The SAP open data ecosystem is a strategic framework that creates a unified environment by connecting all business data, whether it originates from SAP systems or external sources.
This ecosystem focuses on two primary objectives:
- Extending the native capabilities of SAP Business Data Cloud by integrating specialized external tools.
- Enabling the use of preferred analytical tools to process combined datasets, resulting in deeper insights and more informed decisions from a single, reliable source.
This lesson focuses specifically on how to extend the capabilities of SAP Business Data Cloud.
While SAP Business Data Cloud provides a robust foundation, no single platform can address every specialized requirement. The SAP open data ecosystem allows you to integrate best-of-breed tools with your SAP data to enhance its quality, governance, and overall value.
The following two use cases illustrate the practical benefits of the open data ecosystem for SAP Business Data Cloud.
Central Data Governance
The first use case addresses the challenges of data governance.
Watch this video to understand how external data governance and cataloging functions can be integrated into SAP Business Data Cloud.
Organizations often struggle with enterprise-wide data governance when information is fragmented across multiple systems. While SAP Business Data Cloud manages its own internal data effectively, enforcing global compliance policies—such as GDPR—across all other applications and databases remains a complex task.
The solution involves integrating SAP Business Data Cloud with a dedicated data governance platform, such as Collibra. This integration allows the platform to harvest metadata from SAP and other sources into a single, searchable catalog. A unified catalog enables data stewards to apply universal quality rules and access policies centrally, which reduces compliance risks, increases data trust, and accelerates the delivery of insights.
Pre-trained Models Use Case
The second use case explores the application of machine learning.
Developing custom models is resource-intensive. You can achieve faster results by utilizing industry-specific, pre-trained models to solve business challenges.
Watch this video to learn how pre-trained models can be used for predictive maintenance.
Building custom AI and machine learning models is often difficult due to time and expertise constraints. A more efficient approach is to leverage pre-trained, industry-specific AI models.
Instead of starting from scratch, companies can adapt existing frameworks from platforms like DataRobot and fine-tune them using their own high-quality, context-rich business data stored in SAP Business Data Cloud.
The video features a utility company case study focused on predicting equipment failures. By applying a pre-built model and refining it with specific maintenance and sensor data from SAP, the team can rapidly develop a predictive tool. This strategy allows them to anticipate failures, resulting in reduced operational downtime, lower maintenance costs, and improved service reliability.
Summary
In this lesson, you learned how SAP Business Data Cloud integrates with specialized tools to enhance its core capabilities:
- Platforms like Collibra connect to SAP Business Data Cloud to enable centralized data cataloging and consistent policy enforcement across the enterprise.
- Integration with governance tools minimizes compliance risks by applying global data quality rules and access policies from a central location.
- You can leverage platforms like DataRobot to access industry-specific models, using clean data from SAP Business Data Cloud for faster AI deployment.
- Combining SAP Business Data Cloud with specialized models supports predictive analytics, which helps reduce downtime and improve overall business outcomes.