Financial Data Transformation & Reporting Excellence

Industry

Global Manufacturing and Distribution

Size

Fortune 500 company with operations in 25+ countries

Annual Revenue

$5B+

Employee

15,000+

Operational Scope

Multiple subsidiaries across APAC, EMEA, and Americas

Empowering Data-Driven Decision Making with Microsoft Fabric

Executive Summary

In an era where data-driven decision making is crucial for business success, a multinational corporation partnered with Xylity to revolutionize their financial reporting system. This case study details how we transformed their fragmented data landscape into a streamlined, automated reporting ecosystem using Microsoft Fabric and advanced data architecture principles.

Client Profile

Industry: Global Manufacturing and Distribution
Size: Fortune 500 company with operations in 25+ countries
Annual Revenue: $5B+
Employees: 15,000+
Operational Scope: Multiple subsidiaries across APAC, EMEA, and Americas

Business Challenges

The client’s financial reporting ecosystem faced several critical challenges that impacted their operational efficiency:

Data Management Issues:

Managing disparate data sources across 25+ country operations
Dealing with multiple data formats including Access databases, Excel spreadsheets, and legacy system exports
Manual data consolidation requiring 40+ hours per quarter
Inconsistent data naming conventions and formats across regions

Process Inefficiencies:

Quarterly reporting cycle taking 15-20 business days
5+ FTEs dedicated to manual data processing and validation
High risk of human error in data consolidation
Limited ability to perform ad-hoc analysis
Delayed decision-making due to report generation time

Compliance and Control:

Difficulty maintaining audit trails
Inconsistent data quality checks
Limited version control capabilities
Risk of regulatory non-compliance
Challenges in maintaining data security standards

Solution Overview

Strategic Approach

Xylity implemented a comprehensive data transformation solution following a phased approach:

Phase 1: Assessment and Design (4 weeks)

Conducted thorough analysis of existing data sources and formats
Mapped data flow requirements and transformation rules
Designed Medallion Architecture implementation plan
Established key performance metrics and success criteria

Phase 2: Technical Implementation (12 weeks)

Deployed Microsoft Fabric environment
Implemented three-layer data architecture
Developed automated data pipelines
Created Power BI dashboards and reports

Phase 3: Testing and Optimization (6 weeks)

Conducted parallel runs with existing system
Performed user acceptance testing
Optimized performance and refined processes
Documented procedures and controls

Technical Architecture

Bronze Layer (Data Ingestion):

Implemented Dataflow Gen2 for raw data ingestion
Created connectors for multiple data sources
Established data lake storage in OneLake
Set up initial data validation checks

Silver Layer (Data Processing):

Developed data transformation pipelines using PySpark
Implemented data quality rules and checks
Created standardized data models
Established data lineage tracking

Gold Layer (Business Intelligence):

Built specialized data marts for different business units
Created automated reporting templates
Implemented real-time dashboard updates
Developed self-service analytics capabilities

Technology Stack Details

Microsoft Fabric Components:

OneLake for centralized data storage
Dataflow Gen2 for data ingestion
Data Factory for orchestration
Power BI Premium for reporting

Additional Technologies:

Azure SQL for structured data storage
PySpark for complex transformations
Power Apps for user interfaces
To perform complex transformation used data bricks notebook
Custom APIs for system integration

Implementation Highlights

Data Integration:

Connected 30+ data sources across global operations
Standardized data formats and naming conventions
Implemented automated data quality checks
Created comprehensive data dictionary

Process Automation:

Developed automated data refresh schedules
Created notification system for data updates
Implemented exception handling procedures
Built audit logging system

Reporting Capabilities:

Created dynamic dashboards for different user roles
Implemented drill-down capabilities
Developed automated quarterly report generation
Built ad-hoc analysis tools

Business Impact

Quantitative Results:

Reduced report generation time from 15-20 days to 2 days (75% reduction)
Improved data accuracy from 95% to 99.9%
Decreased manual effort by 85%
Achieved 100% audit compliance
Reduced operational costs by 40%

Qualitative Improvements:

Enhanced decision-making capabilities through real-time data access
Improved stakeholder confidence in financial reporting
Increased team productivity and job satisfaction
Better regulatory compliance and audit readiness
Enhanced data security and governance

Future Roadmap

The success of this implementation has led to planned expansions:

Integration with additional data sources
Advanced analytics capabilities
Machine learning-based forecasting
Extended mobile reporting capabilities
Enhanced collaboration features

Lessons Learned

Early stakeholder engagement is crucial for success
Phased implementation reduces risk and improves adoption
Comprehensive testing is essential for accuracy
Regular feedback loops improve final outcomes
Documentation is vital for long-term success