Data Migration - A Methodical Approach

Explore the end-to-end data migration process, including ETL, profiling, mapping, cleansing, transformation, and validation, to ensure data integrity during post-acquisition integration.

Assess the global context of financial services mergers and acquisitions and the need for successful data migration. Identify consequences of failures, desired outcomes, and data migration categories in bank integrations.

Explore the ETL process—extract, transform, load—to integrate data from multiple sources, define the target data model, apply transformation rules, and deploy a scalable data migration solution focused on data quality.

Data profiling analyzes data structure, content, quality, and relationships to assess suitability for the target system, identify issues such as missing or duplicate data, and guide migration.

Transform data by converting and restructuring from source to target formats through data extraction, cleansing, mapping, and validation, enabling accurate migration, integration, and analysis.

Explore data cleansing as a crucial step in data migration, from identifying quality issues via data profiling to developing cleansing rules and validating standardized, transformed, and deduplicated data.

Explore scripting, ETL and data quality tools to implement cleansing rules and validate results, while emphasizing skilled personnel and iterative, well-documented data cleansing practices.

Identify data sources and review requirements to decide data exclusion from migration. Exclude obsolete data or data with privacy or security concerns; document extrusion and reasons, and obtain stakeholder approvals.

Learn to document data exclusion using a policy, criteria, and logs, and use quality reports and risk assessments to ensure compliant, transparent decisions.

Explore how data retention requirements drive data exclusion documentation, guiding retention periods, locations, controls, and monitoring to ensure compliant, secure data governance.

Identify and map data elements between source and target systems, define mapping rules, validate mappings, and manage changes using a robust data mapping document and mapping matrix.

Validate data mapping rules through defined testing, execute test cases, verify data accuracy, and document discrepancies to ensure compliant migration in financial services.

Explore change management in data mapping, detailing the change control process from requests for change to evaluation, approval, implementation, and verification, plus ownership and documentation to protect timelines and quality.

Approve change requests via the change control board by assessing impact, feasibility, cost, and downstream effects, then test and document results to ensure a controlled, successful data migration.

Validate data during migration by verifying accuracy, completeness, and consistency with profiling, rule based checks, and cross referencing external sources to ensure quality. Define criteria and document results.

Explore practices in data validation, including peer validation criteria, completeness, accuracy, consistency, and timeliness; implement comprehensive testing—field level and data type tests—plus data reconciliation, root cause analysis, and continuous monitoring.

Define the scope and develop a comprehensive data migration plan. Identify data sources, map and transform data, validate through testing, and engage stakeholders.

Define and track data migration okrs to align data consolidation, data security, data quality, disruption minimization, and timely completion across the organization.

Document and track every step of data migration with inventories, flow diagrams, lineage, issue logs, and change records, then compile a final report that auditors can review.

Develop a comprehensive testing strategy for data migration, validating process, data quality, transformation, and load in a production-like environment. Verify results by comparing migrated data to originals and involving stakeholders.

Explore unit testing across field level, business rule, data type, and data completeness checks to ensure accurate, reliable data migration with error handling, reconciliation, and rollback strategies.

Audit the entire system through system testing to verify performance, security, and functional requirements, ensure integration across components, and validate usability, compatibility, and data migration.

Data migration user acceptance testing validates migrated data against business requirements with end users and stakeholders, ensuring accuracy, completeness, and production readiness through scenario, alpha/beta, compatibility, usability, and regression testing.

Examine non-functional testing to validate performance, security, usability, and scalability in data migration, using stress and load testing, response time, and throughput metrics.

Learn how data migration testing verifies complete, accurate, and consistent data transfer from source to target, including pre and post migration checks, integrity, validation, and transformation.

Explore high profile data migration failures in financial services, analyze two merger and acquisition cases, RBS NatWest and TSB, and derive root-cause insights and lessons for future migrations.

Explore the RBS NatWest merger and how data migration challenges—data mapping, validation, testing, and legacy system integration—led to data discrepancies, service disruptions, and hundreds of millions in losses.

Examine the TSB acquisition by Banco Sabadell and the data migration failures, including FCA and PRA fines, leadership changes, and customer redress costs, that caused data inaccuracies and service disruptions.

Identify root causes behind data migration failures—planning, data quality, resources, testing, communication, and change management—and apply best practices to mitigate risks and enable successful migrations.