Data Ladder – 360Science Alternative
- Array of one-click list cleansing and standardization features
- Built-in Pattern Designer & Builder for proprietary records validation
- Flexible matching options to meet specific deduplication requirements
- No limits imposed on maximum number of jobs or records
Customer Stories
See what our customers say...
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
DataMatch Enterprise is an efficient, effective, and relatively easy to use software
We could not do these reports before. Now, DataMatch has become a main staple in my suite of tools that I work with!
Our Differentiators
DataMatch Enterprise or MatchIt on Demand – You Choose
Intuitive cleansing & standardization features
Have lists that need cleansing and normalization? Utilize DataMatch Enterprise’s suite of user-friendly data scrubbing and standardization features to spruce up your CRM and database lists as per your needs.
Fix spelling and casing errors, remove leading and trailing spaces, define field patterns using the Pattern Builder, or benefit from reusable workflows for batch or real-time cleansing.
Flexible matching options
Choose between, within or all match configurations to define how your data sources should be matched either by searching inter-data matches, matches within each data source, or both. Select exact, fuzzy, numeric or phonetic matching types to detect different types of variations. Easily configure match threshold levels to effect number of false positives.
| Data Ladder Solution | DataMatch Enterprise (DME: Desktop) | DataMatch Enterprise + Address Verification (DME + CASS:Desktop) | DataMatch Enterprise (DMES: Server) | DataMatch Enterprise Server + Api (DMES + API:Server) |
|---|---|---|---|---|
| Fuzzy Matching | Yes(Advanced Weight System, Advanced Algorithms) | Yes(Advanced Weight System, Advanced Algorithms) | Yes(Advanced Weight System, Advanced Algorithms) | Yes(Advanced Weight System, Advanced Algorithms) |
| Profiling | Advanced (Pattern Delection and Outlier Monitoring) | Advanced (Pattern Delection and Outlier Monitoring) | Advanced (Pattern Delection and Outlier Monitoring) | Advanced (Pattern Delection and Outlier Monitoring) |
| Speed | 1 Million Rec. 10 to 45 Minutes | 1 Million Rec. 5 to 20 Minutes | 1 Million Rec. 5 to 20 Minutes | 1 Million Rec. 5 to 20 Minutes (Web Services - Response Time In Milliseconds) |
| Users | 1 Desktop | 1 Desktop | 3 Virtual Machines on 1 server | 3 Virtual Machines on 1 server (Web Services - Response Time In Milliseconds) |
| Records | No Imposed Limit(Tested on 50 Million+) | No Imposed Limit(Tested on 50 Million+) | No Imposed Limit(Tested on 100 Million+) | No Imposed Limit(Tested on 1 Billion+) |
| Number of Tables | 10+ | 10+ | Unlimited in Theory | Unlimited in Theory |
| Upgradable* | YES | YES | YES | NO |
| Regex | YES | YES | YES | YES |
| Scheduler | YES | YES | YES | YES |
| Wordsmith | YES | YES | YES | YES |
| Report Building | YES | YES | YES | YES |
| Address Verification | NO | YES | YES | YES |
| Support | Platinum: Same Day Email/Phone Based Response(Before 4:00 PM EST) | Platinum: Same Day Email/Phone Based Response(Before 4:00 PM EST) | Platinum: Same Day Email/Phone Based Response(Before 4:00 PM EST) | Platinum: Same Day Email/Phone Based Response(Before 4:00 PM EST) |
| Training | 2 HRS | 2 HRS | 5 HRS | 10 HRS |
Want to know more?
Check out DME resources
Merging Data from Multiple Sources – Challenges and Solutions
Oops! We could not locate your form.
Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Batch processing versus real-time data quality validation
A recent survey shows that 24% of data teams use tools to find data quality issues, but they are typically left unresolved. This means that
ready? let's go
Try now or get a demo with an expert!
