How Can Data Virtualization and Data Profiling Streamline Integration in Acquiring Bank Scenarios?

Discover how data virtualization and data profiling streamline bank acquisition integration, ensuring real-time data access and regulatory compliance.

The article was originally published at Ideanics.com https://www.ideanics.com/post/how-can-data-virtualization-and-data-profiling-streamline-integration-in-acquiring-bank-scenarios

M&A Article Set are at: https://www.ideanics.com/blog/categories/banking-mergers-and-acquisitions

Executive Summary

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Key Takeaways:

• Data virtualization provides real-time access to data from multiple, disparate systems without the need for traditional ETL processes, allowing banks to query and integrate data across complex ecosystems during acquisitions.
• Data profiling ensures that the data is clean, consistent, and compliant before it is integrated into production systems, minimizing risks related to data quality, governance, and regulatory compliance.
• Together, these technologies enable faster access to critical customer, financial, and operational data, ensuring a seamless transition from System Integration Testing (SIT) environments during Mock testing to the final production conversion.

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Benefits for Different Roles:

• For IT Leaders and CIOs: Accelerated Data Integration: Virtualization eliminates the need for physical data migration, offering real-time access to data from several thousand systems. This improves the efficiency of onboarding, reporting, and operational use. Reduced Complexity: Virtualization abstracts the complexity of dealing with diverse platforms (e.g., mainframes, cloud, legacy systems), streamlining the overall data management process.
• For Risk and Compliance Teams: Improved Data Quality: Profiling ensures that all data used in risk assessments and governance processes is validated for accuracy and completeness. Regulatory Compliance: Virtualization combined with profiling ensures compliance with CCPA, GDPR, and other data privacy regulations through real-time audit trails, data masking, and access control.
• For Business Units and Analysts: Faster Insights: Virtualized data access allows immediate analysis of customer behaviors, product portfolios, and operational performance, without waiting for full migrations. Cross-Selling Opportunities: Product data from the acquired bank can be profiled and quickly integrated into cross-selling strategies, providing immediate business value post-conversion.

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Key Benefits of Data Virtualization and Profiling:

1. Real-Time Access: Allows immediate access to critical customer and financial data without waiting for long migration projects.
2. Data Profiling: Ensures data consistency and quality, which is critical for regulatory reporting, customer onboarding, and decision-making processes.
3. Governance and Compliance: Centralized governance through virtualization helps manage access control, audit logs, and ensures sensitive data is masked and compliant.
4. Operational Efficiency: Streamlines daily banking operations, such as payment processing and fraud detection, by ensuring clean, validated data through profiling and virtualization.

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By leveraging data virtualization and profiling, acquiring banks can handle the complexity of integrating thousands of systems while maintaining high standards for data quality, governance, and regulatory compliance. This approach accelerates time-to-value, reduces the operational burden of physical migrations, and ensures that both banks can fully operationalize their data in SIT and production environments.

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Overview

In today’s banking environment, mergers and acquisitions often result in the integration of vast, complex ecosystems comprising 3,000 to 10,000 applications spread across disparate systems. These include legacy mainframes, cloud-based platforms, and on-premise systems, all housing critical data such as customer records, financial transactions, loan portfolios, and more. For the acquiring bank, the challenge lies not only in accessing this data in real-time but also in ensuring its accuracy, quality, and compliance before it can be fully integrated into production systems.

Data virtualization, paired with data profiling, provides an innovative solution to this challenge. It offers real-time access to data across disparate systems without the need for physical movement or complex extract, transform, and load (ETL) processes. Meanwhile, data profiling ensures the quality and consistency of the data before integration, allowing the acquiring bank to minimize risk, ensure compliance, and streamline operations throughout the acquisition process.

This article explores five actionable use cases that demonstrate how data virtualization, enhanced with profiling, can be used to efficiently manage, profile, and integrate data from the acquired bank (Bank B) into the acquiring bank (Bank A). Each use case outlines the steps and activities involved in preparing data for conversion, migrating to System Integration Testing (SIT) environments during Mock, and assisting with the final conversion to production.

