Regression-Assured Lakehouse Data Products for Multi-Source Supply Chain Transformation

Abstract

A supply chain transformation program rarely converts one system at a time and calls it done. More often, dozens of enterprise resource planning instances, legacy applications, and regional operational platforms feed a single analytics environment, and the reliability of that conversion is treated as an implementation detail rather than an architectural discipline. This article proposes a different framing: lakehouse data product governance, in which curated supply chain objects material, inventory, delivery, order, supplier, and bill-of-materials records are built and maintained as reusable, auditable products, not one-time table builds. The framework layers a raw-curated-aggregated lakehouse pattern with a regression-assurance discipline that keeps validation of actively converting source systems separate from validation of source systems that must stay untouched. Operational observability lineage, job duration, cost, and failure tracking are treated here as defining properties of a trustworthy data product, not a monitoring convenience bolted on afterward. Drawing on architectural patterns observed across enterprise supply chain modernization efforts, the article argues that regression-assured release governance, more than curated storage layers on their own, determines whether a lakehouse data product program holds up as source systems keep changing underneath it. The discussion connects this framework to the existing data mesh and data product literature and makes the case that release-time regression assurance belongs inside the data product's own definition, not alongside it as a separate quality-assurance step.