Challenge
A major global contract manufacturer in the beauty and personal care industry, serving Fortune 500 brand owners, had grown through years of M&A into a fragmented data estate.
ERP and MES systems sat across regions with their own definitions, the same supplier appeared five different ways, and there was no single source of truth.
Each new acquisition added more cleanup work, and the broken foundation made it impossible to build AI and predictive analytics on top with confidence. The company needed Snowflake data governance that could unify the estate.
Solution
IWConnect led with governance, not technology. We built a Snowflake lakehouse with dbt where Data Entity Contracts (stored as configuration tables) define schema, quality rules, and business semantics for every entity.
Pipelines generate from those contracts. A Master Data Management engine inside the lakehouse uses multi-level matching to produce Golden Records for materials, suppliers, and customers.
A semantic layer standardizes KPI definitions across reporting and AI use cases. An M&A Staging Protocol turns every future acquisition into a structured onboarding.
Business Value
M&A integration now moves about 5x faster under the new methodology. 4 of those 20+ in-scope source systems are live under unified governance, with the rest sequenced.
Forecasting is moving from fragmented per-facility models to a unified, governed platform supporting clustering, segmentation, and driver-based forecasting.
Get the full case study to see the Data Entity Contract approach and the M&A Staging Protocol in detail.
