DataMatch Enterprise API
DME exposes all of its features through its API, which allows you to use any data quality management function in your custom or existing application and set it up for real-time data profiling, cleansing, matching, or deduplication.
Certified for security, quality, compliance and code integrity.
Use cases
See how our customers use DME API
Smart search
Data governance
Data processing & appending
Organizations store and maintain tons of historical data records about customers and their transactions. Millions of such records are generated daily or weekly. But before new records can be moved to the core data warehouse, it must be tested for appropriate data quality standards, and in the case of duplicates, it must be matched to ensure that the upcoming record enriches the old one, and is not created again as a new record.
This is where DME API performs exceptionally well – processing large quantities of data in near real-time to pull its unique ID, performing data matching to identify exact or fuzzy matches, and appending new data attributes to the existing record.
Results accuracy
DME Performance on a dataset of 2M records
Features
What do you get with DME API?
High performance
Exceptionally high performance and scalability features, allowing tons of data to be cleansed, matched and processed on demand.
Quick implementation
Simply download and deploy the application within minutes with the help of our guided installation wizard and start matching.
Intuitive interface
A highly visual and intuitive interface made for business users, IT specialists, data analysts and scientists, as well as novice users.
Robust matching technology
Rated faster and more accurate than IBM and SAS, DME consistently had the least number of false positives in independent studies.
Seamless integration
Readily integrate the world’s fastest and most accurate data quality features into your custom-built or third-party applications.
Real time syncs
Compute exact, fuzzy, and intelligent matches in real-time, across and within multiple data sources at blazing speeds.
Solution
One solution for all data quality problems
Quick data profiling
Master record selection
Workflow orchestration
Advanced filtering
Instant and live data preview
Record Linkage and Deduplication
Phone number standardization
Bulk cleansing and standardization
Email address cleansing
Seamless data integration
Cross-column matching
Pattern matching and recognition
Customer Stories
See what our customers say...
It’s not just the software which works very well for us, but the focus and knowledge that Data Ladder brings to the table
Thanks to Data Ladder we successfully cleaned up and matched our internal sales file with new leads, greatly improving efficiency and sales.
We could not do these reports before. Now, DataMatch has become a main staple in my suite of tools that I work with
Want to know more?
Check out DME resources
Merging Data from Multiple Sources – Challenges and Solutions
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The Truth About Data as a Service (DaaS): Why It All Breaks Without Data Matching
Everyone’s Talking About DaaS, Few Are Ready for It The concept of Data as a Service (DaaS) is having its moment. On paper, it’s easy
Big Data Analytics Is Booming – But Is Your Data Ready for It?
Amazon generates 35% of its revenue from data-powered recommendations. Netflix enjoys an 89% retention rate by personalizing every experience using viewer behavior, preferences, and interaction
The Truth About Data as a Service (DaaS): Why It All Breaks Without Data Matching
Everyone’s Talking About DaaS, Few Are Ready for It The concept of Data as a Service (DaaS) is having its moment. On paper, it’s easy
Big Data Analytics Is Booming – But Is Your Data Ready for It?
Amazon generates 35% of its revenue from data-powered recommendations. Netflix enjoys an 89% retention rate by personalizing every experience using viewer behavior, preferences, and interaction
Data Ethics in the Age of AI: Why Responsible Matching Matters More Than Ever
When AI systems deliver inaccurate or inequitable results, many people immediately assume that something went wrong in the algorithms. Rarely do we look upstream –
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