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Description
Enhance your customer data within a user-friendly environment by easily exporting it into Microsoft Excel and utilizing our plugin, which can be found in the Office Store for improved data quality. With our tool, you can transform data by abbreviating, elaborating, excluding, or normalizing it across five spoken languages and twelve distinct entity categories. You can assess the similarity between records through various comparison techniques, such as Levenshtein and Jaro-Winkler, and generate phonetic match keys for deduplication purposes, including DQ Fonetix™, Soundex, and Metaphone. Additionally, classify your data to determine what each piece represents—for instance, recognizing Brian or Sven as personal names, while identifying Road, Strasse, or Rue as elements of an address, and Ltd or LLC as legal suffixes for companies. You can also derive information such as gender from names and categorize contact information based on job titles and decision-making roles. DQ for Excel™ operates seamlessly within Microsoft Excel, making it both intuitive and straightforward to use, thus streamlining your data management processes effectively. Moreover, with its powerful features, you can ensure that your customer data remains accurate, relevant, and organized.
Description
DQV is the comprehensive data quality and testing platform developed by Kumaran Systems, catering to teams engaged in the movement, masking, or validation of substantial data volumes. This tool meticulously evaluates source and target datasets on a field-by-field basis, identifies discrepancies, and produces mismatch reports, eliminating the need for manual checks using spreadsheets.
The platform encompasses five key functionalities: it performs field-level comparisons while detecting drift, facilitates migration mapping between different schemas, offers deterministic PII masking, conducts record- and table-level validation with the ability for on-the-fly corrections, and generates synthetic data for teams lacking access to production data for testing.
DQV is compatible with a wide range of data sources, including SQL Server, Oracle, MySQL, PostgreSQL, AWS, Azure, GCP, flat files, JSON, XML, and REST APIs, and it seamlessly integrates with tools like Informatica, Databricks, and CI/CD pipelines, or can operate independently as a library or CLI tool.
In real-world applications, DQV has successfully validated an impressive 26.6 million bank records in less than 22 minutes, showcasing its efficiency and speed. Additionally, a free trial is offered, along with various licensing options, including individual, enterprise, and on-premises plans, ensuring flexibility for different organizational needs. This versatile solution not only enhances data integrity but also streamlines the testing process across multiple platforms.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Microsoft Excel
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DQ Global
Founded
1997
Country
United States
Website
www.dqglobal.com/products/dq-for-excel/
Vendor Details
Company Name
Kumaran Systems
Founded
1992
Country
United States
Website
kumaran.com/products/dqv/
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management