In data cleaning, how should inconsistent date formats be handled?

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Multiple Choice

In data cleaning, how should inconsistent date formats be handled?

Explanation:
Standardize and verify. When dates appear in multiple formats, converting them to a single, clear format (for example, ISO 8601: YYYY-MM-DD) prevents misinterpretation and makes sorting, filtering, and joining reliable. After standardizing, check each date against the source documents to confirm accuracy and catch any transcription errors. This approach keeps the data consistent and auditable, which is essential for trustworthy analysis. Leaving dates inconsistent, substituting with random dates, or deleting date fields would either propagate errors, corrupt analysis, or remove valuable temporal information.

Standardize and verify. When dates appear in multiple formats, converting them to a single, clear format (for example, ISO 8601: YYYY-MM-DD) prevents misinterpretation and makes sorting, filtering, and joining reliable. After standardizing, check each date against the source documents to confirm accuracy and catch any transcription errors. This approach keeps the data consistent and auditable, which is essential for trustworthy analysis. Leaving dates inconsistent, substituting with random dates, or deleting date fields would either propagate errors, corrupt analysis, or remove valuable temporal information.

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