Case Study

Unified. Trusted. AI-Ready: How a Mid-Sized Manufacturer Future-Proofed Its Data with SAP Datasphere—Saving $180K Annually

Manufacturing
Data Engineering

About the Client​

The client is a manufacturer of laser tools that are in turn used by electronics companies to cut, drill, and shape flexible Printed Circuit Boards (PCBs). In addition to their core offerings, the client also produces high-speed testers that support MLCC (Multi-Layer Ceramic Capacitor) testing — a critical function in electronic component quality assurance.

Business Challenge​

The Client had been using multiple application systems to track the everyday operations. These included SAP modules, non-SAP systems, and custom applications, which act as data sources and feed into the centralized On-Prem SAP HANA data platform. However, when it came to reporting and analysis, each department followed their own approach. Some teams used SAP BusinessObjects. Others relied on Excel. Reports were built differently. Every department had their version of metrics and KPIs. Logic was often duplicated or misaligned.

At a time when the business was expanding, the leadership team set out to implement the following:

  • Modernize, unify and standardize data use across departments.
  • Data from MES, PLM, or IOT systems that required custom ETL need to blend seamlessly with data from the SAP Ecosystem.
  • Address the demand for fast access to Data and Self-service analytics by each business unit with minimal or zero dependence on IT for data retrieval or preparation.
  • Ensure that the Data architecture supports the business growth and is designed for scale, speed, security, performance and global access.

Trigent Solution

DataSphere decision explained

As their data engineering services partner, Trigent observed that the client had already made significant investments in SAP infrastructure. Exiting the SAP ecosystem was not practical from both a cost and integration standpoint.
The real decision was not whether to stay within SAP, but which alternative solution offered the best balance of Agility, Cost-efficiency, Cloud-first architecture, Self-service capabilities, and more importantly help the client achieve a unified data reporting infrastructure.
Given all the above criteria, Datasphere emerged as a best-fit solution. Notably, the platform also offered both cloud fabric and business fabric layers. Cloud fabric stitches disparate data into one connected system, and a business fabric layer aligns technical data to business-terms and domain-specific meanings. In summary, Dataspere would enable a cohesive logic and reporting layer grounded in strong data harmonization.

Solution Implementation

A successful migration depended on configuring Datasphere for three critical outcomes

Implement cost-efficient data harmonization strategy for unified logic and reporting.

Strike the right balance between cloud cost and performance.

Enable self-serve analytics within a secured and governed environment

Unified Logic for Federated, Self-service Analytics

In the client’s previous SAP HANA environment, all core data logic, and enterprise-wide structures were built using a heavy SQL-centric approach, with its views (base, composite, and calculation) fully managed by the central IT team. On the other hand, the departmental-level KPIs and metrics were defined by the individual business teams, but these domain-specific definitions were not always aligning to the core enterprise logic.
Trigent re-architected using Datasphere’s layered and space-based architecture, which ensures the bases business-level analytics (business builder) is aligned with technical data models (data builder).
In the Data Builder, Trigent rebuilt the foundational enterprise logic. This became the IT-managed layer. Using the Business Builder, Trigent empowered departmental teams to build their own KPIs and analytical models that were again based on the trusted logic exposed in the data builder.
This separation of responsibilities enabled a federated self-service analytics model that allowed analysts to work independently with less reliance on the central IT.

Model-level optimization that ensured superior performance yet lower TCO

Trigent recognized that optimization had to happen at the model level to minimize compute load without compromising on performance. Engineers removed complex and redundant joins across large tables, flattened data models where appropriate, and ensured that filters were applied at the source to reduce unnecessary data processing. Some of the expensive transformations were pre-calculated and stored as materialized views (snapshots) to avoid repeated computation during runtime. Some calculation views (logics) were referenced once and shared as reusable components to prevent duplication.

Achieved cost-efficient data harmonization by reusing ETL logic to onboard new non-sap sources

Trigent reused existing ETL logic to onboard new non-SAP data sources, avoiding the need to rebuild pipelines from scratch. This ensured faster integration and reduced development effort, maintaining consistency across data ingestion processes. By applying the same transformation rules used for SAP data, non-SAP sources were aligned to common data models. As a result, data from diverse systems was harmonized into a unified structure, supporting enterprise-wide reporting.

Secure self-service enabled through space-level isolation, role-based access, and audit-driven governance

The objective was to prevent overexposure to data while still enabling users to self-serve. Trigent implemented space-level isolation that clearly delineated the IT-owned core space (one that hosted sensitive, enterprise-level models) from the business spaces (where individual teams built their own calculation views). Business teams were given controlled access to shared models, but sensitive fields were masked, hidden, or excluded by design, based on user roles and responsibilities. To maintain accountability and trust, audit trails were activated to track who accessed what, and when—ensuring that self-service analytics operated within a secure and governed environment.

Conclusion

This case study reflects how a small to mid-sized manufacturer moved from a fragmented, inconsistent data environment to a unified, trusted, decision-ready platform. Before the transformation, not all data sources were integrated, logic varied across teams, and reports often painted incomplete or conflicting pictures. With Trigent’s guidance, the client brought structure to their data—connecting sources, aligning logic, and enabling consistent reporting across departments. The move to SAP Datasphere helped the organization turn its data into a reliable source of truth. More than just a technology upgrade, it was a decisive step in future-proofing its infrastructure and steering into an era defined by AI and automation.

Client Benefits

  • $160K–$180K Annual Cloud Cost Savings: Lean data models and reusable flows cut compute usage, saving $160K–$180K in annual cloud costs.
  • 120+ Custom Reports Rebuilt with Consistent Logic: Unified 120+ reports with consistent logic and definitions. Business teams can still use tools such as Excel, now powered by reliable data from SAP Datasphere.
  • 150+ Business Users Enabled for Self-Service: 150+ users now build reports independently using certified data—accelerating decisions without relying on IT for every change.
  • 70% Faster Onboarding of Non-SAP Sources: Trigent reused the existing ETL logic to onboard non-sap sources 70% faster. This eliminated the need to build pipelines from scratch.
  • Smooth migration with no business disruption:  Phased rollout ensured platform stability and uninterrupted operations. Each report was cross-verified with the legacy system to maintain accuracy and consistency.

Technology Stack