Simplifying the complex with SAP DataSphere

Copy of the article by: Matthias_BW

With economic growth and increasing globalization, companies have been paying more attention to quality costs due to rising product complexity and growing customer expectations. In response, the Total Cost of Quality (TCOQ) method was developed to determine and analyze these costs. This approach includes preventive measures, evaluations, and the costs associated with internal and external defects. Effectively managing these key metrics is crucial for optimizing expenses and ensuring customer satisfaction.

To efficiently analyze the various data sources and intricate connections behind TCOQ, a robust data platform is essential. SAP Datasphere enables seamless data integration across different systems, providing a business-oriented context. Beyond advanced data modeling and harmonization, SAP Datasphere allows businesses to consolidate information from both SAP and non-SAP systems using a “business data fabric” approach.

SAP Datasphere also helps manage the total cost of quality by efficiently merging data from multiple sources, including production reports, audit documents, and customer feedback. Users benefit from a semantically enriched data layer that maintains business correlations, forming a solid foundation for precise analyses. These features enable companies to closely track preventive and evaluation costs while systematically reducing defect-related expenses.

With SAP Datasphere and integrated tools like SAP Analytics Cloud, businesses can create real-time dashboards that visualize quality costs, supporting well-informed decision-making. This platform provides a strong foundation for tackling modern quality control challenges and fostering continuous improvements in quality management.

Challenges in Evaluating the Total Cost of Quality: Example Scenario – Manufacturing Company with Subsidiaries

Consolidating a comprehensive evaluation of total quality costs presents practical challenges for many businesses. Data is often collected from different sources in various formats, requiring extensive processing and analysis. This complexity not only makes it difficult to ensure accuracy and timeliness but also consumes valuable resources in financial controlling. The accompanying graphic illustrates a common scenario of this issue.

Scrapping Data via Material Movements

Scrapping data is recorded through material movements and stored centrally in the SAP BW system. The controlling team retrieves this data using Analysis for Office. However, before the analysis is finalized, production management requires an initial review. This review involves internal checks and file storage on a network drive, introducing delays and increasing the risk of errors.

Costs from Quality Management Measures

Costs related to quality management (QM) measures can be accessed through a BW query, which is regularly available. This setup simplifies controlling tasks since the data is immediately accessible without additional processing. However, integrating this data into an overall TCOQ assessment still requires extra steps to ensure a consistent presentation.

Quality Costs from Subsidiaries

Managing quality costs from subsidiaries poses a unique challenge. These costs are provided in CSV format as a byproduct of group consolidation and sent via email to the controlling department. The team must then manually assign the data to relevant cost categories, making the process time-consuming and prone to inconsistencies.

Solution Overview with SAP Datasphere

This example scenario highlights the importance of an integrated data platform like SAP Datasphere in automating processes and unifying data management. A standardized, centralized view of all quality-related costs not only saves time but also improves accuracy and transparency in analyses.

SAP Datasphere enables business teams to utilize comprehensive self-service functions through a graphical modeling interface. The platform's space concept creates a structured environment, isolating data pools while ensuring compliance with IT governance. This allows business users to manage data processes and conduct analyses independently without relying on IT support.

Examples of Self-Service Functions:

• Uploading new data files
• Automating data processes
• Creating mappings and derivations
• Developing analytical models
• Implementing and managing master data
• Documenting data structures and workflows
• Previewing and analyzing data lineage
• Accessing public and licensed data via a marketplace
• Independently sharing data products

These capabilities empower business teams to efficiently utilize data with varying levels of complexity.

Space Concept in SAP Datasphere

SAP Datasphere's space concept provides a structured approach to collaboration between business departments and IT. It streamlines coordination in the development and management of data products.

IT teams can establish centralized connections and data models as data services, ensuring that business teams access standardized and validated data sources. This setup enhances data integration and consistency in analysis.

A unified modeling approach ensures data integration, quality, and security, meeting strict governance and compliance requirements. At the same time, specialized spaces allow business users to work with both central and local data, enabling flexible expansion of analytical models while benefiting from shared data resources.

Data Model in SAP Datasphere – Example Scenario

The SAP Datasphere solution offers multiple advantages for data processing and utilization. One key benefit is the ability to process scrapping data through material movements, integrating it directly into a Datasphere space via BW/4. Alternatively, BW/4’s data model could also serve as a source. Production management now has the option to transfer approved data to a dedicated table for controlling access.

Another major advantage is the improved handling of QM-related costs. This data can be directly integrated into the central TCOQ data pool, facilitating a more efficient and centralized cost analysis.

Furthermore, the management of subsidiary quality costs has been streamlined. Subsidiaries can upload CSV files directly into Datasphere, where source data is automatically archived. The mapping of costs to TCOQ categories can now be configured directly within Datasphere by business teams, significantly simplifying the process.

A major improvement of this solution is the elimination of manual data consolidation. Each department can control access to its data pools, and all business logic for data mapping is stored centrally within one system, improving administration and traceability.

Optimized Project Execution with Parallel Processing

Project execution is divided into multiple phases, which can run in parallel to enhance efficiency. Initially, key characteristics, metrics, and cost categories are defined, followed by the implementation of a table in the target space.

Simultaneously, an analytical model is created using a CSV upload in the target space, which serves as the foundation for reporting in SAP Analytics Cloud. Meanwhile, the development of interfaces to source spaces can begin to integrate data into each designated area. These parallel workflows accelerate implementation and enhance project efficiency.

Key Benefits of SAP Datasphere

SAP Datasphere offers advanced modeling, seamless integration, and flexible use of SAP data in diverse and new combinations. By adopting SAP Datasphere, companies can eliminate data silos from network drives and email inboxes, creating a unified data foundation.

The platform is particularly beneficial for business teams due to its enhanced self-service capabilities, which have been significantly improved compared to SAP BW/4HANA. These scalable self-service features allow for customized adjustments based on business needs. Additionally, the parallel project approach facilitates the efficient implementation of complex KPI systems.

As SAP’s strategic platform for modern data warehousing, SAP Datasphere will continue to evolve with expanded self-service capabilities and enhanced data integration options, including connections to Google Cloud Storage. This approach can also be easily adapted for other KPI systems, such as ESG (Environmental, Social, Governance) or GSRD (Global Supplier Responsibility Data).

A key focus for future development is the structured creation of individual data pools and the exchange of information regarding their use, enabling organizations to implement a holistic data strategy. SAP Datasphere not only meets current requirements but also provides a robust foundation for future data-driven initiatives.

Comment and share: Wagner Sabino (SAP BI Expert)