Missing Values in Power Query? Recover Them Instead of Replacing Them!

Are you working with missing values in Power Query? Before replacing them with a mean, median, mode, or another value, it is worth checking whether the missing information can be recovered from an existing record. In this video, I’ll show you how to recover missing *Age* values using a reference table created from the existing Sales data, even when a separate customer reference table has not been provided. You’ll learn how to: 🔹 Add an Index column to preserve the original row order 🔹 Create a duplicate query from the Sales data to build an Age reference 🔹 Keep only Customer ID and Age in the reference query 🔹 Filter the Age column to retain only non-null values 🔹 Remove duplicate Customer IDs so each customer has a reference Age 🔹 Use Merge Queries to match Customer IDs and recover the missing Age values 🔹 Expand the recovered Age values into the Sales data 🔹 Sort the Index column to restore the original row order 🔹 Load the Age Reference query as a connection instead of creating an unnecessary worksheet 🔹 Remove the Age Reference worksheet to keep the workbook organized and focused on the data needed for analysis What if the missing value cannot be recovered? Using a reference record is the first method to consider when a reliable value can be recovered from the data. If the value cannot be recovered, other approaches may be considered depending on the situation, such as using the mean, median, mode, an "Unknown", or leaving the value as null. The key takeaway is: *Recover the actual value when possible. Replace it only when recovery isn't possible and the chosen method is appropriate for the data.* This is a practical Power Query example of how to approach missing values systematically rather than immediately replacing them. Subscribe to *Stepwise Insights* for practical lessons on Excel, Power Query, SQL, and data analysis. #PowerQuery #DataCleaning #MissingValues #Excel #DataAnalysis #MergeQueries #StepwiseInsights