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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.

What is DataMatch?

The DataMatch Enterpriseâ„¢ solution serves as a user-friendly tool for data cleansing, specifically designed to tackle challenges associated with the quality of customer and contact information. It employs an array of both unique and standard algorithms to identify inconsistencies that may result from phonetic similarities, fuzzy matches, typographical errors, abbreviations, and domain-specific variations. Users have the ability to implement scalable configurations for a variety of processes, including deduplication, record linkage, data suppression, enhancement, extraction, and the standardization of business and customer data. This capability is instrumental in helping organizations achieve a cohesive Single Source of Truth, which significantly boosts the overall effectiveness of their data management practices while safeguarding data integrity. In essence, this solution enables businesses to make strategic decisions rooted in precise and trustworthy data, ultimately fostering a culture of data-driven decision-making across the organization. By ensuring high-quality data, companies can enhance their operational efficiency and drive better customer experiences.

Media

Media

Integrations Supported

Act!
Act-On
Bullhorn
Eloqua
Google Analytics
Highrise
HubSpot Customer Platform
Magento
Mailchimp
Marketo
Microsoft Dynamics 365
NetSuite CRM
Oracle CRM On Demand
PostgreSQL
SAP Commerce Cloud
Salesforce
SendGrid
Veeva CRM
Zoho CRM
unTill

Integrations Supported

Act!
Act-On
Bullhorn
Eloqua
Google Analytics
Highrise
HubSpot Customer Platform
Magento
Mailchimp
Marketo
Microsoft Dynamics 365
NetSuite CRM
Oracle CRM On Demand
PostgreSQL
SAP Commerce Cloud
Salesforce
SendGrid
Veeva CRM
Zoho CRM
unTill

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

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

QDeFuZZiner

Company Website

zmatasoft.wixsite.com/qdefuzziner/qdefuzziner-software-features

Company Facts

Organization Name

Data Ladder

Date Founded

2006

Company Location

United States

Company Website

dataladder.com

Categories and Features

Categories and Features

Address Verification

Address Validation
Autocomplete
Automatic Formatting
Data Cleansing
Data Discovery
Data Quality Control
Data Verification
Geographic Maps
Geolocation
Metadata Management
Reporting / Analytics
Search / Filter

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

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