Accurate matching without friction
Enhance the quality of data spread across disparate sources by uncovering missed or overlooked matches using proprietary and established matching algorithms.
Why choose
Data Ladder
- High match accuracy
- Real-time processing
- User-friendly UI
- Address verification
- Hands-on support
- ZIP+4 geocoding
Features
We take care of your complete DQM lifecycle
Import
Connect and integrate data from multiple disparate sources
Profiling
Automate data quality checks and get instant data profile reports
Cleansing
Standardize & transform datasets through various operations
Matching
Execute industry-grade data match algorithms on datasets
Deduplication
Eliminate duplicate values and records to preserve uniqueness
Merge & purge
Configure merge and survivorship rules to get the most out of data
USE CASES
A codeless solution that helps you to achieve
Link records across the enterprise
Resolve and reconcile entities
Match using fuzzy logic
Match and classify product data
Standardize address data
CUSTOMER STORIES
Still unsure? See why others prefer Data Ladder
DataMatch Enterprise™ was much easier to use than the other solutions we looked at. Being able to automate data cleaning and matching has saved us hundreds of person-hours each year.
We obtained 24% higher match rate using DataMatch Enterprise™ versus our standard vendor.
We liked the ability of the product to categorize the data in the way that we need it, and its versatility in doing that.
Let’s compare
How accurate is our solution?
In-house implementations have a 10% chance of losing in-house personnel, so over 5 years, half of the in-house implementations lose the core member who ran and understood the matching program.
Detailed tests were completed on 15 different product comparisons with university, government, and private companies (80K to 8M records), and these results were found: (Note: this includes the effect of false positives)
| Features of the solution | Data Ladder | IBM Quality Stage | SAS Dataflux | In-House Solutions | Comments |
|---|---|---|---|---|---|
| Match Accuracy (Between 40K to 8M record samples) | 96% | 91% | 84% | 65-85% | Multi-threaded, in-memory, no-SQL processing to optimize for speed and accuracy. Speed is important, because the more match iterations you can run, the more accurate your results will be. |
| Software Speed | Very Fast | Fast | Fast | Slow | A metric for ease of use. Here speed indicates time to first result, not necessary full cleansing. |
| Time to First Result | 15 Minutes | 2 Months+ | 2 Months+ | 3 Months+ | |
| Purchasing/Licensing Costing | 80 to 95% Below Competition | $370K+ | $220K+ | $250K+ | Includes base license costs. |
Professional services
Ensure a holistic data strategy for your mission-critical projects including clear alignment between your data and business goals
Implementation services
Seek assistance in implementing Data Ladder software solutions from set up to execution for your data quality program.
Tailored programs
Get a customized data quality program that is tailored to your business’s specific goals and challenges to define the scope and strategy required.
Training and certification
Learn the skills needed to apply Data Ladder solutions in both simple and complex scenarios via dedicated team or 1-to-1 product training.
SERVICES
Want expert advice on data quality?
Professional services
Implementation services
Training and certification
Tailored programs
Want to know more?
Check out our resources
Merging Data from Multiple Sources – Challenges and Solutions
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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
Why Business Intelligence Initiatives Fail Without Clean and Connected Data
Executives love the promise of business intelligence (BI). Dashboards that tell the truth. Reports that line up across departments. And strategies driven by real data
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
Why Business Intelligence Initiatives Fail Without Clean and Connected Data
Executives love the promise of business intelligence (BI). Dashboards that tell the truth. Reports that line up across departments. And strategies driven by real data
The Make-or-Break Role of BI and Clean Data in a Digital Transformation Strategy
More than 55% of organizational data worldwide is dark, meaning it’s collected, processed, and stored, but never used. But over 80% of business leaders believe
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