Fuzzy matching is the foundation stone of any data migration, data management or data optimization project. Its primary purpose is to identify duplicates within a list or data source that does not have an exact match – meaning, it catches details that an exact match may miss. Because it does not rely on exact structures to deliver results, fuzzy matching is a complex and cumbersome process that makes project costs balloon by hundreds of thousands of dollars when done via manual methods.
Why do businesses need fuzzy matching then? Quite simply, to make sense of their data. Businesses often struggle with poor quality, messy data that they cannot use unless they clean and optimize it. Part of the cleansing process involves identifying duplicate data that is the leading cause of data quality issues. It takes a team of experts, months of planning and multiple attempts at getting the right algorithm to identify improperly held data entries from large volumes of data, fix them, and standardize them – all this leads to companies’ ROI taking a hit.
Gartner, a leading market research firm estimates that about 40% of all business initiatives have lost their value because the data they had was either wrongly linked, mislabeled or just plain messy.
Nonetheless, there’s no avoiding the fact that whether you are looking to identify duplicate data entries before opting for a new CRM, or are building a Single Customer View for your enterprise for easy decision-making, you need to eliminate duplicates and link similar data first. To achieve this goal in time without spending millions of dollars, you need an automated fuzzy matching solution.
But before we discuss the automated solution, let’s take a look at how fuzzy matching works. And how can today’s firms take advantage of this business-critical feature to streamline their decision-making process for the better?
A Look at How Data Matching Works
Fuzzy matching makes use of two methods:
- Deterministic data matching
- Probabilistic data matching
Deterministic data matching
This method uses unique identifiers which are fields that hold constant data and gives a unique attribute to an individual. These are records that more or less remain the same over time i.e. a customer’s name, their email address or their phone number. You can basically do data matching by giving more weight to the customer name as a unique identifier, or you could use something else like phone numbers to accomplish the same. Run a search and voila, you can now see potential matches right in front of your screen.
Deterministic may sound great in theory, however, it is a tad inflexible in practical terms. This is because this approach assumes that all database entries are free of any mistakes. Since human beings enter data, you can find variations in customer name and even phone numbers.
- Variations in name – Salma B., Salma Birch, Selma Birch, etc.
- Variations in phone numbers – +144403242, 44403242, 00144403242, etc.
This approach doesn’t even take into account any typos.
Probabilistic data matching
As you can see in the above examples, data matching done via a deterministic method assumes that there are no mistakes in unique identifiers. With so many variations that inevitably get entered into a database, the deterministic method is not the right approach to determine matches in a database with quality issues.
Enter probabilistic data matching, which is another name for fuzzy matching. This approach is used by modern data quality software to account for everything from spelling mistakes, abbreviations, standardized codes, nicknames, different addresses, and more. All of this is done by using tried and tested algorithms with a matching rate of 85-96%.
Fuzzy matching works by employing various identifiers instead of a unique one. You can choose parameters and also configure how broad or narrow their scope can be. A broad approach will find more matches for you, however, this will also increase the chances of encountering false positives. False positives will need to be eliminated via manual review, but to be fair, the hard part has already been done with the help of fuzzy matching technology.
How can Businesses Benefit with Fuzzy Matching?
Did you know that 94% of businesses have stated that they suffer from duplicate data entries and that they remain undetected? These businesses stand to benefit by taking all your data sources, compiling a list of similar records, and consolidating them in one unique and error-free database.
This helps you and your employees get to the data you need faster, while at the same time make better strategic decisions that have a positive bearing on your business objectives, both in the short and long term. You get a singular source of truth for business analytics, while also integrating data from various sources in one place. Improved data quality means you don’t have to worry about inaccurate data entries ever again.
Thanks to modern data quality software, these concerns are now a thing of the past. They rely on multiple standards as well as proprietary algorithms to clean up data from databases by identifying missed keys, fuzzy, abbreviated, and phonetic entries across the board.