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What is NetOwl NameMatcher?

NetOwl NameMatcher, celebrated for its superior performance in the MITRE Multicultural Name Matching Challenge, offers exceptional accuracy, rapid processing, and scalability in its name matching solutions. Utilizing a cutting-edge machine learning framework, NetOwl adeptly addresses the complex challenges associated with fuzzy name matching. Traditional techniques like Soundex, edit distance, and rule-based systems frequently struggle with precision, leading to an abundance of false positives, and recall issues that result in false negatives, particularly when faced with the varied fuzzy name matching scenarios mentioned earlier. In contrast, NetOwl adopts a data-driven, machine learning-based probabilistic approach to overcome these name matching challenges effectively. It autonomously develops advanced, probabilistic name matching rules from vast real-world datasets containing multi-ethnic name variants. Additionally, NetOwl implements specialized matching models designed for different entity types, including individuals, organizations, and geographical locations. To enhance its functionality, NetOwl incorporates automatic detection of name ethnicity, which significantly boosts its adaptability to the complexities inherent in multicultural name matching. This holistic strategy not only elevates accuracy but also ensures dependable performance across a wide array of applications. Consequently, organizations relying on precise name matching can greatly benefit from the innovative solutions provided by NetOwl.

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

ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
Tableau

Integrations Supported

Microsoft Excel

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NetOwl

Date Founded

1996

Company Location

United States

Company Website

www.netowl.com/name-matching-software

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 Quality

Not specified

Categories and Features

Data Quality

Not specified

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