A global manufacturing company operating across multiple markets discovered a problem that many enterprises face but few address systematically. Their product information, supplier data, and customer records existed in scattered systems that rarely agreed with each other. When the sales team looked up a product specification, they might see different information than what the supply chain team had, which differed again from what appeared in the e-commerce catalog. This fragmentation created more than inconvenience. It led to inventory discrepancies that tied up capital, delayed decisions that required cross-functional data, and created compliance risks when regulatory audits revealed inconsistencies.
The company needed what seemed simple on the surface but proves remarkably complex in practice: a single source of truth for enterprise data. Not a data warehouse that collected copies of information, but an active system that managed the master records and ensured consistency across every application that touched that data.
Architecture for Truth and Flexibility
We designed the master data management platform on Azure with a clear principle: the MDM system would own the authoritative version of core business entities like products, customers, suppliers, and locations. Every other system would either read from this master or write changes back to it through well-defined APIs. This seemingly simple concept required sophisticated architecture because enterprise systems have different update frequencies, different data models, and different reliability requirements.
The platform connected upstream systems where data originated—like procurement systems creating new supplier records or product development teams defining new items—with downstream systems that consumed this data for operations. An e-commerce platform needed product information formatted one way, while the warehouse management system needed different attributes entirely. The MDM platform handled these transformations while ensuring that regardless of how data appeared in different systems, the underlying facts remained consistent.
We embedded data governance directly into the architecture rather than treating it as a separate concern. This meant building workflows for data stewardship, implementing quality rules that validated data at entry, and creating approval processes for changes to master records. When someone tried to create a new product record with incomplete information, the system prevented it rather than allowing bad data to propagate across the enterprise.
The Distributed Services Challenge
The company operated through a distributed services architecture, with different business units running semi-autonomous systems that needed to share data without creating tight coupling between services. This architectural pattern offers significant advantages for organizational agility and system resilience, but it makes master data management more complex.
We designed the integration layer to support both synchronous and asynchronous patterns. Some systems needed immediate confirmation when they read or wrote data, while others could work with eventual consistency. Product catalog updates could happen in near real-time, while nightly batch processes handled less time-sensitive synchronization. The APIs we built provided consistent interfaces while allowing flexibility in how different systems integrated based on their technical constraints and business requirements.
The platform also had to scale horizontally. As the business grew and added new markets, products, or suppliers, the MDM system needed to handle increased load without requiring architectural changes. We achieved this through careful database design, caching strategies, and load balancing that distributed requests across multiple instances of the core services.
Beyond Technical Implementation
The most challenging aspect wasn't technical architecture but organizational change. Different business units had historically maintained their own versions of truth, and moving to a single master required them to agree on definitions, ownership, and processes. We worked with stakeholders across the organization to establish data governance committees, define data ownership models, and create policies for how master data would be managed going forward.
This governance framework meant the technical platform had organizational backing. When conflicts arose about which system should be the source of truth for specific attributes, the governance structure provided a way to resolve them. When data quality issues appeared, clear ownership meant someone was accountable for fixing them.
The result was more than a technical platform. The company gained unified visibility across all operations, eliminated the data conflicts that had caused operational problems, and established a foundation that made future digital initiatives significantly easier. When they wanted to implement new analytics, launch new customer experiences, or integrate acquired companies, they had confidence that the underlying data would be consistent and reliable. Master data management had moved from a technical problem to a strategic asset.