What are the most effective techniques for data quality improvement in a regulated industry?

2 Implement data quality rules and standards

The next step to improve data quality is to implement data quality rules and standards that define the expected level of data quality for your data warehouse. These rules and standards should be aligned with the regulatory requirements and business objectives of your industry. You can use data quality management software or custom scripts to enforce these rules and standards, such as data validation, data transformation, data deduplication, and data enrichment. By implementing data quality rules and standards, you can ensure that your data warehouse meets the quality expectations of your stakeholders and regulators.

5 Review and improve data quality processes

The fifth step to improve data quality is to review and improve data quality processes that document and standardize the best practices and procedures for data quality improvement and monitoring. You can use data quality management methodologies and frameworks to review and improve these processes, such as the Data Quality Assessment Framework (DQAF), the Total Data Quality Management (TDQM), or the Data Quality Improvement Cycle (DQIC). By reviewing and improving data quality processes, you can ensure that your data quality improvement and monitoring efforts are consistent, effective, and continuous.

6 Learn from data quality feedback and incidents

The sixth and final step to improve data quality is to learn from data quality feedback and incidents that provide you with valuable insights and lessons for data quality improvement and monitoring. You can use data quality feedback mechanisms and channels to collect and analyze data quality feedback from your data users, customers, and regulators, such as data quality surveys, data quality reviews, data quality complaints, and data quality audits. You can also use data quality incident management tools and procedures to record and investigate data quality incidents, such as data quality errors, data quality breaches, data quality losses, and data quality fines. By learning from data quality feedback and incidents, you can identify and address data quality gaps, risks, and opportunities, and continuously improve your data quality performance.