How can you maintain data quality during migration to a modern system?

1 Assess your data sources

Before you start the migration process, you need to understand the current state of your data sources, such as databases, files, applications, or external sources. You should identify the data owners, stakeholders, formats, standards, definitions, and dependencies of each source. You should also evaluate the quality of your data sources by performing data profiling, data cleansing, and data validation. This will help you discover and resolve any data quality issues, such as missing values, duplicates, errors, or inconsistencies.

2 Define your data quality requirements

Once you have assessed your data sources, you need to define your data quality requirements for the target system. You should align your requirements with your business objectives, user expectations, and regulatory compliance. You should also specify the data quality dimensions, metrics, thresholds, and rules that you want to apply to your data in the target system. For example, you may want to measure the completeness, accuracy, timeliness, or uniqueness of your data.

3 Plan your data migration strategy

After you have defined your data quality requirements, you need to plan your data migration strategy. You should choose the appropriate migration method, such as big bang, incremental, or hybrid. You should also select the suitable migration tools, such as ETL (extract, transform, and load), ELT (extract, load, and transform), or data integration platforms. You should also design the data mapping, transformation, and enrichment processes that will ensure the compatibility and quality of your data in the target system.

4 Execute your data migration project

When you execute your data migration project, you need to follow a structured and iterative approach that involves multiple phases, such as preparation, extraction, transformation, loading, testing, and verification. You should also implement data quality checks and controls at each phase to monitor and maintain the quality of your data throughout the migration process. You should also document and report any data quality issues, actions, and results.

5 Monitor and improve your data quality

After you complete your data migration project, you need to monitor and improve your data quality in the target system. You should establish a data quality framework that defines the roles, responsibilities, policies, procedures, and standards for data quality management. You should also use data quality tools and techniques, such as data quality dashboards, reports, alerts, audits, or feedback mechanisms, to measure and improve your data quality performance. You should also foster a data quality culture that encourages collaboration, communication, and continuous learning among your data users and stakeholders.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?