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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Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Batch processing versus real-time data quality validation
A recent survey shows that 24% of data teams use tools to find data quality issues, but they are typically left unresolved. This means that