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

INDUSTRIES
Doesn’t matter where you’re from
Want to know more?
Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions
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Buyer’s Guide: 10-Point Checklist for Evaluating Vendor Matching Software
A typical SMB[1] has an invoice duplication rate of 1.29%. That’s more than $12,000 in potential duplicate payments per month if each goes through. Now

Vendor Matching Software Explained: Solving Duplicate Vendor Records at Scale
In a poll by CFO Daily News, 86% of accounts payable (AP) professionals admitted their Master Vendor File (MVF) needed work. More than two-thirds said

Buyer’s Guide: 10-Point Checklist for Evaluating Vendor Matching Software
A typical SMB[1] has an invoice duplication rate of 1.29%. That’s more than $12,000 in potential duplicate payments per month if each goes through. Now

Vendor Matching Software Explained: Solving Duplicate Vendor Records at Scale
In a poll by CFO Daily News, 86% of accounts payable (AP) professionals admitted their Master Vendor File (MVF) needed work. More than two-thirds said

The Role of Data in Mergers and Acquisitions: Why Data Quality Drives Value and De-Risks Your Deal
Mergers and acquisitions (M&A) rarely fail because the lawyers got the paperwork wrong. But they many times do because no one paid enough attention to































