As a Data Analyst at XYZ Corporation, ensuring data quality is a core part of my role. I've encountered numerous instances of missing or corrupted data in our datasets, which can lead to inaccurate insights or decisions if not carefully handled.
For instance, while working on a project to improve our predictive sales model, I realized that there were missing values in our historical sales data. Using SQL and Python with the pandas library, I performed a thorough missing data analysis. Applying my learned techniques, I used multiple imputations to fill in the missing values based on related data points, ensuring we didn't introduce bias or inaccuracies into our model.
We similarly dealt with a few corrupted values, which were beyond the feasible range for certain variables. By creating robust data validation rules and filters, we quickly identified and removed these outliers. The thoughtful handling of our data quality issues boosted our model's predictive power and ultimately improved our sales forecasting capabilities.