Data Warehousing in Azure for
SIG to Enable Centralized Analytics and Governance

Client Overview

SIG is a UK-based international retailer operating across multiple European countries. With several independent business units functioning across regions, SIG generated vast volumes of structured, semi-structured, and unstructured data. For strategic reporting and decision-making, data from all business units needed to be consolidated at a group level. However, fragmented tools, manual processes, and inconsistent data standards created significant challenges in achieving reliable, timely insights.

Business Objective

SIG aimed to establish a centralized, scalable, and automated data warehousing solution on Azure. The goal was to eliminate manual data handling, improve data quality and availability, implement a strong data governance framework, and enable real-time analytics to support informed decision-making across the organization.

Industry

Retail and Distribution

Platform

Azure

Service

Cloud Data Warehousing

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Nick Owen

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Distributed Data Silos

Each business unit operated independently with its own data management tools and methodologies, resulting in inconsistent formats and fragmented data governance practices.

Multiple Data Formats

The organization managed structured data, semi-structured data such as XML files, and unstructured data, including images and audio files, increasing integration complexity.

Manual Data Extraction

Data consolidation and transformation processes were largely manual, leading to delays, quality issues, and increased operational risk.

Data Quality Concerns

Inconsistent data definitions and delayed availability of business-critical information impacted reporting accuracy and executive decision-making.

Looking to unify data across regions and business units with a scalable Azure data platform? NeosAlpha helps retailers design automated data lakes and warehouses that power real-time analytics and governance.

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Solutions

Comprehensive Data Discovery and Governance Design

NeosAlpha’s Azure specialists conducted workshops with SIG’s IT teams to understand existing data lineage, sources, and governance gaps. A collaborative framework was established to break down silos and standardize data terminology across business units.

Azure Data Lake Implementation

A fully automated Azure Data Lake initiative was implemented to store both reference and transactional data from multiple regions. Structured ingestion processes ensured consistency and scalability.

Automated Data Ingestion and Validation

Azure Data Factory pipelines were configured to ingest data periodically, apply quality checks, and watermark datasets to enable controlled transformation intervals. This ensured reliable and repeatable data processing.

Staging and Transformation Architecture

Data retrieved from Azure Data Lake was converted into staging tables within Azure SQL Server. Transformation and modeling processes were applied to structure data for warehouse consumption and agile reporting.

Centralized Data Warehouse and Reporting Enablement

Validated and transformed data were loaded into a centralized warehouse. Power BI dashboards were developed to provide accessible insights and executive-level reporting capabilities.

Error Handling and Monitoring Framework

Monitoring mechanisms were implemented to detect and resolve data pipeline errors proactively, ensuring reliability and traceability across the data lifecycle.