Comparing company names from different sources can be a daunting task, especially when dealing with variations in formatting and spelling. I’ve been there too, trying to find a way to match company names that are ‘very’ different. My initial approach was to use a combination of substring, upper case, and trim functions to check the first few characters. But let’s be honest, it was a crap solution.
My next step was to create a function to standardize the names, using a table of find and replace values. For example, replacing ‘ltd’ with ‘limited’ and ‘corp’ with ‘corporation’. But even that felt inelegant.
That’s when I realized I wasn’t alone in this struggle. There must be a better way to achieve a string near match. After all, it’s a common problem many of us face when working with company data.
The kind of data I’m working with includes variations like ‘Moscow (City Of)’ and ‘CITY OF MOSCOW’, ‘Sika AG’ and ‘SIKA’, or ‘ANZ New Zealand (Int’l) Limited’ and ‘ANZ NATIONAL(INTL)’. It’s clear that a simple substring match won’t cut it.
So, what’s the solution? Well, I’m hoping someone out there has tackled this issue before and can share a better approach. Perhaps there’s a clever algorithm or a tried-and-tested method that doesn’t involve a ton of replaces.
If you have any advice or insights, I’d love to hear them. Let’s work together to find a more elegant solution to this common problem.