What are the best practices and tools for data quality assurance during and after a database migration?

The data migration scope is?the subset of data that needs to be migrated from the source to the target system. It can be defined by various criteria, such as business functions, data entities, data attributes, data quality, data dependencies, or data transformations.

Outline the specific goals of the migration, such as improving data accessibility, integrating systems, or enhancing performance. Create a timeline that includes key milestones, deadlines for each phase of the migration, and dependencies between tasks. Define who will be involved in the migration, including project managers, data analysts, IT staff, and any external vendors. Identify all data sources involved in the migration, including databases, applications, and file systems. Document the formats of the data being migrated (e.g., structured, unstructured) and ensure compatibility with the target system. Calculate estimated costs associated with the migration, including tools, personnel, and potential downtime.

Use data profiling tools to examine the structure, content, and relationships of the source data. This helps identify data types, formats, and potential issues. Use tools and techniques to identify errors, such as typos, incorrect formats, or invalid entries. SQL queries can be effective for flagging issues. Supplement the existing data with additional relevant information to improve its quality and usefulness. This can involve integrating external data sources or using APIs. Use algorithms or tools to identify duplicate records within the source data. SQL queries and Excel functions can help flag duplicates based on specific criteria.

Data cleaning or data scrubbing, is the process of fixing incorrect, incomplete, duplicate or otherwise erroneous data in a data set. It involves identifying data errors and then changing, updating or removing data to correct them.

8 steps to follow in the data migration process. # Define the scope. # Study requirements, business rules and mapping document. # Dependencies and interactions. # Create test cases and queries to verify the data. # Execute test cases. # Compare the results. # Non-functional testing. # Functional application test.

Use tools or scripts to compare the migrated data against the original source data. This ensures that all records were transferred accurately. Compare aggregate values (e.g., sums, averages) between the source and target systems to identify discrepancies. Review audit logs and data lineage to ensure data integrity throughout the migration process. Use dashboards or reporting tools to create visual representations of data quality metrics, making it easier to identify trends or issues. Ensure that the migrated data complies with relevant business rules and regulations. This may involve checking for mandatory fields, data formats, and specific constraints. Set up alerts for any anomalies detected after migration.

Implement data quality software that provides continuous monitoring of data for quality metrics such as accuracy, completeness, and consistency. Schedule periodic data quality audits to assess the state of the data and ensure compliance with established quality criteria. Establish mechanisms for users to report data quality issues, facilitating continuous improvement based on real-world usage. Create a data governance framework that defines roles, responsibilities, and policies related to data quality management. Establish clear data quality policies that outline expectations for data quality and the processes for achieving and maintaining it.

After migration, continuous monitoring and improvement of data quality are essential. Using tools like data quality software, alerts, and workflows helps maintain and enhance data reliability. This ongoing process ensures your data remains valuable and accurate in the new system.

Therefore, planning for data quality should include processes for validity checks, audits, and explanations for variants in the data. When planning for data quality, it is also important to consider the fit for the intended use of the data (e.g., for operations, decision making, and planning).