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How to Use No-Code Fuzzy Matching to Remove Duplicates in Your Database

In this article, we will learn how to use no-code fuzzy matching to remove duplicates in your database? By IncludeHelp Last updated : June 08, 2023

Duplicate data is a common occurrence in databases. It causes the data to be inaccurate and unreliable for business use.

To overcome the challenges of duplicate data, fuzzy data matching is used to identify and remove duplicates that are hard to catch due to their non-exact nature.

For example, if you're using Excel, you can easily identify James Smith of New York is a duplicate of James Smith of Washington, if you've set First Name/Last Names as the match rule.

However, if the first entry is Jamie Smith or J.Smith, you might not be able to catch this duplicate. For instances like this, you would need fuzzy data matching.

Fuzzy matching uses algorithms to compare data values and identify records that are likely to be duplicates.

No-code fuzzy matching is a type of fuzzy matching that does not require any coding knowledge. So, let's think of this as a simple guide to fuzzy matching, which helps you understand how to use this tool better. It is a good option for businesses that do not have the resources to hire a developer to implement a fuzzy matching solution.

In this article, we will discuss how to use no-code fuzzy matching to remove duplicate data from your database. We will cover the following topics:

• What is fuzzy matching?
• Why is it important to remove duplicates from your database?
• How does no-code fuzzy matching work?
• How to use no-code fuzzy matching to remove duplicates from your database

Let's roll.

Why is it important to remove duplicates from your database?

Duplicate data can cause a number of problems for businesses, including:

• Inaccuracy: Duplicate data can make it difficult to get accurate reports and analyses. For example, if you have two records for the same customer with different contact information, you may not be able to accurately track their purchase history or send them marketing materials.
• Wasted resources: Duplicate data can lead to wasted resources, such as time and money. For example, if you send a marketing email to a customer who has already been contacted, you are wasting your time and money.
• Compliance issues: Duplicate data can lead to compliance issues, such as fines and penalties. For example, if you have two records for the same customer with different tax information, you may be in violation of tax laws.

If you have duplicate data in your database, it is important to remove it as soon as possible. There are a number of tools and techniques that can be used to remove duplicates from databases. The best tool or technique for you will depend on the size of your database and the specific needs of your business.

What is fuzzy matching?

Fuzzy matching is a technique that compares data values and identifies records that are likely to be duplicates. Fuzzy matching uses data matching algorithms such as:

• Levenshtein distance
• Jaro-Winkler distance
• Soundex

And many others to identify duplicate data based on their attributes.

For example, the Levenshtein distance algorithm measures the number of edits required to change one string into another. The Jaro-Winkler distance algorithm is similar to the Levenshtein distance algorithm, but it takes into account the frequency of common characters. The Soundex algorithm converts strings into phonetic codes, which makes it easier to compare strings that have different spellings but the same pronunciation.

Here are some examples of how fuzzy matching can be used:

• A marketing company could use fuzzy matching to identify duplicate leads. This would help the company to avoid wasting resources on leads that have already been contacted.
• A customer service company could use fuzzy matching to identify duplicate customer accounts. This would help the company to provide better customer service by ensuring that customers only have to provide their information once.
• A financial institution could use fuzzy matching to identify duplicate transactions. This would help the institution to detect fraudulent activity.

Fuzzy matching is a powerful tool that can be used to improve the accuracy and efficiency of your business. If you have duplicate data in your database, fuzzy matching is a good option for you to consider.

How does no-code fuzzy matching work?

No-code fuzzy matching does all of the above without requiring the user to write any line of code. Generally, to run a fuzzy match on a dataset, data engineers have to spend hours finding, testing, and tweaking fuzzy matching algorithms to get the right results.

No-code data matching is done using a fuzzy data matching tool that allows users to match complex data without having to use scripts or codes. They simply need to plug in the data, use a point-and-click interface to match data. This leaves them plenty of time to strategize, understand the data and spend more time fixing inconsistencies.

No-code fuzzy matching tools typically offer a variety of features, such as:

• The ability to import data from a variety of sources
• The ability to specify the fields that will be used for matching
• The ability to set the level of similarity required
• The ability to choose the type of fuzzy matching algorithm that will be used
• The ability to review the results of the fuzzy matching process
• The ability to remove duplicates manually or automatically

No-code fuzzy matching is a powerful tool that can be used to improve the accuracy and efficiency of your business.

How to Get Started with No-Code Fuzzy Matching?

Get started with codeless fuzzy data matching by following simple steps:

1. Choose a no-code fuzzy matching tool. There are a number of no-code fuzzy matching tools available on the market. When choosing a tool, consider the size of your database, the types of data you need to match, and your budget.
2. Import your data into the tool. Once you have chosen a tool, you will need to import your data into it. This can be done in a number of ways, such as by uploading a CSV file or by connecting to your database.
3. Define the rules for matching. The next step is to define the rules for matching. This includes specifying the fields that you want to match, the level of similarity that you require, and the type of matching that you want to perform.
4. Run the matching process. Once you have defined the rules for matching, you can run the matching process. This will identify the duplicate records in your database.
5. Review the results. Once the matching process is complete, you will need to review the results. This includes verifying that the correct records have been matched and that no false positives have been identified.
6. Take action. Once you have reviewed the results, you can take action to remove the duplicate records from your database. This can be done manually or automatically.

No-code fuzzy matching is a powerful tool that can be used to remove duplicates from your database. It is a simple and effective way to improve the accuracy and efficiency of your data operations.

Also Read: What is Fuzzy Logic in AI and Why It is used?