Product Match
Certified for security, quality, compliance and code integrity.
Use cases
See how our customers use ProductMatch
UNSPSC product classification
Catalog building
Product and attribute gap analysis
Features
What do you get with ProductMatch?
Semantic recognition
The platform’s powerful contextual recognition engine understands helps match and prep data in a structured format, eliminating the need for data transformation
Product deduplication & linkage
Reduce the number of parts in your inventory dramatically while enriching product data with attributes and classifications by matching across the enterprise.
Pattern matching
Use the Regex wizard to quickly identify patterns and parse records into new fields. Example: Text “3 x 4 x 6” can be extracted into: Length = 3, Width = 4, and Height = 6.
Product matching
Match key fields like part number and manufacturer name or key capabilities like product functionality by extracting attributes to understand product relationships.
Point-and-click interface
Data Ladder provides a modern, visual interface proven to improve attribute extraction, standardization, structuring, and match accuracy by at least 10%.
Standardization at scale
Identify and correct typos in unstructured data, parse relevant attributes with advanced pattern matching, and apply standardization rules at scale.
Solution
One solution for all data quality problems
Machine learning capabilities
Pattern matching
Contextual recognition
Data quality validation
Intelligent parsing
Taxonomy development
In-memory processing
Custom output functions
Rule-driven data quality validation
Competitive intelligence
Catalog building
Product gap analysis
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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Source-to-Target Mapping Best Practices for Accurate, Scalable Data Pipelines
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Managing Nicknames, Abbreviations & Name Variants in Enterprise Entity Matching
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Source-to-Target Mapping Best Practices for Accurate, Scalable Data Pipelines
Last Updated on January 28, 2026 Source- to-target mapping usually gets attention for about five minutes, right before a pipeline goes live. After that, it’s
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