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
What is data profiling: Scope, techniques, and challenges
Today, enterprises highly depend on data for growing their businesses and scaling their goals and expectations. Huge efforts are being invested in devising the perfect
Why duplicates exist and how to get rid of them?
According to Natik Ameen, Marketing Expert at Canz Marketing, duplicate data in the company’s CRM happens due to a range of reasons: “from a human error
What is data profiling: Scope, techniques, and challenges
Today, enterprises highly depend on data for growing their businesses and scaling their goals and expectations. Huge efforts are being invested in devising the perfect
Why duplicates exist and how to get rid of them?
According to Natik Ameen, Marketing Expert at Canz Marketing, duplicate data in the company’s CRM happens due to a range of reasons: “from a human error
Merging Data from Multiple Sources – Challenges and Solutions
Merging data from multiple sources Data merging is a process where data is unified from multiple sources to represent a single point of reference or a single point
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