From finding out the exact usage of a word to identifying a particular record, precise name matching is an essential requirement today. Whether it’s about scouring through real estate records or tracking an offender in a criminal database, fuzzy name matching proves an exceptionally reliable solution.
But does fuzzy matching ensure easy workflows in the real world?
Let’s find out!
- Single Customer View (SCV)
The single customer view is an assortment of all the data about customers and accumulates it under a unified record. With the growth of an organization, it becomes tricky to keep track of all the information, especially when there’s cope for redundancy.
For instance, a restaurant can have a record of the most ordered meals by a customer and information about the average time spent on their page. By combining these two details about the same individual, the overall efficiency and productivity of the organization enhance.
In addition, the restaurant can leverage such information for better campaign generation, including the revised target audience persona.
- Data Precision and Accuracy
Data duplicity and redundancy are not only a concern for the extra labor used but also lead to costly accidents in the future. The entire basis of insight-based decision-making lies in the precision of the entered details in a dataset. However, with duplicate records, the authority of the overall database is reduced.
With fuzzy matching, you can get rid of repeated information. It ensures the presence of only high-quality data. The most groundbreaking methods used to remove errors in a database include data deduplication and false-positive identification.
- Fraud Identification
Fuzzy matching ensures quicker detection of fraud in an organization. It can cross corroborate to make sure that all the benefits issued reach the deserving candidates.
Fuzzy clustering enables organizations to segment and analyze their financial statements to detect monetary leakages and fraud.
- Segmented Classification
Exact matching is an extensive database such as a genome sequences repository that involves precision and leaves no room for error. With fuzzy name matching, experts can easily match the query sequence of different genomes with potential candidates.
Post-matching, these genomes can be further categorized depending upon their classification types. What potentially takes hours, if done manually, can be executed within minutes with the suitable fuzzy matching algorithm.
Benefits of Fuzzy Matching
Fuzzy matching, like the name suggests, is an extremely beneficial tool to get the congruence right. But there’s more to this approach than verification, such as:
- Finding matches across different databases with variation detection
- Flexibility with matching threshold to meet businesses requirements
- Easy matching despite the unavailability of unique identifiers through a statistical analysis approach
- Rectification of slight errors to ensure accuracy across the datasets
- Drastically reduces time and efforts required for efficiency
With the insurmountable increase in data across all domains, it’s impossible to read and analyze every entry manually. In addition, the identification of different records and details in various databases through manual methods is a waste of time, effort and money simultaneously.
Thankfully, fuzzy name matching allows easy identification in different databases of a range of records as per given instructions. No matter the diversity in diacritical marks in names or the use of suffixes, name-matching scripts catch the similarity to streamline workflow.
Now that you know all about fuzzy matching, where do you plan to employ the function?
