To ensure data integrity during migration: Before: Profile, cleanse, and validate source data. Test on a sample. During: Use ETL tools, validate data, handle errors, and monitor progress. After: Reconcile data, validate in the target, perform user testing, and monitor continuously. Also: Prioritise security, documentation, and communication. This helps ensure data accuracy and consistency, minimising risks.

Working in Data space for over 20+ years, here are 4 things to do to ensure nsure data integrity stays intact: 1. Define, automate Data Integrity & Validation checks data transformation/ ETL incl: - Profiling - Automatically generate metadata and statistics about data by analyzing data attributes, identifying inconsistencies - Cleanse - missing values, inconsistencies, duplicates, and outliers - Standardize - Auto convert data to a consistent format, such as standardizing dates, times, and currency. - Transform & Handle Errors 2. Conduct Periodct DQ assessment - measure using metrics such as completeness, accuracy, consistency, and timeliness 3. Testing and Validation - Unit, Integration, Comparison test 4. Monitor - Post migration

Data profiling and quality monitoring, along with business validation and data reconciliation mechanisms, are crucial for any data warehousing system. Implementing a feedback system that enables a feedback loop to business teams can facilitate system corrections, fixes, and validations. This ensures that business systems receive quality data inputs from users. This continuous loop helps businesses acquire reliable data for informed decision-making

In migration scenarios maintaining data integrity involves not just quality checks but also reducing the data footprint. - Build an inventory of data in the source system, manually or via catalog tool - Tag/Flag data that needs to be migrated - Identify the quality of the data source by - profiling the data to get a sense check - Cleanse the data, if possible, before moving to target system - After migration a profile the data along with comparison with source system - either by sampling or 1:1 - Finally document your findings

Ensuring data integrity is crucial. Start by conducting a thorough data audit to understand the current data landscape, identifying inconsistencies or errors. Use ETL processes that incorporate data validation and transformation rules to maintain accuracy. Implement checksum validations and record counts to ensure data is transferred without loss or corruption. Post-migration, perform extensive data reconciliation by comparing source and target data. Always involve stakeholders to validate business-critical data throughout the process. #DataMigration #DataIntegrity