Challenge
A global consumer goods company faced a classic data dilemma: information was everywhere, but usable insight was nowhere. Regional teams operated in silos, each relying on fragmented systems—ERPs, CRMs, marketing platforms, and logistics tools—leading to inconsistent reporting, mistrust in data, and slow, disconnected decision-making.
Solution
To rebuild trust and visibility, we implemented a unified Lakehouse Platform using Databricks, layered with modular data transformations powered by dbt. This modern architecture enabled scalable, reusable data models and clear lineage through every processing step. We didn’t just clean data—we aligned it with business goals, from marketing performance to ESG metrics. Critical dashboards were deployed incrementally, allowing each team to plug into a centralized, trusted source of truth.
Business Value
The transformation was profound. Reports that once took days now update in near real-time. Teams across marketing, supply chain, finance, and sustainability speak the same data language. Business users gained self-service capabilities, data engineers saw reduced rework, and leadership made faster, more informed decisions. Most importantly, the platform created a foundation for advanced analytics and future AI initiatives.
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