How do you identify and eliminate duplicate data?
Efficiency question :
Microsoft Excel and Power Query provide features for identifying and removing duplicate data. Before using them, however, the data should be well structured.
In Excel, it is preferable to organize data as a table. This makes it easier to manipulate and to identify the columns that will be used to detect duplicates.
In Power Query, data is already organized in tabular form and transformations can be applied directly to individual fields. Duplicate removal can therefore be incorporated into the data-preparation steps.
Key considerations:
Before removing duplicates, define precisely what constitutes a duplicate.
Two records may contain some identical information without representing the same record. It is therefore important to choose the correct columns for determining whether two records should be considered identical.
The quality of the result depends directly on these criteria. Duplicate removal should eliminate genuinely repeated records without deleting data that must be retained.
Challenges:
The challenge is to remove duplicates without losing any required records.
The columns used to identify duplicates must therefore be carefully selected before processing. Criteria that are too broad may leave duplicates in place, while poorly defined criteria may delete valid records.
This verification is especially important when data comes from several loads or when only new data is retrieved at each refresh.
Technology:
Microsoft Excel and Power Query can both remove duplicates.
In Excel, this can be done directly on data structured as a table. Power Query can incorporate duplicate removal into query steps and repeat the process automatically on subsequent refreshes.
SQL also provides options for identifying or eliminating duplicates directly in queried data.
Developer's advice:
When working with partial data loads, first combine the new data with the history already retrieved before removing duplicates.
One method is to append the new data to data from previous query runs. Duplicates are removed only after this consolidation.
The sequence matters: consolidate first, remove duplicates second. This preserves all available records before identifying those that truly occur more than once.
Finally, pay close attention to the columns used to determine duplicates. The objective is not simply to reduce the number of rows, but to obtain a complete dataset in which each required record is retained once.