Post-Merger Data Transformation

Situation

Telecom mergers often bring complex data challenges with legacy data governance and outdated reporting frameworks. A leading provider undergoing a major merger faced  data fragmentation, governance inconsistencies, and reporting misalignments, disrupting operational efficiency and slowing business agility.

Challenge

The merger brought significant hurdles in aligning operations and data management:

• • Conflicting Hierarchies & Business Rules: Different reporting structures and data governance policies made integration complex.
  • Mismatched Systems & KPI Calculations: Legacy systems used different methodologies for key metrics, leading to inconsistencies in business performance tracking.
  • Data Silos Delaying Decisions: Disparate data sources and unstructured ingestion processes slowed down insights and operational agility.
  • Inconsistent Governance: Varying access controls and role-based permissions led to security risks and inefficiencies in data handling.

Solution

Mu Sigma deployed a structured data framework that seamlessly integrated data through automated pipelines and drove actionable insights through intuitive reporting.

1. Lakehouse Implementation: Designed schema and ingested data from diverse sources into a unified Delta Lake environment.
2. Automated Extract, Transform, and Load (ETL) Pipelines: Developed reusable pipelines to automate data migration and used Azure Data Factory to ingest diverse data sources into a centralized warehouse.
3. Data Harmonization: Integrated data from multiple legacy systems, creating unified dimension tables.
4. Security & Access Control: Implemented row-level security and role-based access in the tabular model to manage hierarchical data mapping.
5. Performance Optimization: Enhanced Azure Analysis Server (ASS) memory efficiency and reduced dashboard refresh times.
6. Power BI Modernization: Revamped legacy reports to reflect real-time data with UX enhancements for faster insights.
7. Sales Performance Tabular Model: Built an analytics and semantic layer for self-service reporting.

Impact

Our automated ETL workflows and self-service reporting strengthened their data foundation, leading to:

• Optimized Costs & Performance:
  • 75% reduction in AAS server memory usage (~90GB saved)
  • 33% faster tabular model refresh times (~20 minutes saved per cycle)
• Enhanced Operational Efficiency:
  • Automated data imports, cutting 6+ hours of manual effort per month
  • Optimized Azure Data Factory schedules, saving 2+ hours daily and ensuring real-time data availability