Match non-exact data records with advanced fuzzy data matching

Still using spreadsheets to manually match data? It can only get you as far. Compare multiple data sources, set match scores, create custom match logic and much more with WinPure’s fuzzy data match tool.

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Fuzzy data matching tool powered by proprietary data match algorithm

Streamline your customer data by consolidating duplicates across marketing lists, databases, spreadsheets, and CRMs. WinPure’s proprietary fuzzy match algorithm, boasting a 97% accuracy, ensures you reach the right customers with the right information. Whether it’s to match name lists, phone numbers, email addresses, or physical addresses, WinPure’s fuzzy match tool can do it all.

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Key fuzzy data match features unique to WinPure

Connects to all popular databases, CRMs, file formats.

WinPure has exceptional data access, effortlessly connecting to and importing information from diverse sources – ranging from files like Text/CSV, Excel, and XML, to a wide spectrum of databases including SQL Server, Oracle, MySQL, MS Access, and cloud platforms like MS Azure & CRM systems like Salesforce and Zoho.

WinPure data integration
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Set custom match logic and fuzzy thresholds

WinPure goes beyond simple duplicate removal. For those with specific data match requirements, WinPure lets you set custom match logic and thresholds. This means you can define exactly at what level similar records need to be to be considered duplicates. Easily tailor the matching process to your data, whether it’s email addresses, names with typos, or variations in addresses.

Create custom dictionaries with a Word Manager

With a custom Word Manager, users can define and manage a list of synonyms, abbreviations, and business-specific terminology that ensures consistent recognition and treatment of similar terms during the data matching phase. For instance, a user could specify that “Corp.” and “Corporation” should be treated as equivalent, thereby reducing errors and inconsistencies in matching results.

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WinPure data matching

User-friendly visuals with detailed match scores

A standout feature of WinPure’s fuzzy data match tool is its user-friendly visuals combined with advanced reporting capabilities, including metrics such as match rates, potential duplicates, and confidence levels, providing a clear and quantifiable overview of the matching quality. WinPure’s fuzzy data matching software not only simplifies the task of managing data quality but also provides powerful tools for data-driven decision-making

How customers use our fuzzy match tool
(and the results they achieved!)

  • Tackle complex duplicates – that spreadsheets and CRMs do not have the capabilities for. Customers have reduced duplicate data by nearly 80% with WinPure’s fuzzy match
  • Cross-platform matching – allows users to match between or across different data sets to obtain key insights
  • Match on anything – not just name and address, but email, phone number, date of birth, account number etc.
  • Match bought-in data to make sure you don’t send existing customers offers intended for new customers.
  • Better GDPR compliance – some customers use WinPure to improve GDPR with address data matching

Explore fuzzy matching for free

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Serving clients across 50+ countries

We’ve helped businesses & government organizations across the US, UK, AUS, CAD, and other countries improve their data quality.

4000+

SME businesses served

97%

data matching accuracy

95%

Customer retention rate

Real-world results
enabling data success across industries​

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Marketing

Consolidate customer records with marketing data

Customer data in marketing is one of the most problematic in terms of consistency and completeness. Because the data is collected via multiple sources, it is impossible to have clean data. WinPure can be used by marketing teams and agencies to treat, transform, and consolidate customer data, with the end goal of achieving holistic customer records.

Marketing Case Studies

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Healthcare

Obtain insights from patient data

The healthcare sector is burdened by the sensitivity and criticality of patient data. Mistakes in identity data, duplicate records, or even human errors can cause cases of misdiagnosis, flawed reports, & difficulties for the patient and their families. WinPure has worked with companies like GE Healthcare & Centura Health to clean and deduplicate data while maintaining accuracy, data sensitivity and integrity.

Healthcare Case Studies

14

Local Goverments

Improve public benefits with government data

WinPure specializes in enabling local and global government bodies with identity resolution, cross-platform data match, and consolidation, as well as creating clean and accurate data. We have worked with delegated civil bodies across the US and UK, helping them with optimizing the quality of public data in a secure, on-premises environment. See how we helped local governments with data quality management.

Government Case Studies

15

Sports

Amplify performance with sports data

Sports teams are now relying on quality data to get granular insights into athletes’ and coaches’ performance. Additionally, it also allows them to create innovative fan engagement strategies fueling deeper analysis and discussions. WinPure has worked with sports associations like the English Football League, West Ham United, and more on data cleaning and deduplication goals.

Sports Case Studies

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Media

Better compliance for manufacturing industries

Manufacturing companies wrestle with vast amounts of data quality issues. WinPure offers a data management solution that tackles duplicates, corrects errors, and ensures the accuracy of compliance lists, vendor and partner information. It streamlines data from various sources, creating a unified view for better decision-making. See how a similar an organization in the manufacturing industry benefited from the WinPure.

Manufacturing Case Studies

Recommended by industry leaders
Rated by leading platforms

Eric Branson
Eric Branson
Founder of Highr

Definitely recommend WinPure for anyone dealing with large quantities of data. The fuzzy matching is really intuitive and after a bit of testing with the settings it ends up being able to remove dupes better than anything else I've ever tried.

alexander
Alexander Goldenberg
Director of Information Technology and Operations

Using WinPure shaves hours off when comparing data from different sources. Its very fast and the results are brilliant. The product support and ease of use are great. The support team is very knowledgeable and easy to connect with.

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Edward B.
Company Owner

We perform multiple matching projects for our clients and WinPure has filled the bill for these. The product is very easy to use, incredibly fast and we can complete a large matches in a very short time.

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Richard F.
Company Owner

WinPure is a really great product, we've been using it with excellent results for many years now, for finding and removing duplicate records and to keep our lists and database more accurate.

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Cynthia T.
Director of Information Technology

WinPure Clean & Match Enterprise works really great to analyze data and find duplicated customer records. It saves us tons of money when mailing catalogs. This is a great product for the money and easy to use.

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Naveed B.
IT Consultant

A very powerful but easy to use tool for cleansing and removing duplicates from databases. I have used Clean & Match for many of my clients, and I am regularly recommending this product to other companies.

Download the 30-Day Free Trial

and improve your data quality with no-code:

  • Data Profiling
  • Data Cleansing & Standardization
  • Data Matching
  • Data Deduplication
  • AI Entity Resolution
  • Address Verification

…. and much more!

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Fuzzy data match tool FAQs

What is fuzzy data matching, and how does it work?

Fuzzy data matching is a process that finds non-exact matches across data sets by using algorithms that can identify similarities and patterns, such as phonetic comparisons and typo recognition.

What are the benefits of using fuzzy data matching?

Fuzzy data matching improves data quality by accurately linking similar entries that are not exact matches, reducing duplicates and enriching data sets, which facilitates more reliable analytics and business intelligence.​

How does fuzzy data matching differ from exact matching?

Unlike exact matching, which requires records to match perfectly, fuzzy data matching tolerates minor discrepancies like spelling errors or format variations, making it more flexible and effective in real-world data environments.

How does fuzzy data matching handle data inconsistencies and errors?

Fuzzy data matching uses sophisticated algorithms to detect and reconcile inconsistencies and errors such as typos, abbreviations, and different naming conventions, ensuring more accurate data linkage despite imperfections.​

Is fuzzy data matching suitable for all types of data?

Fuzzy data matching is versatile and can be applied to various types of data, including customer records, financial data, and inventory lists, but it is most effective when customized to the specific characteristics and requirements of the dataset.

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