Codeless Fuzzy Data Matching

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Farah Kim • March,2023

Fuzzy Matching: Your ultimate solution for data cleansing!

What is Codeless or No-Code data matching & How Does it Help with Data Transformation?

Fuzzy data matching has always been a technical expertise requiring programming expertise and an in-depth knowledge of data matching, which only trained data scientists can possess. However, with the revolution of no-code solution, there are now codeless fuzzy data matching solutions that can help companies merge vast amounts of data within seconds. 

 

Now if you’re a programmer or a data scientist reading this, you’re probably scoffing at this and may even be skeptical!

 

But hear me out.

 

Data prep, data cleansing, data standardization, and data matching are mundane tasks that don’t require 100% of your time. It’s like driving a car. You, the driver needs to focus on the road and the destination. You’re not required to focus on the engine or the inner workings of the car. Similarly, your job as a data specialist is not to get lost in the mundane job of data cleansing. You are needed for strategy, for ensuring optimal outcomes, and for getting the job done on time.

 

NO CODE FUZZY MATCHING

 

No code fuzzy data matching can help:

 

>> Improve organizational efficiency by up to 60%

>> Reduce development cost up to 80%

>> Reduce dependencies & enables faster outcomes

>> Focus on strategic efforts & keeping up with business demands

 

The time you spent in manually fixing errors on Excel or other programs will likely lead to more errors and delayed project timelines. Using a no-code fuzzy matching solution, you can quickly match data from multiple sources, deduplicate records, and create a master record fit-for-purpose

 

Learn More About WinPure Fuzzy Matching Tool

 

 

Instead, fuzzy matching techniques or probabilistic data matching applies parameters that you choose, scoring data patterns mathematically. Then, fuzzy matching techniques compare sets of characters, numbers, strings, or other data types for similarities. When presented with the likelihood, that customer entities match your fuzzy matching search; you decide whether to link records and combine data into a single customer view.

 

As long as you know your goals and your requirements are straightforward, you can speed up your record linkage, data treatment, master data management and deduplication efforts without human and legacy system errors being a hindrance.

How Does No-Code Fuzzy Matching Work?

A codeless fuzzy matching platform works by combining common fuzzy matching algorithms along with their proprietary algorithm (WinPure, for example, has its own algorithm that works in tandem with other fuzzy algorithms). It can detect matches with a higher level of accuracy than traditional methods. Moreover, these platforms use visual interfaces to enable non-technical and business users to treat, merge, and consolidate data through simple drag and drop or point and click actions. Users don’t need to know statistics, programming languages, or have any previous coding experience to use the platform.

 

In addition to the fuzzy matching, codeless platforms also enable you to perform data cleansing which would otherwise be a tedious process when done manually.

 

vodafone case study fuzzy matching

 

See how companies in your vertical are using fuzzy matching today.

 

Codeless fuzzy matching vendors compete based on speed and accuracy. You should be able to get match results for a million records in just about minutes!

 

Some no-code fuzzy matching solutions like WinPure also let you create custom expressions for specific data matching requirements allowing for flexibility and scalability.

Pros and Cons of Codeless Data Matching

Like all things, there are some pros and cons to be aware of. Here’s a compiled list of pros and cons with codeless data management platforms.

ProsCons
Gives you more time to focus on strategy Straightforward requirements & should know what you want
Empowers business users to clean, merge, and dedupe data within their domainNeeds user management and defined roles to make sure only designated users can make changes
Improves organizational efficiency by up to 60%Security issues can occur if there is a lack of control
Cost-efficient, computationally inexpensive, and easy to deploy (on/off premises or cloud)License costs may differ according to requirements
Scalable and flexibleRequires increased hardware performance if there are millions of data sources to match
Performs data preparation & transformation along with data matchingLimitations in complex transformation and matching requirements

It’s important that you know a no-code solution is not a replacement to a data scientist, analyst, or engineer. It is meant to assist, just like most AI applications assist human talents to perform, deliver, and achieve business goals faster and better.  A tool is only as good as the person using it!

What Features to Expect from WinPure’s No-Code Fuzzy Matching Solution

WinPure has been in the data management business for nearly two decades, being the first to offer no-code data matching for businesses of all sizes.

 

Over the years, we’ve gathered much intelligence on the struggles and limitations professionals as well as businesses face with record linkage and data deduplication – from failed master data initiatives to delayed mergers and acquisitions, we’ve seen it all.

 

WinPure’s fuzzy matching solution attempts to reduce and eliminate these struggles so businesses can keep up with the pace of a data-driven world.

Some of WinPure’s key data matching features include:

 

  1. Data Profiling Function: It’s an established fact that you cannot perform a fuzzy match operation with messy data. WinPure kickstarts the matching process by first profiling your data. It lets you see the ‘health’ of your data and what areas need urgent attention.
  2. Data Cleaning Function: Simply select columns or rows you want to normalize/standardize/clean. You can add in specific abbreviations in the WordSmith panel (a dictionary) to keep your original entry. WinPure also uses a Global Parsing Engine (GPE) to parse, standardize, verify, cleanse and format address data.
  3. Data Deduplication Function: Duplicates destroys the credibility of your data and can lead to disastrous real-world consequences. WinPure’s Clean and Match solution uses a combination of fuzzy matching algorithms to let you deduplicate data efficiently.
  4. Merge Data Sources for Final Records: After you’ve cleaned and transformed records, you can finally merge the data to create master records.
  5. Complete Matching Report: A comprehensive, visual report depicting the number of duplicate records found and how many were treated.

Additional features include:

  1. Ability to write regular expressions to meet custom requirements
  2. Ability to integrate easily with CRMs, databases, and more
  3. Ability to automate cleaning and matching schedules

As a trusted innovator in data cleaning and data matching, WinPure’s no-code solution has helped thousands of businesses worldwide save millions of dollars in expensive talent recruitment and in manpower hours.

Case Study – Centura Health Used WinPure’s No-Code Data Match Solution to Create a Single View

The health industry relies on accurate data to offer the best care to its patients. Centura Health, a renowned healthcare facility in the US needed to create a single view by identifying all donors who engage with their company and to also identify all the people who value the organization. With over  6,000 physicians and more than 21,000 donors, the facility needed a strong data matching solution to merge records and create a 360-view.

 

WinPure’s Clean and Match was used to link disparate data sources, dedupe data, and create single view records through an efficient data matching process – all without a single line of code!

Read more here. 

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Farah Kim

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Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, data deduplication, and MDM.

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