I employ a combination of automated tools and manual checks to identify data quality issues. Tools like data profiling and validation scripts help detect anomalies, while manual reviews ensure context-specific accuracy. Once issues are identified, I prioritize them based on impact and collaborate with relevant teams to resolve them. Root cause analysis is key to preventing recurrence. I also establish data quality metrics and monitor them regularly to maintain high standards.

To ensure data consistency, I implement data governance frameworks and standardization protocols. I use ETL processes to synchronize data across systems and enforce validation rules to maintain uniformity. Regular audits and reconciliation checks are conducted to identify discrepancies. Collaboration with IT and business teams ensures alignment on data definitions and formats, reducing inconsistencies.

In a previous role, I noticed inconsistent customer data across CRM and ERP systems. I initiated a data cleansing project, standardizing formats and removing duplicates. I also implemented validation rules to prevent future errors. This improved reporting accuracy by 20% and enhanced customer segmentation efforts, leading to better-targeted marketing campaigns.

I have used tools like Informatica Data Quality, Talend, and SQL-based scripts for data profiling, cleansing, and validation. These tools help automate error detection and ensure compliance with data standards. I also leverage Excel for manual checks and Power BI for visualizing data quality metrics, enabling stakeholders to monitor progress effectively.

To address human errors, I focus on training and process improvements. I conduct workshops to educate teams on data entry best practices and the importance of accuracy. Additionally, I implement validation rules and automated checks to catch errors early. Regular feedback loops and error tracking help identify recurring issues, allowing for targeted interventions to reduce mistakes over time.