For example, let’s take the case of hotels listing in New York as shown by Expedia and Priceline in the graphic below.
How does Fuzzy Matching help in real-world scenarios? There are many situations where Fuzzy Matching techniques can come in handy. Let’s look at some real-world examples of using Fuzzy Matching.
1) Creating a Single Customer View (SCV): A single customer view (SCV) refers to gathering all the data about customers and merging it into a single record.
For example: Assume that you manage a restaurant. You might have the following data sources/tables. Table1 contains information regarding your customers’ order history and Table2 contains information about their surfing patterns on the restaurant’s website.
A large organization is bound to have a multitude of such tables which they could join to obtain a single customer view. This often requires fuzzy string matching (in our scenario Tables 1 and 2 have to be joined using the Customer Name attribute).
The restaurant could leverage the SCV information as follows:
When a regular patron calls to place an order, the restaurant could suggest a new dish based on his/her order history and the recipes that he/she has surfed. The restaurant could also send customized content (new exciting recipes) to their customers using the SCV information.
2) Data Accuracy:
According to a recent study, over 60% of companies have implemented solutions based on Machine Learning. As companies rely on artificial intelligence and machine learning, data accuracy becomes extremely crucial.
Groundbreaking research is often carried out on improving the accuracy of neural networks and Machine learning technologies, however, little is being done to ensure that good quality data is fed to these models. A great machine learning algorithm without accurate data is analogous to launching a rocket to mars using compressed natural gas.
Fuzzy string matching can help improve data quality and accuracy by data deduplication, identification of false-positives etc.
3) Fraud Detection:
A good fuzzy string matching algorithm can help in detecting fraud within an organization. Later in this post, we’ll see how the FAA used fuzzy string matching to single out several pilots for exhibiting fraudulent behaviour. Looking to extract information using Fuzzy Matching? Head over to Nanonets to use the solution that offers advanced Fuzzy Matching!
Interesting Fuzzy Matching Use Cases
1) Spelling Corrector
Nowadays, most of us don’t even bother to find out the correct spelling while writing an email, such is the trust we place in modern spelling correctors.
In the following paragraphs, I will attempt to describe the basic plumbing of a simple spelling checker.