SAP BDC: Intelligent Applications and Data Products for a Successful Start
Intelligent Applications and Data Products are a key building block of modern, data-driven enterprises. In the SAP ecosystem in particular, the SAP Business Data Cloud (SAP BDC), modern data-sharing mechanisms, and new development tools are creating numerous opportunities—but also introducing new requirements for architecture, governance, and strategy around Intelligent Applications and Data Products. In this article Joseph Reinke, Junior Process Automation Consultant, explains how companies can get started productively with SAP BDC today while at the same time building a future-proof strategy for Intelligent Applications and Data Products.
Ready for Intelligent Applications and Data Products?
Before technologies are implemented, clarity is needed regarding the target vision, level of maturity, and concrete use cases. The same applies to the SAP Business Data Cloud (BDC). So how should one proceed? To begin with, a shared understanding of Data Products must be established in order to create the foundation for all subsequent steps.
The goal is to understand the differences between SAP-managed and customer-developed Data Products and, based on this, to develop a robust strategy. At the same time, it is of course possible to define initial prioritized use cases that can deliver rapid business value.
SAP BDC provides a solid foundation
SAP-managed Data Products are clearly defined, including their semantics and metadata. Access is provided via modern zero-copy mechanisms such as APIs, events, or Delta Sharing, enabling data usage without physical replication. In addition, the Business Accelerator Hub supports structured discovery and evaluation of existing Data Products.
It is also advisable to conduct a Data Product Maturity Assessment, in which existing data sources such as BW, S/4HANA, SAP SuccessFactors, or non-SAP systems are analyzed in terms of their suitability. Based on this, architectural decisions can be made—for example, whether to use SAP-managed Data Products, build custom Data Products, or deliberately defer certain developments in anticipation of future platform capabilities.
Another key building block is the definition of a Data Product Operating Model, which establishes roles, responsibilities, and governance structures.
Building Intelligent Applications with Available Tools
Even without future enhancements, powerful data products can already be implemented today. Companies can rely on established tools and immediately generate business value.
What are BW-based Data Products?
With the BW Data Product Generator, existing BW data models can be efficiently transformed into reusable data products. The key advantage is that existing structures can continue to be leveraged without requiring a complete re-modeling effort.
As part of the implementation, the existing data models are first analyzed and suitable InfoProviders such as ADSOs, CompositeProviders, or MultiProviders are identified. The next step is the generation of the data products and their integration into various consumption environments such as SAP Analytics Cloud, Databricks, or HANA Cloud.
The business value is primarily reflected in the rapid availability of usable data products, as well as in automated delta handling and harmonization via Foundation Services.
Custom Data Products with Data Sharing Cockpit
For scenarios that require a higher degree of flexibility, the Datasphere Delta Sharing Cockpit provides an established and stable foundation for creating custom data products. In this approach, modeling is performed directly in SAP Datasphere, where domain-specific semantics are built.
The resulting data products can then be published via the Data Sharing Cockpit to both internal and external catalogs. A particularly important aspect is the ability to integrate not only SAP data but also non-SAP sources—for example, through replication flows from cloud storage systems or streaming platforms.
Future developments: Data Product Studio & Interface Data Products
Since 2026, new features will be introduced that further extend working with data products.
These include:
- Data Product Studio for lifecycle management, versioning, and deployment
- Interface data products with predefined SAP schemas
These are particularly important for intelligent applications such as Spend Analytics & Insights.
During the transition phase, the following topics are relevant:
- Migration from the Data Sharing Cockpit to the Data Product Studio
- Alignment of customer-specific fields with SAP schemas
- Definition of mapping strategies
- Ensuring semantic compatibility
- Coordination of release and roadmap planning
It is important to note that, initially, the focus is primarily on 1:1 mappings.
Intelligent Applications
With intelligent applications, a new application scenario emerges in which data and processes work more closely together.
In the first step, the focus is on:
- Data readiness checks
- Activation and population of the required data products
- Ensuring consistency with interface data products
Another important aspect is insight-to-action processes, in which data is directly translated into actions. In the future, companies will also be able to develop their own applications: Via CAP-based developed apps on data products or using reusable UI components.
Operations & governance
Operational excellence plays a central role in long-term success. Since 2026, new opportunities have emerged, particularly in the area of Data Product observability, which provides greater transparency regarding data freshness and potential issues.
Key operational topics include:
- Monitoring and alerting for early detection of issues
- Structured lifecycle management for Data Products
- Clearly defined governance structures
Governance aspects in particular include:
- Versioning of Data Products
- Well-designed release strategies
- Ensuring high metadata quality
- Appropriate access and security models
SAP Managed vs. Custom Data Products
SAP Managed Data Products provide:
- Harmonized and semantically enriched data
- A centralized “single source of truth”
- A foundation for Intelligent Applications
They can also be used for reporting, planning, and analytics, and extended with Datasphere, SAP Analytics Cloud, or Databricks. Custom Data Products offer maxim…
AI- and ML-Use Cases
Data products serve as a foundation for various AI and ML scenarios, especially in combination with Databricks.
These examples illustrate that data products form the foundation for data-driven applications.
Architecture
The architecture of Data Products is based on several key building blocks that together create a consistent and high-performance data foundation.
Conclusion
Intelligent applications and data products can already be implemented today. Organizations can start with existing tools while simultaneously taking future developments into account. What matters most is the interplay between strategy, architecture, governance and use cases. This creates a foundation for a data-driven organization that is successful both today and in the future.
Are you ready to get started with Intelligent Applications and Data Products? Feel free to reach out to us!
