Average Ratings 0 Ratings
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
Yes
API Access
Has API
No
Integrations
Safyr
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
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
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No
Product Features
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No