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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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 –
