data clean up


We love to say “garbage in, garbage out” when it comes to data management. In an ideal world, you’d be able to guarantee data harmony from the outset by keeping your inputs clean, standardized, and free of errors. But in the real world, that’s much easier said than done.

If your data comes from legacy systems, or from free text inputs written by clients or customers themselves, you simply can’t control the consistency or accuracy of what’s flowing into your database. Garbage, including misspelled names and wrongly formatted dates, is going to find its way in–but that doesn’t mean it has to stay garbage.

RecordLinker’s machine learning-based solution is designed to help you clean up crappy data–no matter how many different sources it’s coming from. You don’t have to know in advance which data is erroneous: our system will flag any data entries that don’t match your canonical record list, then clean them up once you’ve reviewed them.

We also make it easy for your data team to share your canonical record list with the people responsible for your source systems, so they can start working in line with your standards.

The right set of tools can turn data clean-up from a massive burden into an (almost) no brainer. Get in touch with us to learn more about how we can help you achieve clean, reliable data in just a few simple steps.

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