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The heart of our matching software, matchit®, is deliberately designed to replicate human-like perception at scale while removing any need for preprocessing. By harnessing Artificial Intelligence, a distinctive phonetic algorithm, specialized lexicons, and a contextual scoring engine, matchit proficiently tackles the frequent mistakes, inconsistencies, and challenges linked to managing contact and business data. Unlike traditional matching systems that require users to define matching criteria using various functions and standard fuzzy algorithms to create an alphanumeric match key for record comparison, matchit takes a different approach. Instead of relying solely on a single comparison of match keys, it evaluates records contextually, executing multiple comparisons and scoring them individually to assess the similarity of all relevant aspects of your data. This thorough methodology not only boosts accuracy but also greatly enhances the efficiency of the entire matching process, thereby making it a superior choice for data management needs in various industries. The innovative design of matchit ensures that users experience a seamless and streamlined workflow.
What is QDeFuZZiner?
In the QDeFuZZiner application, the core organizational element is termed a project, which includes the definitions of two datasets intended for import and analysis, identified as the "left dataset" and the "right dataset." Each project contains these datasets along with a variable number of solutions that outline the procedures for performing fuzzy match analysis. When a project is created, it is assigned a unique project tag that is then added to the names of the input tables during the raw data importation process. This tagging mechanism ensures the distinctiveness of the imported tables by linking them to their corresponding project names. Additionally, throughout the import process and in subsequent steps for generating and implementing solutions, QDeFuZZiner constructs several indexes within the PostgreSQL database, which improves the effectiveness of fuzzy data matching tasks. The datasets can originate from various spreadsheet formats such as .xlsx, .xls, .ods, or from CSV (comma-separated values) files, which are uploaded to the server database, facilitating the creation, indexing, and processing of the associated left and right database tables. This organized methodology not only enhances data management but also optimizes the analytical process, allowing users to efficiently extract valuable insights from their datasets. Ultimately, this design aims to provide a seamless experience for users, ensuring that they can easily navigate complex data environments.
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
360Science
Date Founded
2016
Company Location
United States
Company Website
www.360science.com/data-quality-solutions/the-matching-engine/
Company Facts
Organization Name
QDeFuZZiner
Company Website
zmatasoft.wixsite.com/qdefuzziner/qdefuzziner-software-features
Categories and Features
Data Cleansing
Address/ZIP Code Cleaning
Charting
Data Consolidation / ETL
Data Mapping
Multi Data Format Support
Phone/Email Validation
Raw Data Ingestion
Sample Testing
Validation / Matching / Reconciliation