC) is SAP’s strategic solution for establishing a unified data platform. Since the official launch of SAP BDC in March 2025, SAP has been continuously working to expand its functionality and provide additional features. So it’s high time to take a look at what has happened with SAP BDC over the past 18 months.
Since its official launch in March 2025, the SAP Business Data Cloud has been SAP’s strategic SaaS solution for consolidating SAP and non-SAP data on a unified platform—serving as the foundation for the Autonomous Enterprise.
Through SAP BDC Connect, the platform exchanges data bidirectionally with the data platforms of Databricks, Snowflake, Google, AWS, and Microsoft without the need to replicate data.
SAP Knowledge Graph forms the semantic knowledge layer of SAP BDC: It describes the meaning, relationships, and context of business data, thereby providing AI agents with a reliable data foundation to avoid hallucinations.
The SAP BDC roadmap includes, among other things, versioning of data products, Agentic Skills as part of the “All in on AI” program, and—following the acquisition of Reltio—data quality standards for data products.
SAP Business Data Cloud is a software-as-a-service (SaaS) solution that consolidates, processes, and manages all data from SAP systems on a unified platform. In addition, SAP BDC enables seamless integration with non-SAP data. In this way, business data from all areas of a company is consolidated into a semantically rich data repository, enabling SAP BDC to serve as a central hub for all business-critical data.
SAP BDC is characterized by the following features and functionalities, among others:
Currently, over 300 data products are available for the various SAP cloud solutions
SAP BDC Connect enables bidirectional data exchange with the data platforms of leading providers such as Databricks, Snowflake, Google, AWS, and Microsoft. This means that platform providers can access SAP data without having to replicate it. Conversely, it enables SAP to aggregate data without first consolidating it at a central location.
Integration of SAP HANA Cloud and SAP Master Data Governance (MDG) is now also possible within your SAP BDC contract. Corresponding capacity units can be distributed and allocated as needed.
Similar to SAP Business Warehouse (BW), SAP MDG is now also available as a Private Cloud Edition (PCE) within the SAP BDC.
The Data Product Generator enables data and info objects built in SAP BW to be used in SAP Datasphere as well as in AI and machine learning scenarios.
The integration of AI features into SAP BDC is steadily progressing, for example in the workflows within SAP Analytics Cloud (SAC).
With a view toward the autonomous enterprise – which is primarily based on the SAP Autonomous Suite and SAP Business AI Platform – SAP Business Data Cloud plays a crucial role as a key component of the data layer. It provides access to both SAP and non-SAP data models. Ultimately, SAP BDC provides the high-quality data foundation needed for the interaction between AI, data, and processes to function effectively.
AI agents achieve their greatest performance in an enterprise context when they combine SAP and non-SAP data. To execute assigned tasks correctly and purposefully, AI agents must know which data is required for that purpose. Access to AI-ready data sets prevents “hallucinations.” In addition, SAP Knowledge Graph creates a shared understanding of business information by describing the meaning, relationships, and context of data, thereby forming the indispensable semantic knowledge layer. SAP Knowledge Graph is fed by the data products.
The SAP Business Data Cloud roadmap is designed to continuously evolve SAP BDC into the central data and AI foundation for the Autonomous Enterprise. SAP has set a goal of delivering new features for SAP BDC at a rapid pace. Here’s a look at the key development priorities announced for the coming months:
Versioning of data products prevents the need to rebuild an entire branch when changes are made to a data product (such as adding or modifying a field).
Lifecycle management for the lakehouse architecture is being established, similar to what is found in database systems.
The onboarding time for data products has already been significantly reduced (from hours to minutes) thanks to improved creation and optimized management of data products.
The “All in on AI” program calls for the integration of agentic skills into SAP BDC workflows. This requires automating many of the previously manual steps using agents.
By integrating master data management functionalities into SAP BDC following the acquisition of Reltio, data products are now equipped with data quality checks. As a result, AI agents only access those data products that meet a certain quality standard.
Investments are being made in partnerships for bidirectional data sharing to enable seamless communication between SAP AI agents and other agents that a company has already built on other platforms.
The Query Template Generator helps companies modernize their SAP BW landscape. It is capable of translating created SAP BW queries into analytical models.
The SAP BW Migration Assistant is designed to use AI to reduce the effort required for a complete SAP BW migration to SAP Datasphere.
SAP Business Data Cloud paves the way for the Autonomous Enterprise. It unifies data from various sources, provides context for that data, and thus forms the basis for informed decisions and the successful deployment of AI. After all, powerful AI applications are only as good as the data on which they are built.
Modern data landscapes are complex. Therefore, companies should establish their data platform in a targeted and phased manner – with a clear focus on data quality, metadata, and consistent semantics. Companies that use SAP BW are in a favorable position: The existing semantic structure of their data facilitates integration with modern AI environments and lays the groundwork for successfully moving to SAP Business Data Cloud and fully realizing the potential of SAP BDC over the long term.