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 co....