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Problem Context: Complex Data Ecosystems in Bank Mergers

When one bank acquires another, they inherit an array of systems that vary significantly in terms of technology, structure, and data governance. These systems often house critical customer, financial, and operational data that must be accessed and analyzed in real-time for decision-making. Traditional methods of data migration, involving time-consuming and costly ETL processes, introduce risks of data quality issues, compliance breaches, and operational delays. Moreover, the sheer volume and variety of data pose significant challenges in ensuring data consistency and accuracy across both banks.

Key Challenges:

• Accessing and integrating data from thousands of disparate systems.
• Ensuring the quality and consistency of data before conversion.
• Maintaining data compliance across regulatory standards such as CCPA, GDPR, and SOX.
• Minimizing operational disruption during data migration and conversion.

Data virtualization offers a powerful approach by creating a unified, real-time view of data from disparate systems, while data profiling enhances this process by ensuring the data is clean and consistent. This approach significantly accelerates the process of preparing data for migration to SIT environments during Mock tests and for the final conversion to the acquiring bank’s production systems.

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Use Case 1: Real-Time Access to Acquired Bank’s Customer Data (Conversion Readiness)

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Scenario

Bank A acquires Bank B and needs immediate access to Bank B’s customer data to profile it, validate it for quality, and prepare it for onboarding into Bank A’s CRM system during conversion week. Data must be accessible and ready for Mock testing in SIT environments before being finalized for the conversion.

Paths and Activities:

1. Data Virtualization Layer Setup: Initial Connection: The data virtualization platform is configured to connect to Bank B’s customer databases (relational, mainframe, cloud systems, etc.). Metadata Cataloging: A metadata catalog is created to map the structure of customer records across different systems. Unified View: A unified virtual table or dataset is created that presents customer data as one logical dataset, allowing seamless querying across systems.
2. Data Profiling for Customer Data: Data Quality Assessment: Profile data to check for missing fields, inconsistencies, and duplicates. Outlier Detection: Flag any anomalous entries to ensure that clean data is moved to the SIT environment.
3. Data Access for CRM Integration (SIT Preparation): Mock Testing in SIT: During the Mock test cycle, the data is loaded into the SIT environment via virtualization for real-time validation. The data remains invisible to the production environment but is fully accessible for SIT testing. API Virtualization: Bank A’s CRM system interacts with customer data via the data virtualization layer using APIs, abstracting the complexity of accessing data from multiple sources. Data Transformation and Filtering: On-the-fly transformations standardize customer names, address formats, and merge duplicates, ensuring the CRM system only accesses the correct data.

Benefits:

• Immediate access to customer data without waiting for a full migration.
• Improved data quality ensures accurate and reliable customer information before conversion.
• No duplication or movement of data until Mock testing in SIT environments is complete.

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Use Case 2: Faster Data Integration for Risk Assessment and Reporting (SIT and Conversion)

Scenario

Bank A needs to assess Bank B’s financial risks (loan portfolios, transactions, and investments) before final conversion. This requires profiling Bank B's production databases and loading data into the SIT environment for Mock testing, ensuring accuracy and compliance before migrating to the production environment.

Paths and Activities:

1. Initial Data Virtualization Setup: Virtual Data Federation: The data virtualization layer connects to Bank B’s financial systems, creating a unified dataset across loan management, investment portfolios, and transactional databases.
2. Data Profiling for Financial Data: Field-Level Analysis: Profiling checks key metrics such as loan amounts, interest rates, and payment schedules for consistency and completeness. Anomaly Detection: Detect and flag anomalies like negative interest rates or missing records.
3. Data Integration in SIT: Mock Testing in SIT: Financial data is moved into the SIT environment for comprehensive Mock testing and validation. Real-Time Profiling and Transformation: On-the-fly transformations ensure that the data conforms to Bank A’s risk models and reporting requirements during testing.

Benefits:

• Real-time risk reporting using profiled, validated data improves decision-making.
• Profiling in the SIT environment ensures data readiness for final conversion.
• No need for complex ETL processes, reducing costs and operational delays.

