Analytics and Data Services

Transforming Industrial Finance with a Cloud-Native Foundation

Improved Decision Making with Cloud Analytics and AI ready Data Model

#Azure
#Cloud
#Analytics
#Modernization

Challenge

The Legacy Data Anchor

The financing arm of a global industrial equipment manufacturer was struggling with a fragmented and aging data landscape. Decision-makers lacked a “Single Source of Truth,” forcing them to rely on incomplete or outdated information for critical risk assessments and loan approvals.
Core Challenges:

Data Fragmentation: Critical information was siloed across manufacturing, sales, inventory, and financial operations.
The SAP BW Bottleneck: Heavy reliance on legacy SAP BW systems led to slow, inconsistent reporting and high maintenance costs.
Infrastructure Rigidity: On-premises systems were unable to scale with fluctuating workloads or support modern AI/ML initiatives.
Delayed Intelligence: Slow processing times meant that by the time data reached leadership, it was often too old to drive competitive decision-making.

Impact

Customer's targets to meet their business goals

By shifting away from heavy on-premises overhead, the organization achieved immediate cost efficiency and a durable foundation for long-term digital growth.

Credit Decisioning: 60% Reduction in loan approval turnaround time.

Residual Value Forecasting: 25% Greater Accuracy in predicting end-of-lease vehicle values.

Dealer Satisfaction: 40% Faster dealer payout processing through integrated data.

Delinquency Management: 15% Improvement in early detection of at-risk accounts via AI/ML. IT Overhead: 35% Savings by eliminating legacy hardware and licensing.

Solution

Azure-Native Data Transformation

Apolis designed and implemented a scalable, cloud-native data architecture to replace the legacy on-premises footprint.
Technical Architecture:

Modern Data Lakehouse: Leveraged Azure Synapse Analytics for enterprise warehousing and ADLS Gen2 for high-performance data lake storage.
Automated Ingestion: Built streamlined pipelines using Azure Data Factory and Logic Apps to orchestrate near real-time data movement from SAP and other legacy sources.
AI-Ready Engineering: Developed a harmonized data model using PySpark and Synapse Notebooks, ensuring data is clean, reusable, and optimized for machine learning.
Self-Service Business Intelligence: Deployed Power BI as the interactive reporting layer, allowing stakeholders to build their own dashboards without IT intervention.

Results

Delivering Impact, Driving Excellence

The transition from SAP BW to a cloud-native platform fundamentally changed how the client interacts with their data.

Decommissioned Legacy Systems: Successfully phased out the costly SAP BW environment, shifting entirely to an agile, self-service model.
Near Real-Time Visibility: Leadership now has access to interactive dashboards that reflect current operational realities rather than yesterday’s news.
Enhanced Risk Modeling: The harmonized dataset allows for more sophisticated risk assessments and more accurate demand forecasting.
Operational Agility: The platform can now scale on-demand to handle complex analytical workloads during peak financial cycles without upfront infrastructure investment.