Driving Enterprise Data Transformation with ACI’s Azure Lakehouse
Learn how ACI Infotech built an Azure Lakehouse with Databricks and Synapse to accelerate analytics, improve data governance, and enable faster AI model deployment.
Key Results
The Challenge
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
Our Solution
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
Technologies Used
Results & Impact
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
"Working with ACI Infotech helped us streamline our data environment and significantly reduce the time taken to deploy models and generate insights. Their team understood both the technical and operational challenges and delivered a solution that scaled with our needs."
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