In my view, managing data quality while meeting tight deadlines requires a focused approach. For example, in a recent project, I had to resolve data discrepancies that impacted reporting. I first assessed which errors had the most significant effect on business outcomes and addressed those immediately. By automating data validation processes, I saved time on repetitive checks. Regular updates to stakeholders helped manage expectations. This approach allowed me to maintain data quality without compromising the project timeline.

Dealing with Data Quality Issues: An Agile Approach: Attempting to tackle all data quality issues at once can be overwhelming and counterproductive. 3 key points to handle data quality issues in agile way: A. Focus on High-Priority Analytics : Identify the most critical analytics projects that will drive business value and focus on cleaning data specific to thisr projects. B. Clean Data Elements in Iterations: Break down data quality efforts into manageable iterations. Clean and validate these data elements in a series of sprints. C. Measure Progress and Refine: Regularly measure the impact of data quality efforts on business outcomes. Use these insights to refine the data quality roadmap, adjust priorities, and identify new areas.

First, I will identify the reasons for data quality issues. If the issues are due to: 1. Format standardization, text inconsistencies, missing data, or duplicates, I will use advanced tools or Python libraries (like Pandas) to handle these tasks and meet the deadline. 2. Technical or logical errors that require serious attention and can't be solved using advanced tools, I will assess their impact on the outcome: - If the impact is minimal and can be ignored or resolved in the future, I will document the issues along with the assumptions I made during the analysis. - If the impact is significant, I will communicate with the stakeholders about the data quality issues and their effect on decisions and request a revision of the deadline.