How a Regional Bank Built its Data Future
A major regional bank in the Southeast United States partnered with SunnyData to modernize their data infrastructure using Databricks on Azure. This transformation enabled the bank to meet stringent regulatory requirements, enhance operational efficiency, and prepare for future growth.
Key Metrics
INDUSTRY: Banking & Financial Services
SOLUTION: Self Service, Business Intelligence, ML Ops, Data Governance, including data lineage, documentation and inventory needs for internal and external audits.
PLATFORM USE CASE: Delta Lake, Data Science, Machine Learning, ETL, Unity Catalog, Profisee Integration
The banking landscape is undergoing a significant shift as AI and real-time personalization reshape customer expectations. Traditional banks face mounting regulatory pressures, intense scrutiny from investors, and fierce competition from fintech disruptors redefining what's possible in financial services. This new reality demands more than just incremental improvements to legacy systems, it requires a fundamental transformation in how banks manage, govern, and leverage their data - those who successfully modernize will thrive; those who don't risk falling behind.
The Problem: Growth Outpaces Infrastructure
For one major regional bank in the US, transformation became imperative as they approached $100B in assets under management. Years of growth through acquisitions had created a complex web of challenges that threatened their ability to compete effectively.
Their data architecture challenges included:
Fragmented customer data across disparate systems, preventing a unified customer view
Multiple data marts with different ETL tools creating silos and inconsistent reporting
Absence of a centralized data lake, leading to manual data requests and long SLAs
IT teams overwhelmed with service tickets, often taking weeks to fulfill requests
High operational costs from maintaining multiple tools and on-premise systems
These architectural issues cascaded into severe operational inefficiencies:
IT support teams drowning in service tickets from legacy systems, causing resource strain
Undocumented ad-hoc data requests from business units led to high SLAs (often weeks) frustrating users
Manual processes and outdated systems consistently missing internal SLAs, affecting business performance
Multiple ETL tools and expensive on-premise systems driving up operational costs significantly
Innovation was equally constrained. While ML and AI initiatives were emerging within business units, IT teams couldn't support these efforts effectively. The data analytics team struggled to keep pace with innovation needs, and data science capabilities remained underdeveloped.
The bank needed a comprehensive transformation of its data infrastructure to address these interconnected challenges and enable future growth. They began evaluating cloud analytics platforms, focusing on SQL-based solutions that would:
Support seamless migration from existing data marts
Enable future data science capabilities
Ensure compliance at scale
Minimize business disruption during transition
The Solution: Building a Modern Data Foundation
The solution leverages Databricks as a unified analytics platform to modernize the bank's data ecosystem. The architecture integrates a data lake with Delta Lake to provide seamless data ingestion, transformation, and analytics.
Key Platform Integrations with Databricks Lakehouse
Master Data Management with Profisee MDM
Provides single, accurate view of customer and household data
Ensures consistent, high-quality information across disparate sources
Enables better decision-making and risk management
Maintains data governance and compliance standards
The bank can confidently harness the power of its data while ensuring security, compliance, and operational excellence.
Sensitive Data Management with Informatica Data Catalog
Scan and classify sensitive data at the column level - PI and PII
Ensures secure and automated data ingestion
Integration Informatica Data Catalog with Unity Catalog on Databricks ( tagging of sensitive data imported into UC)
Protects sensitive data throughout the adoption process by applying masking and filtering through Unity Catalog.
Implementation Strategy
The migration followed a carefully planned and phased approach to ensure business continuity:
Planning & Pilot Migration: Analysis of existing pipelines and prioritization of high-impact workloads
Full Production Migration: Parallel testing and incremental migrations
Optimization & Scale-Out: Performance optimization and capability expansion
SunnyData's Role
SunnyData’s accelerators were used for access control management. The accelerator can achieve granular access control, streamlined deployment, scalability and flexibility, and enhanced governance (automated policy enforcement). Plus it provided a structured, scalable approach to role-based and attribute-based access management to tackle the biggest challenge during the implementation (ensuring seamless data governance and access control while maintaining compliance with financial regulations).
The success of this initiative was driven by a highly skilled, cross-functional delivery pod with deep expertise in Databricks, data security, and banking regulations. Their ability to navigate technical complexities, collaborate effectively, and adapt to evolving requirements ensured a smooth and secure transition to a modern data platform.
The Results: Impact Across the Enterprise
The Lakehouse Platform implementation has positioned the bank for the next 5-10 years with a modern, cloud-native, governed platform that meets regulatory requirements for banks exceeding $100B in assets. The transformation delivered significant improvements across multiple areas:
📊 Advanced Analytics and Self-Service Capabilities
SunnyData developed a repeatable pattern for data ingestion and transformation through the various layers (bronze, silver, gold) to provide a standard consumption layer for new use cases across business functions.
Prior to implementation, IT would intake numerous service requests and undocumented requests for ad-hoc data retrieval, which could take days or weeks. Post Lakehouse implementation:
Certified data assets were published across various domains
Service tickets for data pulls were reduced by 65% through Unity Catalog's search features, DB SQL and PowerBI
SLAs for data requests improved significantly
Onboarding of users and data sources became faster
✅ Enhanced Regulatory Compliance and Governance
The Lakehouse centralized most of the bank's data across treasury, deposit, credit, household, and customer history with audit, lineage, and access controls that met internal and external audit needs (OCC, SCRA).
Key improvements:
Enhanced audit efficiency through detailed, real-time insights into data usage
Reduced MRAs (Matters Requiring Attention)
Improved audit completion times
Automated governance through Unity Catalog
High quality, accurate, and reliable traceability and lineage of data and AI assets from source to raw, clean, and curated
🤖 Machine Learning and AI Transformation
Prior to the implementation, ML and AI efforts were siloed, with business teams spinning up their own ML initiatives that were not supported by Enterprise IT and were ungoverned.
Through the transformation the team:
Migrated to MLflow on Databricks and standardized ML Ops
Ensured ML and AI efforts around risk scores, customer data, and churn were documented, governed, secure, and met Ethical AI concerns
💰Operational Efficiencies and Cost Savings
The transformation delivered substantial operational improvements:
57% faster ETL processing to improve SLAs on data requests.
40% acceleration in change management through SunnyData accelerators
Consolidated three data warehouses into a single Lakehouse
Reduced tooling through migration from DataStage and SSIS to Databricks
Reduced licensing costs and hardware savings
Decreased operational costs from on-premise system maintenance
📈 Business Impact
Business lines across the organization were ecstatic with the transformation. Business application data that would take days or weeks to be updated in the analytics systems were fed in near-real time in the Databricks lakehouse. This resulted in:
Improved data processing times
Increased adoption of self-service analytics
Empowered business users to generate insights without IT dependency
Faster time to market for new financial products
The platform's unified approach now enables quick error diagnosis and solution implementation, positioning the bank for continued innovation and growth. With modernized infrastructure, streamlined operations, and enhanced governance, the bank stands ready to meet evolving customer needs and regulatory requirements while maintaining its competitive edge in the financial services landscape.
