How many different ways could you write “Philadelphia Indemnity Insurance Company?”
Here are just a few, off the top of our heads:
Philadelphia Insurance Co
Phil Ind Insurance
You can probably think up at least 10 more in no time at all. Then, for each of those variants, imagine all the different misspellings and typos you could end up with!
Data entry is a highly error-prone process, and it’s not hard to imagine “Philadelphia” coming out as “Philidelphia” or “Philedelphia”, or “Indemnity” as “Indemity”.
When we deduplicated 150 million insurance policy records, we found more than 800 unique (case-insensitive) spellings of this highly respected insurance company’s name. Each spelling came from a different input system.
Duplicate records can harm your business in a number of ways. They can skew your reporting and analytics. They also take up unnecessary storage and network bandwidth, and they can slow down your migrations. In other words, they can cost you time and money, reduce efficiency, and frustrate your employees. They’re all pain and no gain.
Deduplicating data may sound like a headache, but RecordLinker’s Machine Learning-powered solution makes it easy and manageable.
Once our system has trained on your data, it will learn to reliably identify and weed out duplicate entries. You can steer it and keep tabs on its work using our intuitive user interface