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features
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Description

Match2Lists provides the quickest, simplest, and most precise solution for matching, merging, and de-duplicating your data. With our Match2D&B feature, you can seamlessly enhance your datasets with Dun & Bradstreet information whenever needed. Within a matter of minutes, you can rid your data of duplicates and integrate disparate raw data into impactful insights. Our primary goal is to achieve the highest match results possible for our clients. Before we developed Match2Lists, we operated analytics and data visualization firms, utilizing various "fuzzy" matching software available in the industry. Frustrated by their inadequate match outcomes, we dedicated ten years to crafting the most sophisticated data matching algorithms. Our secondary goal is to optimize time: we aim to allow our clients to devote less time to data matching and cleansing, and instead focus on analysis and execution. This led us to implement our cutting-edge matching logic on the fastest in-memory cloud computing infrastructure we could find, which can process 200 million records in just 30 seconds. Now, businesses can enjoy enhanced productivity and make informed decisions rapidly.

Description

In QDeFuZZiner software, the fundamental unit is referred to as a project, which encompasses the definitions of two source datasets for import and analysis, known as the "left dataset" and "right dataset." Each project not only includes these datasets but also a variable number of solutions that detail the methodology for conducting fuzzy match analysis. Upon creation, every project is assigned a distinct project tag, which is subsequently appended to the names of the corresponding input tables during the raw data import process. This tagging system guarantees that the imported tables maintain uniqueness through association with their respective project names. Furthermore, during the import phase and later when generating and executing solutions, QDeFuZZiner establishes various indexes on the PostgreSQL database, thereby enhancing the efficiency of fuzzy data matching procedures. The datasets themselves can be sourced from spreadsheet formats such as .xlsx, .xls, .ods, or from CSV (comma separated values) flat files, which are uploaded to the server database, leading to the creation, indexing, and processing of the associated left and right database tables. This structured approach not only simplifies data management but also streamlines the analysis process, making it easier for users to derive insights from their datasets.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

PostgreSQL No 

Integrations

PostgreSQL Yes 

Pricing Details

$95 per month
Billed annually
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Match2Lists

Country

United Kingdom

Website

www.match2lists.com

Vendor Details

Company Name

QDeFuZZiner

Website

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

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