Data Models: Key to Personalization in Modern Banking

In the dynamic banking sector, customer experience is the main differentiator. Customers expect offers and services tailored to their individual financial reality. Achieving this hyper-personalization is impossible when customer information is fragmented across internal systems, creating a partial and inaccurate view.

The Challenge: Scattered Information Preventing Personalization

A bank with a nationwide presence faced the challenge of scattered information. Customer data such as transactions, balances, credit history, and call center interactions resided in multiple independent operational applications. This lack of integration made several things difficult:

• Traceability: It was almost impossible to track the customer journey across different products and services.
• Unified View: Executives could not obtain a 360° view of each customer, limiting cross-selling opportunities and product design.
• Operational Risk: Data fragmentation increased the risk of inconsistencies and reporting errors, affecting efficiency.

The bank needed a structure that would act as a central axis for all information.

The Infomedia Solution: Enterprise Data Modeling and the Unified Customer Database

Infomedia addressed the challenge through the design and implementation of an advanced Enterprise Data Model, whose main result was the creation of a Unified Customer Database.

The model defined not only how data would be stored, but also how it would be logically related throughout the bank's ecosystem. Key actions included:

• Design of an Integrating Data Model: A central schema capable of consolidating information from all operational applications such as core banking, CRM, and trading while preserving traceability to the original source.
• Creation of an Enterprise Data Dictionary: Definitions and structures for critical data such as "income," "balance," and "active customer" were standardized to ensure consistency throughout the bank's systems.
• Implementation of a Traceability System: Mechanisms were established to identify the origin and quality of each data element at any time, which is essential for regulatory compliance.

Results: A Bank with Higher Customer Satisfaction Ratings

The implementation of the data model and unified customer database produced an accurate and reliable 360° view of each customer. Results included:

• Improved Customer Satisfaction: The bank was able to design more personalized and strategic services, positioning it as one of the highest-rated organizations in its sector.
• Operational Efficiency: Reporting accuracy improved significantly, reducing time spent reconciling data.
• Strategic Decision-Making: Marketing and product teams were able to identify cross-selling opportunities and design offers with much greater precision.

This case demonstrates that banking data integration through robust enterprise data modeling is at the core of any digital transformation project, laying the foundation for innovation and customer experience.