E-Commerce Data Integration & Analytics
Online Retail Operation
Context & Objective
This project focused on creating a unified and reliable view of online retail operations. The objective was to design an automated data pipeline that integrates sales, inventory, and customer data into one trusted source, enabling real-time analytics and ensuring compliance with data protection standards.
Challenges Faced
Fragmented Data Landscape
Sales, customer, and inventory data were stored in separate spreadsheets, APIs, and legacy databases, resulting in inconsistency and poor integration.
Poor Data Hygiene
Duplicate orders, missing identifiers, and inconsistent time formats made it difficult to produce accurate metrics and reconcile data across systems.
Regulatory Risk
Customer records lacked proper anonymisation and retention policies, creating potential non-compliance with GDPR requirements.
Approach & Solution
Fabric Dataflows & Cleansing
Used Microsoft Fabric Dataflows to ingest and transform multi-source data. Standardised structures, fixed nulls, deduplicated records.
Secure Data Modelling
Built a Lakehouse schema with region-based access and hashed personal identifiers. Each transformation step logged for full auditability.
Optimised Star Schema
Created a central fact table linked to product, region, and customer dimensions. Developed 30+ reusable DAX measures.
Dynamic Reporting Layer
Four-page Power BI dashboard with drill-through analytics for sales performance, profitability, cohort trends, and product segmentation.
Results & Impact
- Created a single, governed dataset now used by multiple business units
- Implemented GDPR-aligned data policies including anonymisation and retention schedules
- Delivered consistent, accurate metrics improving commercial decision-making