Big Data & Analytics Modernization

Objective: Enhance customer data quality and unlock deeper insights across a major Latin American banking institution with over 3,500 branches and 18 million clients. The goal was to support an improved omnichannel experience while boosting cross-sell and up-sell effectiveness through a modern data infrastructure.

Key Goals:

• Consolidate fragmented customer data across 10+ banking products.
• Eliminate data redundancies and improve data quality.
• Establish a scalable analytics governance model.
• Enable next-best product recommendations and actionable insights.
• Support regional expansion with a future-ready data platform.

Challenges:

• Data Silos: Customer data was dispersed across more than 20 systems, limiting visibility and trust.
• Quality Issues: Redundant, outdated, and incomplete data impeded campaign effectiveness.
• Scalability Constraints: Existing infrastructure couldn’t support advanced analytics at scale.
• Limited Insight Generation: Lack of automated models slowed the development of targeted offers.
• Governance Gaps: There was no centralized framework to manage data ownership, quality, or usage.

Solutions:

• Data Lake Implementation: Deployed a centralized Big Data architecture that aggregates and cleanses data from 20+ sources.
• Automated Data Cleansing: Applied rule-based deduplication and validation to ensure consistency and reliability.
• Insight Generation Models: Designed and deployed two machine learning-driven business insights and a next-best product recommendation engine.
• Analytics Governance Framework: Established enterprise-level standards and roles to manage data across teams and countries.

Technology Used:

Big Data Lake Architecture, Data Integration Pipelines, Machine Learning Models, Data Quality Engines, Analytics Governance Framework, Cloud Storage & Compute Platforms

Results:

• Consolidated 20+ legacy systems into a single customer intelligence hub.
• Enabled omnichannel strategies powered by accurate, real-time customer data.
• Improved customer segmentation and targeting capabilities.
• Launched cross-sell and up-sell campaigns based on predictive insights.
• Scalable platform ready to support new product lines and additional geographies.

Conclusion: This Big Data and Analytics modernization effort transformed the bank’s approach to customer intelligence. By establishing a unified, governed, and insights-ready data platform, the client is now positioned to scale predictive analytics across products and regions—enhancing customer engagement and business growth.