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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.

What is DQE One?

In our current digital age, customer information can be found everywhere, from mobile devices and social media to IoT gadgets, CRM systems, ERP tools, and various marketing strategies. While organizations gather an enormous amount of data, much of it tends to be underused, incomplete, or inaccurate, which can hinder operational efficiency and threaten potential growth. To avoid disruptions, it is crucial that customer data acts as an integrated component linking all aspects of business operations. Ensuring the reliability and accessibility of this information for all stakeholders at any given moment is paramount. The DQE One solution is designed to support all departments that depend on customer data, enhancing the quality of information and building trust in the decision-making process. As contact information accumulates from various sources within corporate databases, issues may arise, such as data entry errors, incorrect contact details, and missing information. Therefore, it becomes essential to consistently validate and maintain the customer database throughout its lifecycle, converting it into a trustworthy asset. By emphasizing data quality, businesses can not only enhance their operational efficiency but also open doors to new opportunities for growth and innovation that were previously unexplored. Ultimately, the commitment to high-quality data management can significantly influence a company's long-term success.

Media

Media

Integrations Supported

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

Integrations Supported

Act!
Act-On
Bullhorn
Eloqua
Google Ads
Google Analytics
Highrise
HubSpot Customer Platform
Magento
Mailchimp
Marketo
Microsoft Dynamics 365
NetSuite
Oracle CRM On Demand
SAP Commerce Cloud
Salesforce Marketing Cloud
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

Data Ladder

Date Founded

2006

Company Location

United States

Company Website

dataladder.com

Company Facts

Organization Name

DQE

Date Founded

2008

Company Location

United Kingdom

Company Website

dqe.tech/en/dqe-one/

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

Categories and Features

Data Quality

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

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