A multi-location retailer had inventory, POS sales, and e-commerce data in three separate systems with no integration. Azure Data Factory and AI-assisted mapping unified them into a single analytics-ready warehouse.
Inventory data lived in a legacy ERP, sales data in a POS system, and online orders in a separate e-commerce platform. Product SKUs were inconsistent across systems, making any cross-channel analysis impossible without hours of manual reconciliation each week. Leadership had no unified view of stock levels, sell-through rates, or reorder needs.
Used Azure Purview to automatically profile the ERP, POS, and e-commerce databases — identifying data quality issues, null rates, and SKU format inconsistencies before building any pipelines.
Developed Azure Data Factory pipelines to extract from all three sources nightly, with Copilot for Data Factory generating the initial pipeline templates from natural language descriptions.
Used Power Query's AI-powered fuzzy matching to reconcile product SKU variations across systems (e.g., 'SKU-1234', 'SKU1234', '1234') into a single canonical product identifier.
Transformed and loaded unified inventory, sales, and order data into a star schema SQL Server data warehouse, with full audit logging and row-count reconciliation checks.
Built Power BI dashboards on top of the warehouse providing cross-channel inventory visibility, sell-through rates, and reorder alerts — updated nightly automatically.
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