What SAP's Business Data Cloud Means For Enterprises

Forbes

09-04-2025

I recently spent time in New York City with SAP leadership as they unveiled the company's Business Data Cloud. I think the BDC represents a significant shift in enterprise data management for SAP because it integrates both structured and unstructured data from various SAP applications as well as external sources. Besides its advantages for data management, it represents a key part of SAP's AI strategy.
BDC leverages a partnership with Databricks to improve data access and governance, which benefits users of SAP S/4HANA Cloud ERP while also enhancing Joule, SAP's AI assistant. BDC offers several advantages, including simplified data integration, potential cost savings on data storage and enhanced AI-driven analytics capabilities. It creates new opportunities for customers, partners and their developers and data scientists. The platform also enables new roles that combine technical expertise with business acumen, such as a BDC architect who designs and implements new solutions for organizations. In essence, SAP BDC is set to transform the data landscape for SAP customers into a dynamic environment ripe for innovation and collaboration. Let's look at how BDC benefits SAP customers, the broader ecosystem and Databricks — and how it supports SAP's transformation into a cloud-first ERP provider.
(Note: SAP is an advisory client of my firm, Moor Insights & Strategy.)
SAP's BDC consolidates data from SAP HANA Cloud databases, SAP Datasphere (formerly SAP Data Warehouse Cloud), SAP Analytics Cloud and SAP Business Warehouse into a unified platform. SAP Datasphere functionality keeps business rules and metadata intact, which makes data easier to access and interpret, regardless of its original format or storage location.
The intent is to reduce data silos and make it easier to discover, combine, manage, govern and analyze data by applying data fabric principles. A data fabric is an architecture that supports consistent data management and access across different systems. I'm seeing many organizations adopt these principles to handle their growing data needs.
BDC also integrates with non-SAP data sources including relational databases like Oracle Database and Microsoft SQL Server, cloud storage services such as Amazon S3 and Google Cloud Storage and enterprise applications such as Salesforce, ServiceNow and Workday. This helps enterprises with hybrid IT environments unify their data for a more complete view. Because of this, BDC should be especially valuable for enterprises that use SAP alongside third-party systems such as Salesforce, ServiceNow or Oracle to handle customer data.
As a core part of SAP's AI strategy, BDC provides a unified data foundation for AI-driven automation. Beyond that, it strengthens Joule. Enterprises can customize Joule AI agents or develop proprietary AI solutions, leveraging the SAP Knowledge Graph to connect data across systems for more effective AI model training. Meanwhile, Databricks' collaborative tools enable real-time data processing, supporting use cases such as fraud detection and supply chain monitoring. By integrating SAP data with AI applications while preserving business semantics, BDC can deliver critical insights without disrupting workflows, as well as enhancing machine learning, automation and forecasting.
By eliminating complex data extraction and replication, BDC provides enterprises with real-time, reliable data in one place. Centralizing data management with BDC — especially because it integrates seamlessly with SAP ERP's core operational functions — could improve business operations and compliance while reducing costs across finance, supply chain, HR, customer experience and sustainability. Let's look at how this might play out in some different areas of enterprises.
In finance, BDC can integrate real-time financial data from SAP S/4HANA with external economic indicators, potentially improving forecasting while automating reporting. Automation should reduce manual reconciliation and accelerate financial close cycles. CFOs can also model different market conditions — such as inflation scenarios or supply chain disruptions — to assess potential revenue and profitability impacts. Among other possible benefits for financial agility, enterprises might be able to adjust budgets more quickly in response to fluctuating exchange rates.
For supply chains, BDC combines SAP and non-SAP data to enhance visibility, optimize inventory and improve logistics monitoring. Predictive analytics help enterprises anticipate disruptions like raw material shortages or supplier delays. A retailer could use real-time demand sensing to adjust stock levels across distribution centers, reducing excess inventory while ensuring product availability.
HR teams stand to benefit from integrated analytics for talent management, workforce planning and compliance reporting. Predictive modeling can help anticipate employee turnover, allowing HR to implement targeted retention strategies. For example, an enterprise might identify departments with higher attrition rates and introduce engagement initiatives to reduce recruitment costs and maintain workforce stability.
BDC could enhance customer experience by integrating SAP CRM data with external sources like social media and customer feedback. AI-driven segmentation and real-time behavior analysis might enable enterprises to refine engagement strategies and personalize interactions. A telecom company, for instance, could detect patterns in customer service inquiries and proactively offer tailored solutions to improve satisfaction and reduce churn.
BDC also centralizes ESG data, automates compliance reporting and enables real-time carbon footprint monitoring. AI-powered scenario planning helps enterprises meet regulatory and sustainability goals. For instance, a manufacturer could track energy consumption across global facilities, identify inefficiencies and implement targeted reductions to align with corporate sustainability commitments. For more on this topic, see my overview of sustainability data practices, as well as my analysis of the Sustainability Data Exchange that SAP launched last year.
The Databricks partnership enhances enterprise data management by integrating SAP's structured and unstructured data with Databricks' AI and analytics capabilities. One of Databricks' standout features is its zero-copy data sharing through Delta Sharing, which eliminates redundant data replication, reducing storage costs and simplifying data governance. Additionally, its Apache Spark-based processing engine enables real-time analytics, predictive modeling and AI-driven decision-making. For example, a manufacturing plant might use Databricks to integrate IoT sensor data with SAP maintenance records in BDC to enable predictive maintenance, helping to prevent equipment failures, reduce downtime and improve operational efficiency.
