Data cleansing software

Our data cleansing tool is feature-rich solution that helps you to eliminate inconsistent and invalid values, create and validate patterns, and achieve a standardized view across all data sources, ensuring high data quality, accuracy, and usability.

data cleansing & standardization

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What is a data cleansing tool?

A data cleansing tool is a solution that helps eliminate incorrect and invalid information from a dataset, and achieve a consistent and usable view across all data sources. Some common data cleansing and standardization activities include:

  • Removing and replacing empty or garbage values,
  • Parsing aggregated columns to identify smaller sub-components,
  • Transforming letter cases,
  • Merging the same or similar columns together to avoid duplicates,
  • Transforming values to follow the correct pattern and format,
  • Flagging, replacing, or eliminating the most repetitive words in a column to remove noise in bulk.


Why do you need a data cleansing tool?

Keep your data error-free

Remove missing, incomplete, and invalid values to avoid major roadblocks in the execution of business processes.

Preserve data usability

Presence of data doesn’t guarantee data usability; improve data adaptability with a clean and standardized dataset.

Benefit from data-driven initiatives

Make the best decisions for your business by using clean and reliable data sources and gain true business insights.


Ensure data compliance

Ensure that your data management strategies comply to data compliance standards, such as such as GDPR, HIPAA, CCPA, etc.

Improve brand loyalty

Perform CRM data cleansing and leverage accurate information to offer personalized experiences to customers.

Stay relevant and up-to-date

Run quick data quality checks with an easy-to-use and inexpensive data cleansing tool for staying relevant and up-to-date.


What DME’s data cleansing tool can do for you?

With DME’s data cleansing tool, you can remove or replace empty values, leading and trailing spaces, specific letters or numbers, non-printable characters, and more.
Run data fields against a dictionary of words to identify its sub-data elements (such as Street Name and Number for Address), and merge columns to follow custom-created formats.
DME offers various features for transforming cases of letters in strings, ensuring a consistent and standardized view across all data sources.
Use DME’s extensive pattern library while data profiling and data standardization. It offers the ability to identify valid and invalid values, as well as transform data fields to follow a defined pattern (a technique widely used for masking personally identifiable information (PII)). DME also offers a visual, drag and drop regex designer for creating custom patterns.
With DME’s wordsmith tool, you can fetch the most repetitive words occurring in a data field, and decide to flag, replace, or delete certain words to achieve standardization, and prepare data for matching and deduplication.

There’s more

What else do you get out of the box?

Our data cleansing solution comes with a number of in-built features that facilitate easy, automatic, and cost-effective data cleansing 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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