Combining SAP data with information from other business areas increases its value exponentially. The objective is to integrate your rich SAP data into central data and AI platforms as a first-class citizen, avoiding the complexities of traditional data extraction.
While SAP Datasphere offers extensive functionality, you may have already invested in other solutions for data warehousing, analytics, or custom AI development.
SAP provides several ways to leverage SAP Business Warehouse (BW) content through BW modernization strategies.
Furthermore, the open data ecosystem allows you to maintain your existing investments in third-party solutions while benefiting from out-of-the-box, curated SAP data products and advanced features like the knowledge graph.
The following use cases demonstrate how the open data ecosystem simplifies data sharing with SAP Datasphere.
Centralized Analytics
The first use case focuses on centralized analytics.
Watch this video to learn how sharing SAP data with external platforms provides a comprehensive 360-degree view of your business operations.
A frequent business challenge is data fragmentation across siloed systems, which hinders a holistic view of operations. For instance, connecting marketing data from social media and web analytics with sales data from an SAP system is often difficult when trying to map the complete customer journey.
Traditional integration methods, such as ETL (Extract, Transform, Load) pipelines, are often slow and expensive. They can also strip data of its business context, resulting in cryptic technical information that is difficult for business users to interpret.
A modern solution involves using the SAP open data ecosystem to share live data directly from SAP to cloud data warehouses like Snowflake or Google BigQuery, without the need for data replication. A key advantage is that the data retains its business context; information arrives with clear, understandable labels (such as "Net Sales Amount") rather than obscure technical codes.
Preserving this business context allows analysts to easily merge operational data with other sources. This improved access enables organizations to achieve a 360-degree customer view, accurately measure marketing ROI, and empower teams to make faster, data-driven decisions.
Custom AI Use Case
The second use case explores custom AI development.
Data scientists typically use specific tools to develop custom models. Sharing SAP data directly with these preferred tools accelerates the development lifecycle.
Watch this video to learn more about a custom AI development scenario.
Data science teams can build more accurate predictive models, such as for demand forecasting, by combining historical enterprise data with external variables. However, critical business data is often locked in complex SAP structures, making it difficult for data scientists who prefer working with languages like Python to access it efficiently.
The SAP open data ecosystem addresses this by providing a simplified, business-focused "semantic layer" over complex source data. This layer allows data scientists to use minimal code to pull clean, curated business information directly into their preferred environments without needing to understand the underlying database schemas.
This streamlined access empowers teams to innovate rapidly. By integrating internal enterprise data with external factors, they can develop precise models that lead to optimized inventory, reduced waste, and fewer lost sales due to stockouts.
Summary
In this lesson, you learned how the SAP Datasphere open data ecosystem allows you to combine SAP data with other business data in central platforms without complex extraction processes.
- You can share live, business-ready SAP data with cloud warehouses to create a comprehensive 360-degree business view.
- You empower teams to build advanced models and insights faster by simplifying governed data access.
- You maintain semantic meaning during data sharing, making information immediately usable without requiring deep SAP expertise.
- You enable data scientists to access curated SAP data using familiar tools such as Python and Databricks.