Data Cleansing MS 03

One of the key reasons businesses struggle with data quality objectives is simply due to the fact that they still rely on traditional methods like coding and programming to clean, dedupe, and consolidate data. Python-trained specialists would take months to compile, test, tweak, and refine fuzzy data match algorithms to get customer 360 views and build golden records. This is both time-consuming and expensive; considering that most businesses do not have the resources to hire trained specialists.

That’s where a no-code (or also codeless) fuzzy data match solution can facilitate IT managers or business users with data ownership to clean, deduplicate, and consolidate their records without having to impulsively hire expensive specialists – or – outsourcing the project, risking data privacy and data breach amongst many others.

Fuzzy data matching tools like WinPure are designed on the principles of a data quality framework, allowing businesses the ability to clean, deduplicate, match, and consolidate large data sets within minutes. No coding knowledge or extensive training is required!

In this guide, we’ll look at how no-code/codeless fuzzy data match works, the pros and cons, and how it benefits businesses. We’ll also show you how WinPure, the first true no-code fuzzy match solution works.

WHAT IS CODELESS FUZZY MATCHING?

If you’re a pro-coding data analyst reading this, you’re probably skeptical of no-code. But before you dismiss the case, hear me out.

A no-code fuzzy match does not take away the expertise of a Python programmer or developer. Instead, it helps you do your job faster, better, and with more accurate results.

How?

By taking away the tedious, mundane, and repetitive aspects of the job. For example, just standardizing all the abbreviations for countries and cities can take up a significant chunk of your time. You need to build a reference dataset, standardize the data, extract a fuzzy match algorithm, fine-tune it to meet your data requirements then match the now transformed data. This seemingly simple process can take weeks.

In essence, codeless fuzzy matching allows users to match within and across datasets, build custom match configurations and word libraries, merge and purge data, build REGEX expressions without requiring coding knowledge or expertise. It is designed for IT managers, database managers, data analysts, and engineers who want to clean & match large datasets quickly, efficiently, and without the need to code. 

“Isn’t this taking away my job?”

No.

A codeless fuzzy match doesn’t take away your job. Instead, it leaves you with more time to do more important things – like developing processes or analyzing the data on a deeper level for business growth. You get to become more strategic with the time you save on doing mundane, repetitive tasks!

Most importantly, you become efficient and play a critical role in solving business problems with readily available, treated and consolidated data. Your business teams won’t have to wait for transformed data to run a report. With no code, you can send them the final master records within a day.

As long as you know your goals and the scope of the project, you can speed up your record linkage, data treatment, master data management, and deduplication efforts requiring no additional resources.

No conflicts. No delays.

Increased organizational efficiency. Increased accuracy.

What’s not to love!

NO CODE FUZZY MATCHING


HOW
 DOES NO-CODE FUZZY MATCHING WORK?

Behind a no-code fuzzy matching platform is a complex proprietary code built on Python and JSON language. The platform works by comparing two strings of text and producing a similarity score – based on factors like phonetic similarity or edit distance (a fuzzy match logic that measures the difference between two strings and assigns it a numeric value). 

To use no-code fuzzy matching, you typically upload your data to an on-premise platform which then uses a variety of fuzzy match algorithms to match the data. It then provides you with a list of potential matches clustered into a group ID. You can then review the matches and select the ones that are correct and turn that into a master record.

Here is a simplified example of how no-code fuzzy matching works:

  1. Integrates data from different sources
  2. Allows for data profiling and cleaning (only available in best-in-class tools)
  3. Allows for advanced data cleaning and data preparation
  4. Matches the dataset based on the columns selected
  5. Compares the data using fuzzy match algorithms like Levenshtein or Jaro Wrinkler
  6. Provides you with a list of potential matches

All of this without the need to code!

No-code fuzzy matching can be used for a variety of tasks, including:

  • Deduplicating data: Fuzzy matching can be used to identify and remove duplicate records from a database.
  • Merging data: Fuzzy matching can be used to merge data from multiple sources into a single dataset.
  • Enriching data: Fuzzy matching can be used to add additional information to a dataset, such as customer contact information or product descriptions.

These are mundane, repetitive tasks that take hours to do manually. A business user can spend up to 120 hours on cleaning and standardizing 10,000 records – that’s almost a month’s work!

Save yourself the hassle. Use a no-code tool and simplify the process.

PROS AND CONS OF CODELESS DATA MATCHING

Like all tech tools, there are some pros and cons to be aware of.

Here’s what you need to know before using a codeless solution:

Pros Cons
Easy-to-use with a friendly UI. Maybe limited based on number of users/license.
Empowers business users to clean, merge, and dedupe data without coding knowledge Users must have knowledge of data management and data matching to get the best from the software.
Improves data match efficiency by 60% compared to traditional methods Limited customization as there is no access to platform code, which may demand an alternative approach or use of third-party connectors.
Improves match accuracy by 96% Users must build a knowledge library to ensure accuracy of results
Scalable and flexible Requires increased hardware performance if there are over a million records
Performs data preparation & transformation along with data matching Limitations may occur if the pre-defined algorithms are not a match for the data’s specific variation.

A no-code solution does not replace the skills of a data scientist, data analyst, or data engineer. It simply helps them do the job faster, with better and more accurate results.

WINPURE’S BEST-IN-CLASS NO CODE FUZZY MATCH 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.

Watch this video to see how easily you can match data using WinPure’s fuzzy data match capabilities.

Some of WinPure’s key codeless data-matching features include:

Data Profiling: WinPure kickstarts the matching process by profiling your data. You can see the ‘health’ of your data and what percentage of your data fields have incomplete data, or how many have odd and printable characters in the fields.

Data Cleaning Function: You get a powerful data cleaning suite that allows you to transform the data in one batch. For example, you can choose to remove all odd characters from all cells with just one click. Additionally, with a WordSmith panel (a dictionary) you can save a list of words you don’t want to include in the cleaning and matching.

Data Deduplication: With the option of fuzzy, exact, and numeric data match, you can perform advanced data deduplication on multiple data sources. You can deduplicate within the same data set, or across data sheets.

Merge Data Sources for Final Records: When satisfied with the match results, simply select your complete and transformed records to save them as a master record. You can merge multiple records into a consolidated record by a simple selection.

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 manpower hours.

Our goal is to facilitate tech users and business users to take charge of their data without having to outsource them to third parties. We make it easy so you can have control over your data and your challenges. The software is intuitive and easy-to-use, requiring minimal training, enabling even a junior resource to own the data clean and match process. You don’t need to hire an expensive data specialist or developer to handle your organizational data. That’s the power of a true no-code solution!

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. 

Written by Farah Kim

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, entity resolution and Master Data Management.

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