RetailEnterprise Data Platform for a Global Retailer
Building a Snowflake enterprise data platform for a top-10 global retailer — consolidating 14 data sources across 40 markets, enabling real-time inventory analytics, and cutting reporting query times by 70%.
Client
Confidential — Top-10 Global Retailer
Expertise
Programme OutcomesResults at a glance
70%
Faster queries
Reduction in reporting time
14
Data sources
Consolidated into Snowflake
40
Markets live
With real-time analytics
22%
Forecast accuracy
MAPE reduction vs legacy
The ChallengeA fragmented data estate preventing real-time retail decisions
The client — a top-10 global retailer operating across 40 markets — was making inventory, pricing, and demand decisions on data that was 24–48 hours stale. Their reporting landscape comprised 14 different data sources: legacy data warehouses, ERP extracts, e-commerce platform feeds, third-party logistics data, and point-of-sale systems from 8 different regional technology stacks.
The business consequence was direct: stockouts that could have been prevented with real-time inventory data, promotional pricing decisions made on yesterday's sell-through, and demand forecasting that couldn't incorporate live signals from social and search. A major competitor had already deployed real-time analytics and was gaining market share.
BrezQ was engaged to design and build an enterprise data platform that would consolidate all 14 sources into a single Snowflake environment, enable real-time inventory analytics, and serve as the foundation for a new demand forecasting capability.
The BrezQ ApproachA Snowflake data platform on a data mesh architecture
BrezQ designed the data platform on data mesh principles — with each of the 14 source systems treated as a data domain, responsible for producing clean, validated data products to an agreed schema contract. This approach eliminated the 'central team does everything' bottleneck and enabled source system teams to participate in the data platform without being dependencies on the BrezQ central engineering team.
Real-time ingestion was built using a combination of Kafka (for the highest-frequency POS and inventory signals) and dbt on Snowflake (for structured transformation of batch feeds). A Snowflake Dynamic Tables architecture was used to make real-time data immediately queryable without the complexity of managing streaming compute infrastructure.
The demand forecasting layer was built as a separate ML platform on Vertex AI, consuming clean data products from the Snowflake platform and publishing forecast outputs back as Snowflake tables — queryable by any downstream business intelligence tool without additional data movement.
Technologies Deployed
The OutcomeReal-time analytics across 40 markets — 70% faster queries, measurable business impact
Reporting query times reduced by 70% across all business intelligence tools — driven by Snowflake's query engine combined with a materialised data product layer that pre-computed the joins and aggregations most commonly needed by merchandising, supply chain, and finance teams.
Inventory visibility moved from 24–48 hour latency to under 10 minutes across all 40 markets — enabling the client to pilot a real-time stockout alert programme in five test markets, reducing stockout incidents by 18% in the first quarter.
The demand forecasting model, deployed six months after platform go-live, reduced forecast error (MAPE) by 22% versus the legacy forecasting approach — translating to a reduction in excess inventory holding costs of approximately £14M in the first year.
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