Data Migration and Management Strategies in Solution Implementation

Correct Answer: Profile data sources, establish validation rules, perform iterative cleansing, verify data quality metrics

The best sequence for data cleansing follows a logical progression that ensures data quality and minimizes risks: 1. Profiling data sources is the essential first step to understand the current state and scope of data 2. Establishing validation rules creates a framework for quality standards 3. Performing iterative cleansing allows for gradual improvement and validation 4. Verifying data quality metrics confirms the success of the cleansing process This sequence is optimal because it starts with understanding (profiling), sets standards (validation rules), executes the work (iterative cleansing), and measures results (quality metrics). The other options have flaws: - Creating backup systems before understanding data quality needs is premature - Executing full data transfer before proper validation creates unnecessary risks - Starting with field mapping before understanding data sources may lead to incorrect assumptions and rework The correct sequence ensures a methodical, controlled approach to data cleansing that reduces risks and improves outcomes.

Correct Answer: Implement parallel processing with phased validation checkpoints to verify data consistency

Implementing parallel processing with phased validation checkpoints is the optimal strategy for large-scale data migration because: - It allows for continuous monitoring and validation of data quality throughout the migration process - The phased approach helps identify and resolve issues early in the process - Parallel processing maintains system performance and reduces downtime - Validation checkpoints ensure data integrity at each stage Why other approaches are less effective: - Full data transfer with post-migration validation risks discovering problems too late in the process, making corrections more complex and costly - Performing migrations during peak business hours creates unnecessary risks and can impact business operations negatively - Using automated transformation tools that modify source data structures during migration introduces additional complexity and risks to data integrity The best approach maintains operational continuity while ensuring data quality through systematic validation and parallel processing, reducing risks and allowing for proper quality control at each stage of the migration.

Correct Answer: Establish a master record based on predefined business rules and merge relevant attributes

Establishing a master record based on predefined business rules and merging relevant attributes is the best approach because: 1. It ensures consistent data governance through structured business rules 2. It maintains data integrity by following established organizational standards 3. It allows for systematic attribute consolidation based on business requirements 4. It provides clear traceability and documentation of merge decisions The other approaches are less suitable because: Automated deduplication based solely on timestamps may not consider business context and could lead to loss of important data variations. Creating new unique identifiers for all records would unnecessarily duplicate data and complicate system maintenance. Using only data completeness metrics for scoring may overlook important business rules and context, potentially retaining less relevant but more complete records.