Data deduplication software

Find duplicate data records – even in the absence of unique identifiers and exact data values – by leveraging a combination of advanced probabilistic and deterministic algorithms, and identifying fuzzy, phonetic, mis-keyed, and abbreviated variants of data values.

data deduplication

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DEFINiTION

What is data deduplication?

Data deduplication removes duplicate items from databases and lists either by matching records manually or using data matching algorithms to automatically detect duplicates. The purpose of deleting duplicate rows/records is to clean the underlying data set to achieve productivity improvements, save on duplicate mailings, and increase customer satisfaction.

Manually deleting duplicates can be a time consuming and error prone task, which is why dedupe software is an essential tool for enterprise-wide data quality initiatives.

Benefits

Why do you need a data deduplication tool?

Identify different types of duplicates

Find and resolve different types of duplicates, including exact, non-exact, or varying values, stored within or across data sources.

Avoid losing data while deduping

Prevent data loss and ensure retention of the most accurate and comprehensive view of an entity after deduplication.

Perform scalable deduping

Use more advanced and scalable features for CRM deduplication than the ones built in CRMs like HubSpot or Salesforce.

Implement custom merge behavior

Take the guesswork out of data deduplication by configuring custom merge and survivorship rules according to your needs.

Compare and integrate backups and archives

Reduce the number of versions residing in your archives by merging important information to the latest data record.

Improve customer journey

Leverage personalized customer experiences by deduping customer data captured at different touchpoints.

Features

What DME’s data deduplication can do for you?

DME allows you to prepare your data before deduping it, which involves advanced data profiling , cleansing, and standardization. With DME, you can execute the necessary steps to ensure deduplication accuracy, such as pattern recognition, word replacement, letter case transformation, and address standardization.

DME leverages advanced field and record matching techniques that consider misspellings, human typographical errors, and conventional variations in data values. DME can assess similarity between records right down to the character level. Moreover, advanced fuzzy matching techniques are also used to compare words and long sentences.

DME runs powerful data matching algorithms and categorizes records in duplicate groups – all records in a duplicate group are similar to (or duplicate of) each other. Each duplicate record is also assigned a match score that gives insight into the level of match confidence computed for the match.
Manual review and selection of master record is quite a tedious task. This is why DME comes with an in-built ability to configure rules that automatically determine master record and its duplicates. For example, based on your dataset, you can configure the master record to be the one that has the longest first name, or the one that was most recently created, and so on.
DME can help you to retain important information from duplicate records, so that you do not lose data and preserve a complete and unique view of your database. By configuring conditional operations for merging and overwriting data values, you can get the most out of your data.

There’s more

What else do you get out of the box?

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

User roles

A tool made for everyone

Data analysts

Business users

IT Professionals

Novice users

Features

We take care of your complete DQM lifecycle

Import

Connect and integrate data from multiple disparate sources

Profiling

Automate data quality checks and get instant data profile reports

Cleansing

Standardize & transform datasets through various operations

Matching

Execute industry-grade data match algorithms on datasets

Deduplication

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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