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Average Ratings 0 Ratings
Description
Customer information is ubiquitous in today's world, spanning across cell phones, social media platforms, IoT devices, customer relationship management systems, enterprise resource planning tools, and various marketing efforts. The sheer volume of data collected by companies is immense, yet it frequently remains underutilized, incomplete, or even inaccurate. Poorly managed and low-quality data can disrupt organizational efficiency, jeopardizing significant growth opportunities. It is essential for customer data to serve as a cohesive element connecting all business processes. Ensuring that this data is both reliable and readily available to everyone, at any time, is of utmost importance. The DQE One solution caters to all departments that utilize customer data, promoting high-quality information that fosters trust in decision-making. Within corporate databases, contact details sourced from different channels often accumulate, leading to potential issues. With the presence of data entry mistakes, erroneous contact details, and information gaps, it becomes vital to regularly validate and sustain the customer database throughout its lifecycle, transforming it into a dependable resource. By prioritizing data quality, companies can unlock new avenues for growth and innovation.
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
Integrations
Safyr
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
DQE
Founded
2008
Country
United Kingdom
Website
dqe.tech/en/dqe-one/
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