Unified data governance has become a prerequisite for retailers scaling AI and advanced analytics to drive personalization, optimize supply chains, and improve operational efficiency. Yet many enterprises struggle with fragmented data platforms, siloed MLOps processes, and inconsistent controls that limit their ability to scale ML and GenAI initiatives. As regulatory scrutiny increases and agentic AI becomes more prevalent, retail data governance — built on platforms like Databricks Unity Catalog — must extend across the enterprise, covering data lineage, access controls, and interoperability. Without a shared Data and AI foundation, innovation slows, trust erodes, and the ability to turn data into real-time, intelligent decisions is significantly constrained.
Problem Statement
Sobeys Inc., one of Canada’s leading retailers under the Empire umbrella, is pursuing a tech-first strategy to deliver exceptional shopping and food experiences. As its Advanced Analytics team scaled ML across marketing, retail operations, and merchandising, the organization faced growing challenges from siloed MLOps practices and inconsistent data governance. Leadership recognized that future ML, GenAI, and agentic AI initiatives would require unified data governance, end-to-end lineage, and seamless cross-team interoperability. Without an enterprise AI governance foundation, scaling innovation risked fragmentation, compliance gaps, and slowed time to value. This prompted a proactive shift toward a governed, scalable Data and AI platform built on Databricks Unity Catalog.
Impact
- 70% faster dataset onboarding with unified, enterprise-wide governance
- Always audit-ready with end-to-end data lineage and controls
- ML & GenAI use cases activated faster, from idea to production
- Lower storage and compute costs by eliminating redundancy
- Trusted, transparent data across teams and platforms
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