Improving Data Governance and ERP Readiness through Item Master Data Management

Summary:

SourcingHaus was engaged to support the refinement and integration of item master data into a global ERP system for a leading international organization. Our role focused on improving data quality, standardizing classification, and ensuring the item master data structure aligned with organizational needs and ERP interoperability. The consultancy helped lay the foundation for better procurement visibility, reduced duplication, and long-term efficiencies in supply chain operations.

The Challenge:

As part of a broader transition to a new enterprise resource planning (ERP) system, the organization needed to ensure that its item master data — the foundation of procurement and inventory records — was complete, accurate, and fit-for-purpose. This required aligning existing records with form-fit-function principles, eliminating redundancy, integrating standards, and enabling future interoperability across systems. The complexity of the task was amplified by legacy data inconsistencies, ongoing field operations, and tight timelines leading up to system go-live.

What We Did:

• Deployed automation solutions, mapping thousands of item records and introducing NLP tools (Stanford Stanza) and Python scripts for improved item matching and classification.
• Developed clear process documentation aligned with the data governance manual and delivered technical recommendations to enhance catalogue searchability and accuracy.
• Continued data integration and cleanup to support ERP go-live, while delivering training and technical assistance to build internal capacity.
• Refined item creation and approval workflows, and advised on taxonomy consistency and naming conventions to improve system usability and reduce errors.

The Impact:

• Strengthened data quality and governance ahead of ERP implementation
• Supported smoother system integration by ensuring structured and standardized item records
• Increased team capacity to maintain data consistency over time
• Reduced manual workload through automation and improved matching tools
• Provided reusable SOPs and search strategies for future scaling and training