Cloud modernization leads to reliable and automated data insights

Our client, a leading package service provider with 600+ operating facilities, was faced with several issues regarding data. They did not have a centralized data repository for business users, were manually forecasting model reports, and dealing with issues from working with multiple cloud providers, databases, and reporting tools. The absence of automated business insights, paired with unavailable source databases, caused data discrepancies with modifications. There were also inconsistencies across Azure layers, due to data silos having different source refresh times.

Ultimately, our client needed a modern data platform for FP&A functions to serve the needs of their Ground Financial Systems & Data (FS&D).

Data on autopilot: Making it clean, mean and lean

Ascendion played the role of a strategic partner. We provided deep data engineering expertise in data strategy and architecture, DataOps, and MLOps. By leveraging modern data strategies and technology expertise, we accelerated FS&D data product delivery and optimized costs. Our data solution derived reliable and efficient insights.

 
Additional details of the solution include:

• Migrated from GCP to Azure to reduce infrastructure costs
• Developed data pipelines, orchestrated DLT pipelines and Databricks notebooks through ADF, and used Terraform for storage account setup
• Built ADLS gen2 layer in Azure with Azure Data Factory as ETL tool and Data Products layer for consuming
• Incorporated sprint planning and epic management via Azure DevOps
• Established a user-friendly reporting data layer that enabled easy access to data across entities

The results:

• 100% reduction in manual intervention for data transformation
• 18-month rolling forecast produced monthly
• Enhanced ability to flex variable costs with demand in a timely manner
• Improved financial performance through proactive P&L management
• Early warning indicator spanning fiscal years