Data matching software

Execute proprietary and industry-grade match algorithms – based on custom-defined criteria and match confidence levels – for exact, fuzzy, numeric, or phonetic matching, and visually deduplicate or merge records belonging to the same entity.

data matching

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What is data matching?

Data matching is the process of comparing data values and calculating the degree to which they are similar. This process is helpful in eliminating record duplicates that usually form over time, especially in databases that do not contain unique identifiers or appropriate primary and foreign keys.

In such cases, a combination of non-unique attributes (such as last name, company name, or street address) is used to match data and find the probability of two records being similar.


Why do you need a data matching tool?

Execute custom data matching

Weigh in the nature of your data and choose the right matching fields, algorithms and confidence levels to attain the best match results.

Reduce computational complexity

Eliminate duplicate records present in databases and free up storage space to attain quick and timely query results.


Increase operational efficiency

Reduce manual labor, level up data quality, and optimize business processes with automatic data matching technology.

Facilitate any use case

Whether you want to clean mailing lists, detect fraudulent behavior, or match patient records, data matching software can help you out.

Ensure data compliance

Ensure that the records in your databases follow data compliance standards, such as GDPR, HIPAA, CCPA, etc.

Enrich data for deeper insights

Efficiently match organizational data present at different data stores and determine the next best move for your business.


What DME’s data matching can do for you?

data matching
Use DME to intelligently map data fields and reduce the hassle of manually assessing and renaming fields across disparate sources. DME achieves this by creating word clouds of all values in a field, and mapping the ones having the maximum number of common values.
In DME, you can create multiple match definitions, and each definition can hold multiple criteria. This structure helps you create various logical AND/OR expressions, based on which the data records can be matched. Furthermore, you can assign custom weights to matching fields to ensure prioritized calculation of match scores.

DME uses the correct algorithms, combining established and hybrid, depending on the nature of your data. You can, of course, fine-tune the setting to emphasize certain types of data matching, for example, exact, fuzzy , numeric, phonetic, or domain-specific matching.

DME outputs the match results in terms of scores that indicate the level of match confidence. DME calculates the match score as a numeric value in the range of 0 – 100%, and allows you to set the deciding level that classifies records as a successful match or non-match.
Rerun match algorithms with varying threshold levels and choose the deciding value that ensures least number of false positive and negatives. Moreover, you can also flag records as duplicates or non-duplicates and correct any misclassified record.

DME allows you to utilize the match results for subsequent steps of deduping , merging, and purging data records. You can also use the match results and scores to create survivorship rules or perform advanced data analysis – for example, merging records that show a high level of match confidence, or identify households where records have the same (or similar) residential address.

There’s more

What else do you get out of the box?

Our data matching solution comes with a number of in-built features that facilitate easy, automatic, and cost-effective data matching operations at any time.

User roles

A tool made for everyone

Data analysts

Business users

IT Professionals

Novice users


We take care of your complete DQM lifecycle


Connect and integrate data from multiple disparate sources


Automate data quality checks and get instant data profile reports


Standardize & transform datasets through various operations


Execute industry-grade data match algorithms on datasets


Eliminate duplicate values and records to preserve uniqueness

Merge & purge

Configure merge and survivorship rules to get the most out of data

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions

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