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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
Enhance the integrity of your data both during transit and when stored by implementing superior monitoring, visualization, remediation, and reconciliation techniques. Ensuring data quality should be ingrained in the core values of your organization. Go beyond standard data quality assessments to gain a comprehensive understanding of your data as it traverses through your organization, regardless of its location. Continuous monitoring of quality and meticulous point-to-point reconciliation are essential for fostering trust in data and providing reliable insights. Data360 DQ+ streamlines the process of data quality evaluation throughout the entire data supply chain, commencing from the moment information enters your organization to oversee data in transit. Examples of operational data quality include validating counts and amounts across various sources, monitoring timeliness to comply with internal or external service level agreements (SLAs), and conducting checks to ensure that totals remain within predefined thresholds. By embracing these practices, organizations can significantly improve decision-making processes and enhance overall performance.
API Access
Has API
API Access
Has API
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
Precisely
Founded
1968
Country
United States
Website
www.precisely.com/product/precisely-data360/data360-dq
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