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Identifying entries that *could* be fixed by fuzzy match

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Identifying entries that could benefit from fuzzy matching across multiple tables can enhance your data management process, particularly in stock datasets where variations like "META" and "META US" exist. While running a fuzzy match in Power Query is one approach, there may be more efficient methods to pinpoint potential discrepancies without executing the match itself. By developing a systematic comparison strategy, you can streamline the identification of similar entries, ensuring a thorough manual review later. Explore innovative solutions that empower your data accuracy and efficiency.

Is there any way to pull a list of entries that would, in fact, be fixed by a fuzzy match without relying on the fuzzy match itself (data still needs to be manually reviewed at the end of the day) . Best I can think of is running the fuzzy match itself in power query and then adding a column that checks if the result is the same as the original input data but I have to imagine there’s a better way to do it (and it’s a comparison between 8 separate tables which is my secondary question lol)

TLDR trying to find the best way to identify entries that are close, but not quite the same across multiple tables. We’re talking stocks, so META vs META US for Facebook as an example of the data inputs that should be pinged

submitted by /u/Codenamerondo1
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