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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 Anomalo?

Anomalo empowers organizations to proactively address data challenges by swiftly identifying issues before they affect users. It offers comprehensive monitoring capabilities, featuring foundational observability with automated checks for data freshness, volume, and schema variations, along with in-depth quality assessments for consistency and accuracy. Leveraging unsupervised machine learning, it autonomously detects missing and anomalous data effectively. Users can navigate a no-code interface to create checks that compute metrics, visualize data trends, build time series models, and receive clear alerts through platforms like Slack, all while benefiting from insightful root cause analyses. The intelligent alerting system utilizes advanced unsupervised machine learning to dynamically adjust time series models and employs secondary checks to minimize false positives. By generating automated root cause analyses, it significantly reduces the time required to understand anomalies, and its triage feature streamlines the resolution process, integrating seamlessly with various remediation workflows, including ticketing systems. Additionally, Anomalo prioritizes data privacy and security by allowing operations to occur entirely within the customer's own environment. This ensures that sensitive information remains protected while still gaining the benefits of robust data monitoring and management.

Media

Media

Integrations Supported

Alation
Amazon Athena
Amazon Redshift
ArcGIS
Atlan
Azure Synapse Analytics
DataHub
Databricks Data Intelligence Platform
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
Presto
SQL Server
Snowflake
SolrCommerce
Tableau

Integrations Supported

Alation
Amazon Athena
Amazon Redshift
ArcGIS
Atlan
Azure Synapse Analytics
DataHub
Databricks Data Intelligence Platform
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
Presto
SQL Server
Snowflake
SolrCommerce
Tableau

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

NetOwl

Date Founded

1996

Company Location

United States

Company Website

www.netowl.com/name-matching-software

Company Facts

Organization Name

Anomalo

Date Founded

2018

Company Location

United States

Company Website

www.anomalo.com

Categories and Features

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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