Data cleansing software
A feature-rich toolkit 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.
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DEFINiTION
What is data cleansing?
Data cleansing is the process of fixing incorrect and invalid information present in databases, and achieving a consistent and usable view across all disparate sources.
This process usually consists of eliminating incorrect values, validating the format and pattern of data values, using appropriate data types, putting min/max character limit, and more.
Benefits
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.
Features
What DME’s data cleansing can do for you?
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.
- Live preview of cleansed data
- Data type identification
- Pre-built & custom patterns
- Scheduler for automatic data cleansing
- Proper case transformation
- Dictionary of common words
- Custom filters for personalized view
- 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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An average enterprise – with 200-500 employees – uses about 123 SaaS applications to digitize their business processes. With large amounts of data being generated
Data quality management: What, why, how, and best practices
Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise choice
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