I have extensive experience in data quality management, having worked on projects that involved data cleansing, validation, and integration. I am proficient in using tools like SQL and Python to identify and resolve data discrepancies, ensuring that the data is accurate, consistent, and reliable. My approach involves setting up automated checks and monitoring systems to maintain high data quality standards.

I prioritize tasks by assessing the impact of data quality issues on business operations. Critical issues that affect decision-making or customer experience are addressed first. I also consider the complexity and feasibility of resolving each issue, ensuring that resources are allocated efficiently. Regular communication with stakeholders helps me stay aligned with their priorities.

I implement a combination of automated and manual strategies to ensure data accuracy. This includes setting up data validation rules, conducting regular audits, and using data profiling tools to identify anomalies. I also encourage a culture of data stewardship among team members, emphasizing the importance of accurate data entry and maintenance.

When data quality issues arise during a project, I first assess the severity and impact on the project's objectives. I then collaborate with the relevant teams to develop a remediation plan, which may involve data cleansing, updating processes, or providing training to prevent future issues. Transparency and regular updates to stakeholders are key to managing these situations effectively.

In a previous role, I led a project to improve data quality in a customer database. By implementing a comprehensive data cleansing process and introducing automated validation checks, we reduced data errors by 40%. This improvement not only enhanced the accuracy of reporting but also improved customer satisfaction by ensuring that marketing campaigns were targeted more effectively.