| Features of the solution | Data Ladder | IBM Quality Stage | SAS Dataflux | In-House Solutions | |
|---|---|---|---|---|---|
| Match Accuracy (Between 40K to 8M record samples) |
96% | 91% | 84% | 65-85%* | |
| Software Speed | Very Fast | Fast | Fast | Slow | |
| Purchasing / Licensing Costing | 80 to 95% Below Competition | $370K+ | $220K+ | $250K+ | |
| Time to First Result | 15 Minutes | 2 Months+ | 2 Months+ | 3 Months+ | |
| Comments | Above tests were completed on 15 different product comparisons with university, government, and private companies (80K to 8M records). This includes the effect of false positives. | Need multi-threaded. in memory, no-SQL processing to optimize for speed and accuracy. Speed is important, the more match iterations you can run, the more accurate your results will be. | Includes base license costs. 2014 prices or later, in-house, includes salary + benefits. Note in-house implementations had a 10% chance of losing in-house personnel, so over 5 years half of the in-house implementations had lost the core member who ran and understood the matching program. | A metric for ease of use. This is the time to first result, not necessarily full cleansing. |
| Match Accuracy (Between 40K to 8M record samples) | Data Ladder: 96%IBM Quality Stage: 91%SAS Dataflux: 84% In-House Solutions: 65-85%* |
| Software Speed | Data Ladder: Very FastIBM Quality Stage: FastSAS Dataflux: Fast In-House Solutions: Slow |
| Purchasing / Licensing Costing | Data Ladder: 80 to 95% Below Competition IBM Quality Stage: $370K+ SAS Dataflux: $220K+ In-House Solutions: $250K+ |
| Time to First Result | Data Ladder: 15 Minutes IBM Quality Stage: 2 Months+ SAS Dataflux: 2 Months+ In-House Solutions: 3 Months+ |
Research reveals that 94% of businesses admit to having duplicate data, and the majority of these duplicates are non-exact matches and therefore usually remain undetected. Fuzzy matching software helps you make those connections automatically using sophisticated proprietary matching logic, regardless of spelling errors, unstandardized data, or incomplete information.
Spelling errors and inconsistent data formats result in a lot of missed matching and linkage opportinities.
Inconsistent field definitions exist across disparate data sources.
Lack of data standardization and governance creates duplicate records which are difficult to identify.
Name, address, and phone number changes are very common and cause duplicates of the same entity.
Fuzzy Matching is defined as the process of identifying records on two or more datasets that refer to the same entity across various data sources such as databases and websites. fuzzy matching software is required when combining data sets that don’t have a common identifier, such as an identification number, or when linking records where exact matches are rare because of misspellings, unstandardized data, or incomplete information. The technique is invaluable when creating a Single Customer View (SCV) or building a strategic foundation for Master Data Management (MDM).
Regular matching techniques won’t be able to detect a match between “Hammer” and “Hamer”, but fuzzy matching software will.
Leverage standardization libraries to match variations of first names or nicknames – “Vinny” and “Vinnie”, or “Peggy” and “Margaret”.
Set a match threshold (0-100%), match on multiple fields/columns, and see the match percentage on each field/column.
Match “Cleaners” and “Cleanrs”, “Folder” and “Foldwer” using phonetic fuzzy matching algorithms like Metaphone 3.
Studies evaluating the top 15 data quality vendors found that ours was consistently the fastest and most accurate fuzzy matching software.
Data Ladder offers pre-populated, fully customizable nickname, address, abbreviation, phone number, and pattern-based libraries.
Determine cross-cultural matches regardless of language or country. All languages in the Unicode standard are supported.
The Quick Data Profile tool finds and fixes data quality issues within the first 5 minutes of setup to improve match quality.
People may enter different variations or combinations of names, addresses, and contact information. John Smith and John A. Smith may be the same customer, but the addition of one alphabet will result in duplicates. Such errors are very common in data, and manually detecting them is not a feasible strategy when there are millions of records.
DataMatch is a highly visual desktop fuzzy matching software specifically designed to solve customer and contact data quality issues. DataMatch includes multiple proprietary and standard algorithms to detect phonetic, fuzzy, miskeyed, and abbreviated variations. The suite consists of scalable configurations for deduplication and record linking, suppression, enhancement, extraction, and standardization of business and customer data.
Data Ladder’s fuzzy matching tools are employed in many different industries to cleanse data. find and link customer data, consolidate data across multiple sources, and remove deceased and unwanted records – quickly and easily improving your marketing and mailing performance. It is used in fraud prevention to detect deceptive entities. It is used in the healthcare sector to ensure that every patient has only one complete medical history, enriched from 3rd party repositories. It is used in research firms to clean data before analysis. Any business activity that needs clean data will need advanced fuzzy matching tools.
Our fuzzy matching software works on relational databases, big data lakes, CRMs, file formats, and social media platforms. The intuitive user interface has been designed for business users and uses graphical reports, multiple tabs, and simple menus to ensure that both business and IT users can “Get the Most Out of Your Data”.
During your 30-day trial, you can access DataMatch Enterprise risk-free. The software is user-friendly and easy to install – what you see is what you get! However, we recommend a 30 to 60-minute non-obligatory online consultation with one of our subject matter experts to help you get the most out of your free trial.
