A consumer packaged goods company had five business units independently engineering the same customer and demand-forecasting features for their own machine learning models, with no shared definitions and frequently conflicting results.
Creating a Governed Feature Store Across Five Business Units
Service Provided
Capabilities Utilized
Feature Engineering, Data Governance, MLOps
Client challenge
Two business units had each built a “customer lifetime value” feature using different underlying logic, and neither matched finance’s own definition used for reporting — a discrepancy that surfaced publicly in a leadership meeting and triggered a broader review of how features were being built and reused across the company.
Our solution
We built a centralized feature store with versioned, documented feature definitions that every business unit’s models could pull from, alongside a lightweight governance process for proposing and approving new shared features. Existing team-specific features were audited and either mapped onto the new shared definitions or explicitly documented as deliberately different, closing the ambiguity.
We prioritized making the feature store easier to use than building features from scratch, since adoption depended entirely on data scientists choosing it voluntarily rather than being mandated to use it.
Results
Within six months, four of the five business units had migrated their highest-use features onto the shared store, cutting duplicate feature engineering effort by an estimated 60% across those teams. The customer lifetime value discrepancy that triggered the project was resolved with a single shared definition now used consistently in both modeling and financial reporting.
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