Case Study

Data liquidity: How a national financial services institution transformed static information silos into flowing, actionable customer intelligence

The short version

Challenge:

A national financial services institution had fragmented customer data across silos following major acquisition hindering regulatory compliance and cross-sell opportunities.

Solution:

Enterprise data strategy with governance framework, Customer 360 master data management roadmap, and data quality monitoring using DataCatalyst’s Accelerate methodology.

Results:

Reduced regulatory reporting time from weeks to days while enabling personalized banking and improved cross-sell analytics.

The challenge: Post-acquisition data fragmentation threatens growth and compliance

A national financial services institution with over 1,300 branches faced critical data challenges following a major regional acquisition. The bank’s diversified portfolio, spanning retail banking, commercial lending, and wealth management, operated with completely fragmented data environments.

Siloed data warehouses and reporting platforms by line of business prevented enterprise-wide visibility. The recent acquisition exacerbated existing problems, creating inconsistent customer data that made it impossible to link retail, commercial, and wealth relationships under a single customer view. Meanwhile, regulatory pressure intensified expectations around data quality, lineage, and reporting transparency.

Digital transformation initiatives had added complexity as mobile banking adoption generated new streams of unstructured and semi-structured customer interaction data. Business units demanded more accurate, trusted data to power AI-driven credit risk models, personalization engines, and product cross-sell initiatives.

The need: Strategic data transformation to unlock competitive advantage

The institution recognized that fragmented data was constraining both regulatory compliance and business growth. Leadership required an enterprise-wide data strategy that could deliver a unified customer view while supporting advanced analytics capabilities.

The bank needed to establish data governance practices that would satisfy regulatory requirements while enabling business users with self-service analytics. Additionally, the data platform strategy had to align seamlessly with ongoing cloud migration initiatives to maximize technology investments.

The approach: Accelerate methodology drives comprehensive transformation

DataCatalyst deployed a lean team of seasoned experts using the proprietary Accelerate methodology to deliver rapid time-to-value. The business-driven approach ensured requirements aligned with strategic objectives rather than technology constraints.

The engagement spanned six critical data domains:

  • Customer
  • Account
  • Product
  • Party/Household
  • Transaction
  • Financial Advisor

Discovery and assessment involved comprehensive interviews, system inventories, and data profiling across all business lines to understand current-state challenges.

Target-state architecture design created a scalable, cloud-aligned model integrating core banking systems, CRM, and digital channels into a unified data platform. The governance model established a bank-wide data governance council with dedicated line-of-business data stewards.

The solution: Integrated strategy connecting business priorities with execution

DataCatalyst delivered a comprehensive enterprise data strategy blueprint encompassing current-state assessment, target-state architecture, and a detailed implementation roadmap. The solution included a defined data vision and principles treating data as a strategic asset.

  • Customer 360 master data management roadmap with actionable steps toward creating a unified customer profile by integrating data from all touchpoints – retail banking, commercial lending, wealth management, and digital channels – into a single, comprehensive view that enables personalized service and cross-selling opportunities
  • Data governance operating model featuring councils, stewardship roles, and escalation processes
  • Data quality framework identifying critical data elements for regulatory and financial reporting with automated monitoring plans
  • 3-year phased roadmap balancing regulatory compliance requirements with business-driven analytics use cases

The architecture integrated disparate systems while supporting ongoing core banking modernization and cloud migration programs.

The transformation: From siloed data to strategic asset

The institution shifted from fragmented data ownership to shared governance accountability across business units. Technology strategy aligned with business priorities, connecting cross-sell opportunities, risk modeling, and personalization initiatives with IT execution capabilities.

Leadership adopted the comprehensive data strategy with CIO, CDO, and the executive team commitment, ensuring enterprise-wide implementation. The governance framework operationalized council structures, defined stewardship roles, and established clear escalation processes for data-related decisions.

The results: Measurable improvements in compliance and business performance

  • Regulatory efficiency: Reduced time to deliver regulatory and management reports from weeks to days through improved data lineage and quality
  • Revenue opportunities: Enhanced cross-sell capabilities through Customer 360 master data management enabling a comprehensive understanding of customer relationships across all business lines
  • Risk reduction: Proactive data quality management significantly reduced regulatory remediation costs
  • Customer experience: Enabled personalized digital banking services through consolidated customer profiles that capture preferences, behaviors, and needs across all touchpoints

Pilot data quality dashboards successfully launched monitoring of regulatory-critical data, reducing reporting discrepancies. The governance framework became fully operational with established councils and stewardship processes.

The future: Foundation for advanced analytics and competitive differentiation

The enterprise data strategy provides the foundation for advanced AI-driven capabilities including enhanced credit risk modeling, sophisticated personalization engines, and predictive customer analytics. The unified data platform positions the institution to rapidly deploy new digital banking services while maintaining regulatory excellence.

The Customer 360 master data management capability will enable deeper customer relationship insights by consolidating data from every interaction, transaction, and service touchpoint into unified profiles. This comprehensive view supports both retention strategies and targeted product development, allowing the bank to understand complete customer journeys and deliver truly personalized financial services.

The established governance framework ensures sustainable data quality as the business continues to evolve and expand.