Business Objective / Goal
To build a scalable, secure, and analytics-ready Big Data platform to support advanced business intelligence, customer analytics, AML, segmentation, and other use cases—while optimizing existing infrastructure and unlocking the full value of enterprise data.
Solutions & Implementation
- Designed and deployed a scalable Vertica and MapR Hadoop architecture for structured and unstructured data analysis.
- Migrated 12,000+ tables, 1,000+ stored procedures, and ~700 TB of data from legacy platforms.
- Built end-to-end analytics for use cases such as pre-approved loans, financial health scoring, customer segmentation, and conversion tracking.
- Implemented advanced capabilities including chatbots, text mining, AML detection, remittance anomaly tracking, and early-warning systems.
- Developed and maintained a flexible, customizable data science environment with 24x7 infrastructure support.
Major Technologies Used
- Vertica, MapR Hadoop, Sybase IQ, Oracle – Core databases and storage
- Spark, Kafka, Informatica, SAS – Data processing and ingestion
- Python, R, Java, SQL, Linux – Language stack for modeling and automation
- RapidMiner – For business-facing analytics workflows
- Text Mining/NLP, Social Intelligence tools – For document mining and behavior analysis
Business Outcomes
- 100% to 300% Improvement in Query Performance Achieved significant speedup in data processing and analytics execution.
- Migration of 700+ TB Across 12,000+ Tables Seamlessly migrated large-scale data infrastructure including workflows and procedures.
- USD 15+ Million ROI from Phase 1 Implementation Demonstrated measurable business value through scalable data science delivery.
- 99.6% SLA Achieved with 24x7 Platform Support Enabled high availability and operational reliability for business-critical analytics.
