Get to know us
About Data Ladder
- Pinpoint Matching Accuracy
- Real-Time Processing
- US & CA Address Verification
- ZIP+4 Level Geocoding
- User-Friendly Interface
- Hands-On Support
Our History
Delivering 15+ years’ worth of industry experience
Based out of Suffield, Connecticut, Data Ladder has relentlessly pursued to fulfill market needs for high-precision data quality and matching.
With over 15 years in product installations across government, financial services, education, and marketing verticals, Data Ladder’s matching prowess and rapid time-to-value has successfully delivered modern data cleansing, address verification, and entity resolution projects. This has enabled Data Ladder to serve large Fortune 500 companies such as Deloitte, GE and HP, government institutions such as USDA and Department of Transportation, and small to mid-sized startups.
Our Values
What do we care about?
Integrity
We pride ourselves on offering solutions based on expertise and industry experience.
Trust
Our teams work closely with clients to understand their unique challenges and deliver accordingly.
Scalability
We equip you with the solutions to accelerate your operations and long-term profitability
Employees
Our employees are the foundation of our success and the secret to achieving higher milestones.
There’s more
Where are we headed?
We firmly believe in the importance of keeping our ear close to the ground to learn from our experiences and continually refine and optimize our product offering to address current and upcoming data quality and matching challenges.
At Data Ladder, we aim to embed and harness the power of AI to tailor our solutions for complex matching environments without compromising simplicity in usability and time to value.
Customer Stories
Still unsure? See what others are saying…
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
Last Updated on January 21, 2026 Source- to-target mapping usually gets attention for about five minutes, right before a pipeline goes live. After that, it’s
Managing Nicknames, Abbreviations & Name Variants in Enterprise Entity Matching
Last Updated on January 26, 2026 A name might feel like the simplest identifier, but in enterprise datasets, it rarely is. In the US and
Source-to-Target Mapping Best Practices for Accurate, Scalable Data Pipelines
Last Updated on January 21, 2026 Source- to-target mapping usually gets attention for about five minutes, right before a pipeline goes live. After that, it’s
Managing Nicknames, Abbreviations & Name Variants in Enterprise Entity Matching
Last Updated on January 26, 2026 A name might feel like the simplest identifier, but in enterprise datasets, it rarely is. In the US and
Linking Similar Records with Incomplete Data: Proven Approaches for High-Accuracy Entity Matching
Last Updated on January 13, 2026 If record linkage were as simple as matching names and emails, organizations wouldn’t be sitting on mountains of unleveraged
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