Azure-Powered Financial Transformation

Situation

The Analytics team of a leading food producer relied on financial datasets stored across multiple SharePoint sites for balance sheet reconciliation and reporting. However, decentralized storage created fragmented access, frequent delays, and inconsistencies that slowed analysis and impacted trust in reporting.

Problem

SharePoint’s distributed setup required complex access permissions for each site, creating long wait times to retrieve essential data. Analysts struggled with delays in accessing accurate and consistent datasets which reduced operational efficiency and hampered timely insights for decision-making. A scalable, centralized solution was needed to eliminate bottlenecks and risks of data inconsistency.

Solution

Mu Sigma implemented an automated data ingestion framework leveraging Azure Data Factory (ADF) for orchestration and Azure MySQL Database as a metadata and execution backbone. The framework migrated fragmented SharePoint files into Databricks Delta tables, creating a single source of truth for reconciliation and analytics.

Key Components:

• Data Integration: Azure Data Factory pipelines automated ingestion from multiple SharePoint sites, handling unstructured/semi-structured formats.
• Metadata-Driven Execution: MySQL database managed ingestion parameters, job tracking, and source configurations-enabling faster onboarding of new datasets.
• Databricks Delta Tables: Optimized storage for Atomicity, Consistency, Isolation, and Durability (ACID) transactions, scalability, and efficient queries.
• Scalable Architecture: Supports multiple SharePoint sources and can expand without redesign.
• Automation & Monitoring: ADF pipeline monitoring combined with MySQL-based logging allowed proactive error handling, faster troubleshooting, and traceability.

Impact

• 50% reduction in manual effort for ingestion by automating extraction, transformation, and loading from SharePoint.
• 6–8 hours reduced to <1 hour for average issue resolution through real-time monitoring and proactive troubleshooting.
• Faster time-to-insight: Centralized Delta tables in Databricks provided analytics-ready data for reconciliation, Power BI reporting, and ML models.
• Improved scalability: Framework supports rapid onboarding of new data sources without redesign.
• Greater reliability: Centralized metadata and execution control ensured data consistency and trust in financial reporting.