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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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 –
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 –
Why Data Interoperability Challenges Are Holding Back Your Insights – And What to Do About It
Every modern organization thinks it’s interoperable—until the data says otherwise. Your systems are technically connected. APIs are firing. Data is flowing. But when you zoom
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