From fragmented data and disconnected processes to a scalable master data management foundation

Challenge

A large food manufacturer managed critical master data across SAP, local files, emails, and department-specific sources, making it difficult to maintain consistent and reliable information across teams and systems.

Master data management relied heavily on manual reconciliation and field-by-field SAP entry, increasing workload and the risk of errors. Unclear ownership and limited workflow traceability further complicated data governance and made it difficult to track information and requests.

The process was also completely sequential, meaning that delays in one activity could block subsequent steps and extend the overall process lead time. This created a clear need to reduce dependencies between activities and enable greater parallelization.

The company needed a centralized approach to govern, validate, monitor, and integrate master data across different business domains and systems.

Solution

LTPlabs designed and developed a centralized master data management ecosystem that provides a common framework for managing multiple data domains while integrating with existing enterprise systems.

Within six weeks, LTPlabs delivered a functional MVP built around three core capabilities:

• Standardized data governance: configurable data templates define the fields, validation requirements, ownership, permissions, precedences, criticality, and SLAs associated with each data type.
• Centralized workflows and data quality: dedicated business and back-office cockpits allow users to create, edit, validate, and monitor data, while a data management component applies validation rules and tracks data quality and completeness.
• Enterprise integration and automation: API-based workflows connect the platform with systems such as SAP and WMS, while a GenAI capability supports the creation of new validation rules.

The redesigned workflow also enables activities to progress in parallel, reducing sequential dependencies and minimizing overall process lead time.

The architecture was designed to be reusable across different master data domains.

As a concrete use case, the MVP explored the automated calculation of centesimal product composition. The process was fully digitized, eliminating the need to create product sheets and perform manual calculations, often generated with several errors. By combining material data, bills of materials, ingredient quantities, unit conversions, and process information, the platform can calculate the composition of finished products once the required relationships are configured, eliminating an entire manual process.

Results

In just six weeks, LTPlabs turned the company’s master data management vision into a functional MVP, demonstrating that multiple data domains could be governed through a single, scalable architecture.

The MVP established a reusable framework for ownership, validation, permissions, data quality, workflows, and system integration, allowing new data domains and use cases to be incorporated without building separate solutions for each one.

By validating this approach, the project created the foundation for moving from fragmented, manually coordinated processes to centralized and scalable master data governance.

The organization now has a clear path to expand trusted master data management across teams, processes, and enterprise systems while progressively reducing manual coordination.