How would an expert analyst precisely identify and remove duplicate customer records in a dataset where duplicates are defined by identical values across three specific columns (e.g., Email, Phone, Address) while other columns may vary?

An expert analyst precisely identifies and removes duplicate customer records by systematically applying a multi-stage process, beginning with data standardization. The first crucial step is data standardization (also known as data normalization), which involves transforming the values within the specified three columns (Email, Phone, Address) into a consistent format. For Email, this typically means converting all characters to lowercase and trimming any leading or trailing whitespace. For Phone, it involves removing non-numeric characters like hyphens, spaces, or parentheses, and potentially standardizing country codes or prefixes. For Address, standardization might include consistently abbreviating street types (e.g., "Street" to "St"), converting to a uniform case, and removing excessive internal spaces. This ensures that variations in input format, such as "john.doe@example.com" and "JOHN.DOE@EXAMPLE.COM" or "123-456-7890" and "(123) 456-7890", are recognized as identical. Once the data is standardized, the analyst proceeds to duplicate identification. This is commonly achieved by creating a unique composite key for each record, formed by concatenating the standardized values of the Email, Phone, and Address columns. Records sharing an identical composite key are identified as duplicates. Using database query languages like SQL, this can be efficiently performed by grouping records based on these three standardized columns and then filtering for groups where the count of records is greater than one. For instance, `SELECT Email_standard, Phone_standard, Address_standard FROM Customers GROUP BY Email_standard, Phone_standard, Address_standard HAVING COUNT(*) > 1` would identify the sets of duplicate key combinations. After identifying groups of duplicates, the next step is master record selection, which determines which record within each duplicate group should be retained and which ones should be removed. This critical decision is based on predefined business rules or criteria. Common strategies include retaining the record that is the most complete (e.g., has the fewest null values in other important columns), the most recently updated, the oldest by creation date, or the one associated with a specific primary key value (e.g., the lowest `customer_id`). The chosen criteria aim to preserve the richest or most accurate information. For example, if a duplicate group contains three records, and the rule is to keep the most recently updated, the analyst would identify the record with the latest `last_updated_timestamp` within that group. Finally, duplicate removal is performed. Once the master record is designated for each duplicate group, all other records within that group are systematically eliminated from the dataset. In a database environment, this is often implemented using advanced SQL techniques like Common Table Expressions (CTEs) combined with window functions (e.g., `ROW_NUMBER()`) partitioned by the standardized duplicate columns and ordered by the master record selection criteria. Records with a `ROW_NUMBER()` greater than one (meaning they are not the designated master) are then deleted. In programming environments, this involves filtering the dataset to include only the selected master records and excluding the non-master duplicates, thereby creating a new, deduplicated dataset. A crucial final step is verification, where the analyst re-runs the duplicate identification process on the cleaned dataset to confirm that no duplicates remain based on the specified criteria, ensuring the accuracy and integrity of the deduplication process.