SIMULATION - Explain the 4 primary steps in the typical de-dupe process.
See the solution below.
1
Establish what qualifies as a duplicate.
2
Find a common identifier.
3
Determine which other fields and methods can be used to de-dupe.
4
Merge the losing records into the winning record.
The de-duplication process is an essential part of data management in any organization. It involves identifying and removing duplicate records from a database, which helps to improve data quality and accuracy. The typical de-duplication process involves four primary steps:
Data Profiling: The first step in the de-duplication process is to conduct data profiling. Data profiling is the process of analyzing the data to identify potential duplicates. This is typically done using automated tools that can compare the data in the database and identify records that have similar attributes.
Data Matching: Once potential duplicates have been identified, the next step is to match them. This involves comparing the data in the potential duplicate records to determine if they are in fact duplicates. Matching can be done using a variety of criteria, such as name, address, email, phone number, or a combination of these.
Data Merging: Once duplicates have been identified and matched, the next step is to merge them. This involves consolidating the data from the duplicate records into a single record. The merged record should contain all the relevant information from the duplicate records, while also eliminating any duplicate data.
Data Verification: The final step in the de-duplication process is to verify the accuracy of the merged record. This involves reviewing the data to ensure that all relevant information has been included and that there are no errors or omissions. Data verification is a critical step in the process, as it ensures that the de-duplication process has been successful in improving data quality.
Overall, the de-duplication process is an iterative process that may need to be repeated multiple times to ensure that all duplicates have been identified and removed. By following these four primary steps, organizations can improve data quality, reduce errors, and enhance the accuracy of their data-driven decision-making processes.