Driving Enterprise Data Transformation with ACI’s Azure Lakehouse

The client is among the largest debt management firms in the U.S. Its core mission is to empower customers by providing effective solutions for managing their debt burdens. It aims to offer comprehensive debt management services that cater to the diverse financial needs of individuals across these regions. Through its extensive market presence and commitment to innovation, the client continually strives to enhance customer outcomes and financial well-being, leveraging advanced technologies and strategic partnerships to achieve sustainable debt management solutions.

As the organization expanded its data ecosystem through growth and acquisitions, its existing infrastructure struggled to keep pace with increasing data volume, complexity, and analytics demands. This created operational bottlenecks that hindered timely insights, scalability, and cross-functional alignment.

Some of the challenges encountered by the client include:

Fragmented data sources created inconsistencies across business, reporting, and data science teams

Manual reporting and validation slowed decision-making and increased error risk

Legacy on-premises systems failed to handle growing data volume and complexity

Long query runtimes delayed analytics, models, and reporting for years

Post-acquisition integrations added complexity to already siloed data ecosystems

After conducting a thorough analysis of the client's environment and requirements, ACI proposed a scalable Data Lakehouse architecture that was designed and implemented using Azure Data Lake and Databricks Delta Lake. This solution was tailored to support complex data science products such as feature stores, sample selectors, and reporting datasets.

Key capabilities delivered:

End-to-end model lifecycle with Databricks, MLOps, DevOps, AKS deployment, and Synapse storage

Feature store enablement for scalable ML experimentation and reuse

Automated DataOps pipelines using Azure Databricks and Azure Automation

Enterprise data warehouse via Synapse powering QlikSense and Power BI dashboards

Built-in compliance framework for GDPR and PII data governance

With a modern, scalable data foundation in place, the client transformed its analytics and machine learning capabilities by unlocking measurable improvements in speed, efficiency, data reliability, and business responsiveness across the enterprise.

This way, the client could accrue substantial benefits such as:

75% faster model deployment - reduced release cycles from 8–12 months to 2–4 months

90% reduction in report development time for faster business insights

70% faster onboarding of new data sources after acquisitions

Up to 60% faster data processing with scalable, ACID-compliant Lakehouse architecture

80–90% fewer data quality issues through automated validation and governance

50% faster engineering cycles enabled by standardized DevOps and MLOps pipelines