Approaching duplicate data elements and formulating deduplication rules in data transformation using Power BI calls for extreme caution in Power Query. Below are the main action steps that I typically adhere to when resolving the issues associated with duplicate entries:
Remove duplicates: In Power Query, my first step is ‘Remove Duplicates,’ which can be found within the ‘Transform’ tab. By this step, I can indicate the respective columns for which only those records that are not repeated will exist in the file. However, it would be prudent to suggest that I do not select all the columns but rather the ones that best define the records. For example, you have a table with customers, and you will be looking for “duplicates,” meaning you will either want the field “Customer ID” alone or some other combination of fields like “Order Date” and “Product ID” to find duplicates.
Advanced Deduplication with Group By: At times, it is simply not enough to remove duplicates, especially when I am required to perform some data aggregation. In these instances, I make use of the “Group By” feature. This enables me to group certain columns of data and then perform aggregations on other columns to eliminate duplicates more constructively. For instance, in cases where there are many entries for a single sale of a customer, I will group them according to “Customer ID” and sum the sales amounts.
Custom Deduplication Logic: There are instances where the dataset has more advanced duplication patterns, such as partial duplicates, among others. In this case, I may employ custom logic instead. I create conditional columns or leverage Power Query M code, for instance, to implement specific guidelines for removing duplicates. For example, when there are a number of records for a particular product written slightly differently, I will apply logic to take the one with the highest sales figure or the most recent one.
Handling Nulls or Inconsistent Data: Sometimes, the duplicates I encounter can be a result of null or inconsistent data present in some of the columns. As such, I first tackle the issue by cleaning the data. I do this either by deleting all rows that contain null values or by putting in some default values. After this stage of cleaning up all the excess data, I would then proceed to apply the deduplication procedures without worries about any inaccuracy in the data set.