The SAP/Databricks partnership enables SAP to expand its AI and data analytics footprint by simplifying the process of using SAP data for advanced analytics and machine learning. This collaboration accelerates AI and machine learning initiatives, shortens time to value and enables more modern, scalable AI-driven use cases beyond traditional reporting and BI. All in all, this partnership positions SAP to deliver more value from data in today's competitive data-driven environment.
Beyond Databricks, BDC also supports interoperability with platforms including Google Cloud, Collibra and Confluent, allowing enterprises to manage and analyze diverse data types across various environments.
Moving to SAP BDC will be a key step toward modernization for many SAP customers, but some customers currently using SAP's on-premises ERP may be hesitant to make the shift because of concerns over data migration complexity, potential downtime during the transition and the need to retrain staff on new cloud-based workflows. In New York, I spoke with SAP CEO Christian Klein, who emphasized the importance of supporting legacy customers through this transition. We can expect SAP partners to play a key role in easing the shift, helping enterprises adopt the cloud-based solution more smoothly.
SAP also has two well-established in-house programs to aid with these transitions. RISE with SAP, launched in 2021, streamlines migration with tailored tools and services like automated conversion and code analysis. Ongoing support includes business process intelligence and a guided adoption framework for legacy customers to modernize at their own pace while minimizing risk. The GROW with SAP program, introduced in 2023, accelerates SAP S/4HANA Cloud Public Edition adoption for mid-sized companies and scaleups by offering preconfigured best practices, embedded AI and automation. The company touts this approach as reducing implementation timelines from years to months. Together, RISE with SAP, GROW with SAP and BDC expand SAP's cloud offerings and provide flexible modernization options.
Note that BDC operates differently for Databricks and non-Databricks customers. Non-Databricks users can still access BDC's native data integration capabilities through SAP Datasphere, but they won't have access to Databricks' advanced AI and machine learning features. For SAP Cloud ERP customers, Databricks functionality is not included by default. It is offered as an optional component within the BDC subscription and is priced based on BDC Capacity Units. Customers must purchase enough units to enable and utilize SAP Databricks capabilities.
It's important to remember that companies must also bridge skill gaps between SAP-centric and Databricks teams. While the benefits seem compelling, organizations should evaluate the added cost and complexity.
Customers considering SAP's BDC may face several challenges. Since BDC is a managed cloud service, it does not support on-premises strategies, limiting its appeal for enterprises that are committed to on-premises infrastructure. Additionally, enterprises that have built their own data semantics or rely on third-party technologies may see little benefit from SAP's predefined content.
While I think the Databricks partnership benefits both companies as well as their customers, there could be concerns about long-term interoperability down the line, especially if Databricks were ever to be acquired by an SAP competitor. The Databricks platform's bundled approach, combining analytics, semantics, data integration and storage, could also lead to vendor lock-in, making it difficult to replace individual components or integrate non-SAP services. Customers may also end up paying for redundant data and analytics components, mainly if they use similar tools outside BDC. In particular, pricing concerns exist for SAP Business Warehouse customers, although the company has partially addressed how it will prevent these customers from paying twice when transitioning to BDC.
Beyond that, SAP BW developers may need to adapt to BDC's relational modeling approach, which is different from SAP BW's guided application framework. This transition requires learning new data modeling concepts and techniques. Pre-built data products for S/4HANA and finance in BDC can help mitigate this learning curve by providing ready-to-use datasets. These data products offer a starting point for analysis and can be customized to fit specific business needs, potentially easing developers' workload transition.
The data warehouse and analytics market offers several alternatives to the SAP/Databricks solution, with varying integration complexities for potential SAP BDC customers. Oracle is a notable option for those seeking a single-vendor solution for both ERP and data management. For organizations looking to combine SAP with other data warehouse solutions, options include cloud-based platforms such as Snowflake, Google BigQuery, and Amazon Redshift; analytics platforms such as Azure Synapse Analytics; open-source and specialized solutions including Dremio, Teradata and Cloudera Data Warehouse; and enterprise solutions including IBM Db2, Oracle EPM and Microsoft SQL Server.
The ease of implementing these alternatives depends on existing SAP infrastructure and specific business requirements. Some solutions may offer simpler integration, while others might require more extensive customization and data migration. The choice should be based on factors including scalability needs, AI integration requirements, analytics capabilities and long-term strategic alignment. Each option has its strengths and limitations, and the best fit will vary depending on the organization's specific context and goals.
SAP's introduction of BDC improves its cloud and AI strategy. With the Databricks partnership, SAP improves AI-driven analytics, governance and operational efficiencies for its customers while delivering real-time insights across finance, supply chain, HR and customer experience.
Certainly, the challenges mentioned above — for companies with on-premises commitments, pre-existing data architectures or concerns about vendor lock-in — mean that enterprises should carefully evaluate the transition. But for enterprises committed to SAP's ecosystem, I believe BDC presents an important opportunity to modernize data management while leveraging AI-powered automation. The choice will ultimately depend on an enterprise's specific data strategy, infrastructure investments and long-term digital transformation goals.