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Use Case 3: Data Consistency and Governance During Acquisition (Governance and Mock Testing in SIT)

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Scenario

Bank A needs to enforce consistent governance policies across both banks and ensure that data (including sensitive information) meets regulatory standards before moving to production. Data profiling is used to ensure data quality, while data virtualization facilitates the process of loading and validating data in the SIT environment during Mock testing.

Paths and Activities:

1. Governance Policy Integration: Centralized Data Governance Layer: The data virtualization platform applies Bank A’s governance policies to data from Bank B, ensuring only compliant data is visible in the SIT environment.
2. Data Profiling for Sensitive Data: Profiling ensures that Bank B’s sensitive data (e.g., PII) meets governance standards before it becomes operational in production.
3. Mock Testing and Final Conversion: Audit Trails and Compliance Reporting: The SIT environment uses the profiled data to test governance and compliance features before the final conversion. Data Masking: Data virtualization applies masking rules for sensitive fields like social security numbers during Mock testing.

Benefits:

• Improved data governance ensures compliance with regulatory standards.
• Centralized control of data handling across multiple systems during the Mock cycle.
• Data is ready for conversion with quality and governance validated.

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Use Case 4: Merging Product Portfolios Across Both Banks (Cross-Selling Preparation)

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Scenario

Bank A wants to cross-sell Bank B’s products to its customers post-conversion. Before this, Bank B’s product data must be profiled for accuracy and consistency in the SIT environment during Mock testing to ensure it aligns with Bank A’s offerings.

Paths and Activities:

1. Virtualized Product Catalog Creation: Cross-System Data Aggregation: The data virtualization platform aggregates Bank B’s product data and presents it as a unified catalog in the SIT environment.
2. Data Profiling for Product Data: Profiling ensures that product attributes (e.g., loan types, interest rates) are accurate and consistent across systems.
3. Data Testing in SIT (Cross-Selling Readiness): During Mock testing in SIT, Bank B’s product data is profiled and validated for cross-selling purposes, ensuring it matches Bank A’s standards.

Benefits:

• Faster product integration ensures readiness for cross-selling post-conversion.
• Profiling in SIT ensures product data consistency with Bank A’s offerings.

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Use Case 5: Operational Efficiency in Daily Banking Operations (With Data Profiling in SIT)

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Scenario

Bank A needs to streamline daily operations such as payment processing and fraud detection. Bank B’s transaction data must be profiled and validated during Mock testing in the SIT environment to ensure operational readiness post-conversion.

Paths and Activities:

1. Virtual Transaction Data Layer: Profiling checks for errors in transaction data before it is used in SIT testing for payments and fraud detection.
2. Real-Time Fraud Detection: Data profiling in SIT ensures that anomalous transactions can be identified during fraud detection.

Benefits:

• Improved operational efficiency through validated transaction data.
• Enhanced fraud detection using clean data in SIT before conversion.

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Conclusion: A Holistic Approach for Bank Integration Using Data Virtualization and Profiling

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Data virtualization provides a powerful solution for accessing, integrating, and managing data from thousands of disparate systems during a bank acquisition, while data profiling ensures the quality, accuracy, and consistency of this data before it is used for operations, reporting, or decision-making. Together, these technologies enable the acquiring bank to overcome the challenges of complex data ecosystems, improve risk management, ensure compliance, and enhance operational efficiency.

By leveraging both virtualization and profiling, Bank A can achieve real-time access to critical data without the need for extensive migration or ETL processes, while simultaneously maintaining high standards of data quality and governance throughout the integration process. This ensures readiness for Mock testing in SIT environments and a smooth transition to production during the final conversion.

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Author: Shawkat Bhuiyan

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Related Articles:

• How Data Virtualization Assists with Bank Acquisition?
• Bank M&A Overview

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Hashtags

#BankAcquisition, #MergerandAcquisition, #AcquisitionIntegration, #AcquistionPlayBook, #DataConversion, #DataVisualization, #DataProfiling, #ITStrategyandArchitecture, #DataArchitecture, #EnterpriseArchitecture