Imagine you are an analyst handling messy data and you need to build a dashboard for the team to understand the relationship between the numbers. The journey begins with data cleaning in Power Query, where inaccurate, duplicate, or missing values are corrected.
Managing missing values is a critical step in data cleaning, as it improves data quality and ensures accurate analysis. Here is the trick:
Text columns: Replace missing values with "N/A" or "Unknown" to indicate that the information is unavailable while maintaining consistency in the dataset.
Numeric columns: Missing values can either be left as blank (null) or replaced using appropriate statistical measures such as the mean, median, or mode, depending on the nature of the data and the analysis being performed. Numeric fields are generally the only columns where leaving blanks is acceptable without affecting data integrity.
Rows with excessive missing data: If a row contains approximately 90% missing values, it is often best to remove it, as it contributes little or no meaningful information and may negatively impact the quality and reliability of the analysis.
