The importance of data quality cannot be overstated. Accurate, reliable, and consistent data builds the foundation for sound data-informed decision-making, successful business operations, and trust of stakeholders.
WashU’s Data Governance Program focuses on building data proficiency, especially as Artificial Intelligence (AI) and predictive analytics grows. To be successful, data quality must remain a top priority and driven by a continuous improvement culture to adapt for evolving business needs. Some key aspects include:
Data Quality Policies and Procedures
Data governance enables the creation of data quality policies and procedures. These guidelines help identify data quality issues, outline steps for resolution, and establish preventive measures. We can proactively and efficiently address data quality challenges with these policies and procedures in place.
Data Stewardship Community
The Data Stewardship Community oversees the quality of specific data sets within data domains. They take ownership of data quality by monitoring and ensuring it meets predefined standards. This ownership is a powerful motivator for continuous improvement and maintaining data quality.
Monitoring Data Lineage and Traceability
Data governance promotes data lineage and traceability. Knowing where the data comes from, how it is transformed, and where it goes is critical. When you can trace the journey of data, issues can quickly be identified and rectified.