CASE STUDY Detail

Big Data & Analytics Platform Implementation for Enhanced Business Performance in Banking

Industry
Banking
Technologies
Vertica
capabilites
Data Foundation & Value Management

Business Impact

100% to 300% Improvement in Query Performance

Migration of 700+ TB Across 12,000+ Tables

USD 15+ Million ROI from Phase 1 Implementation

99.6% SLA Achieved with 24x7 Platform Support

Table of Contents

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.
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