You merged a dataset from two systems. One uses MM/DD/YYYY. The other uses DD/MM/YYYY. "01/06/2024" — is that January 6th or June 1st? You can't tell from the value alone. And neither can your analytics tool. Every date calculation in your merged dataset is now unreliable.
Mixed date formats are one of the most common and most damaging data inconsistencies in CSV files. Here's how to identify and fix them.
The Most Common Date Format Problems
MM/DD/YYYY vs DD/MM/YYYY: The classic confusion. Ambiguous for any date where day and month are both ≤ 12.
2-digit vs 4-digit years: "24" could mean 1924 or 2024. Systems that default to 1924 cause obvious problems; systems that default to 2024 cause subtle ones.
Dates stored as text: Excel often imports dates as text strings, especially when the format doesn't match its expected format. Text dates don't sort, filter, or calculate correctly.
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Various separators: "2024-01-06", "2024/01/06", "2024.01.06" all mean the same date but may be treated differently by different systems.
Written-out months: "January 6, 2024", "Jan 6 2024", "6 Jan 2024" — all valid in human communication, all problematic in data systems.
How to Detect Format Inconsistencies
Profile your date column and look for multiple distinct patterns. If your date column has values that sort alphabetically rather than chronologically, dates are stored as text. If your min/max values seem implausible given your data, you likely have an ambiguous format being misinterpreted.
How to Standardize in Excel
If dates are stored as text: Use DATEVALUE() to convert text dates to Excel date values, then format as your target format.
If dates are in mixed formats: Use Text to Columns to split the date into components, then reassemble in your target format using DATE(year, month, day).
If the format is ambiguous: You may need to identify which records came from which source system and handle each group separately.
Target format recommendation: Use ISO 8601 (YYYY-MM-DD) as your standard. It sorts correctly alphabetically, is unambiguous, and is accepted by virtually every system.
Sohovi detects multiple date format patterns in your column and flags the inconsistency before you begin cleanup — saving you from discovering the problem after a calculation returns wrong results.
The rule for dates in any shared dataset: pick one format and enforce it from the first record. Standardizing after the fact is always more work than preventing the inconsistency.
