The Data Lake Trap: Why Your Master Data Strategy Is Still Siloed

Home » Blogs » Think Tank » The Data Lake Trap: Why Your Master Data Strategy Is Still Siloed Think Tank RSS The Data Lake Trap: Why Your Master Data Strategy Is Still Siloed October 27, 2026 Tom Preston, SCB Contributor You spent $15 million on a modern, cloud-native data lake. It ingests data from every major system. Your chief technology officer declared victory. Then nothing changed.

Three years in, and you’re still answering the same questions the old way: Which is the current spec? Why does procurement have different supplier data than manufacturing? Why does the "unified" view of data still require manual reconciliation with three other systems? You now maintain legacy systems; a data lake; custom extract, transport and load (ETL) pipelines, and the governance overhead of all three. You paid for a solution to operational data fragmentation and got a reporting system that sits on top of that fragmentation, unchanged.

This is a design failure, because data lakes treat fragmentation as an analytics problem when it’s fundamentally an operational problem.

Operational systems, such as enterprise resource planning, product lifecycle management, procurement platform and manufacturing execution system, need connected data at the point of use, in real time. A spec change in R&D needs to reach manufacturing instantly, not after a nightly batch sync. A supplier certification update needs to be visible to both procurement and quality the moment it arrives, not hours later when the lake is refreshed.

This story was originally published by SupplyChainBrain. Read the full story at SupplyChainBrain →