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What is DataGroomr?

DataGroomr offers a straightforward solution for eliminating duplicate records in Salesforce by leveraging advanced Machine Learning technology. This tool automatically identifies duplicate entries, placing them into a convenient queue where users can easily review and compare them side by side, making informed decisions about which data to retain, modify, or merge. With DataGroomr, you gain all the necessary features to efficiently find, merge, and eradicate duplicates. The sophisticated algorithms at work streamline the entire process, allowing for seamless merging of duplicate records with just a click or in bulk directly within the application. Users have the flexibility to choose specific field values to establish a master record, or they can opt for inline editing to introduce new information. It's crucial to maintain a duplicate-free environment across your organization, and you have the ability to customize your data based on various criteria such as industry, region, or any specific Salesforce field. The import wizard enhances this functionality by enabling you to merge, deduplicate, and append records during the import process. Additionally, you can schedule automated duplication reports and bulk merging tasks according to your preferred timeline, ensuring a smooth and efficient workflow. This way, DataGroomr not only simplifies the management of your Salesforce records but also significantly boosts overall data integrity and organization efficiency.

What is DQ for Excel?

Elevate your customer data management in an accessible setting by effortlessly exporting it to Microsoft Excel and employing our convenient plugin available in the Office Store, which enhances data quality significantly. Our tool allows you to modify data by abbreviating, expanding, omitting, or normalizing it in five languages and across twelve distinct categories of entities. You can analyze the similarities between records using various comparison methods, including Levenshtein and Jaro-Winkler, while also generating phonetic match keys for deduplication, such as DQ Fonetix™, Soundex, and Metaphone. Furthermore, classify your data to identify the nature of each entry—for example, distinguishing Brian or Sven as individuals, while recognizing Road, Strasse, or Rue as parts of an address, and identifying Ltd or LLC as corporate legal designations. You have the capability to extract information like gender from names and sort contact details based on job titles and roles that involve decision-making. DQ for Excel™ integrates seamlessly with Microsoft Excel, ensuring that it is both user-friendly and efficient for managing data effectively. In addition, its robust functionalities guarantee that your customer data stays precise, pertinent, and well-organized. This comprehensive approach not only streamlines your workflow but also significantly enhances the overall quality of your data management practices.

Media

Media

Integrations Supported

Salesforce

Integrations Supported

Microsoft Excel

API Availability

Has API

API Availability

Pricing Information

$99 per user per year
Free Trial Offered?

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

DataGroomr

Date Founded

2019

Company Location

United States

Company Website

datagroomr.com

Company Facts

Organization Name

DQ Global

Date Founded

1997

Company Location

United States

Company Website

www.dqglobal.com/products/dq-for-excel/

Categories and Features

Data Deduplication

Not specified

Data Preparation

Data Cleansing
Data Transformation
Machine Learning
Visual User Interface

Data Quality

Address Validation
Data Deduplication
Master Data Management
Match & Merge

Categories and Features

Data Quality

Not specified

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