In today’s global supply chains, where delays in procurement, stockouts, and compliance gaps can disrupt entire operations, the problem is often less about logistics and more about data. At the center of this issue is master data—the core set of information that underpins every supply chain transaction—and how it is governed.
Sandeep Ramanamuni, a long-time enterprise data management professional with deep experience in manufacturing and regulated industries, has spent years working with global organizations to untangle these hidden complexities. He argues that Master Data Governance (MDG) is not simply an IT concern, but a strategic capability.
“You can’t automate or optimize what you can’t trust,” Ramanamuni said in a recent interview. “Master data governance ensures that the foundational data—the very DNA of operations—is synchronized, accurate, and aligned across business units.”
Reportedly, master data inconsistencies are more common than most organizations realize. In one engagement with a global consumer goods manufacturer, Ramanamuni and his team discovered that over 18% of materials in the ERP system were duplicates. As per the reports, these duplications led to excess inventory, supplier confusion, and distorted planning cycles.
“Those numbers aren’t unusual,” he noted. “They point to a broader issue—organizations are making decisions on flawed information.”
Master data—ranging from material numbers and vendor codes to product specifications—is used across various platforms including ERP, transportation, and manufacturing systems. When this data is siloed or inconsistently maintained, even minor discrepancies can cascade through the supply chain. “You might be sourcing the same item under three different codes, from three different suppliers, all at different costs,” Ramanamuni explained.
A large part of Ramanamuni’s recent work has centered around implementing SAP Master Data Governance (SAP MDG) in complex enterprise environments. Coming from the expert’s table, he described SAP MDG as a system that provides a single point of truth for core data and enforces consistency through rule-based workflows.
In a recent project for a global pharmaceutical CDMO (Contract Development and Manufacturing Organization), Ramanamuni led an SAP MDG rollout across 12 manufacturing sites and five ERP instances. Prior to implementation, each site maintained its own data sets, resulting in inconsistent material classifications and naming conventions.
“After standardizing the material creation process through SAP MDG, we were able to embed validation rules directly into workflows,” he said. “It led to a 30% reduction in order processing errors and streamlined sourcing decisions.”
Furthermore, in highly regulated sectors like pharmaceuticals and aerospace, traceability is non-negotiable. Ramanamuni shared an example from a biopharma engagement where SAP MDG was used to link product master data directly with quality and regulatory systems.
“We designed rule-based validations to ensure product definitions met region-specific compliance before they ever reached production,” he said. The approach not only reduced compliance exceptions but also improved audit readiness across the board.
In another case, Ramanamuni helped an automotive Tier 1 supplier integrate SAP MDG with supply chain planning tools to synchronize material master data with production schedules in real time. “Without that level of integration, just-in-time manufacturing becomes very difficult,” he said. “Data lag leads to missed builds and inventory mismatches.”
Additionally, Ramanamuni designed a master data framework to eliminate the disconnect between newly approved materials and actual production workflows, which in turn helped reduce lead times and improve operational agility.
With rising data volumes and complexity, Ramanamuni sees potential in machine learning-led data governance. In a small pilot with an industrial equipment manufacturer, his team developed a model to auto-classify materials based on historical descriptions, cutting down the need for manual categorization.
“This is just the beginning,” he said. “We’re moving toward cognitive master data governance, where systems not only enforce data rules but also learn and adapt dynamically based on patterns.”
But Ramanamuni is quick to caution that technology alone is not the answer. “You need the right alignment between people, processes, and platforms,” he emphasized. He has led numerous master data maturity workshops and governance model assessments, helping organizations move from reactive data management to proactive stewardship.
As companies place greater emphasis on resilience and visibility in their supply chains, Master Data Governance is moving out of the IT back office and into the executive boardroom. Furthermore, experts like Ramanamuni are showing that reliable data is no longer a nice-to-have—it is a prerequisite for competitiveness.
“Whether it’s reducing order errors, improving compliance, or simply being able to trust what your systems are telling you—MDG makes it possible,” he said. “Without it, everything else in the supply chain is built on sand.”