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.
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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.
There’s more
What else do you get out of the box?
- Live preview of deduplicated data
- Unique and duplicate record selection
- Character and token-based similarity checks
- Phonetic and numeric similarity detection
- Scheduler for automatic data deduplication
- Fine tuning deduplication algorithm
- Merging and overwriting records
- Multi-format support for exporting results
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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Address standardization guide: What, why, and how?
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While surveying 2,190 global senior executives, only 35% claimed that they trust their organization’s data and analytics. As data usage surges across various business functions,
Address standardization guide: What, why, and how?
Inaccurate and incomplete address data can cause your mail deliveries to be returned. In fact, the US postal service handled 6.5 billion pieces of UAA
What is data integrity and how can you maintain it?
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