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features
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support

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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.

Description

Assisting businesses in actively overseeing their comprehensive data quality lifecycle is achieved through the implementation of contextual data quality assessments, anomaly detection, and corrective measures. By revealing anomalies and relevant metadata, teams are empowered to implement necessary corrective actions effectively. Automated remediation workflows can be initiated to swiftly and efficiently address any errors that arise. This proactive approach helps ensure superior data quality, safeguarding against inaccuracies that could undermine business decision-making. Additionally, the SLA chart offers a detailed overview of service level agreements, showcasing the total number of monitoring activities conducted and any violations encountered. Such insights can significantly aid in pinpointing specific areas of your data that may necessitate further scrutiny or enhancement. Ultimately, maintaining robust data quality is essential for driving informed business strategies and fostering growth.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Redshift No 
Amazon S3 No 
Azure Blob Storage No 
Databricks No 
Google Cloud BigQuery No 
Google Cloud Storage No 
Safyr Yes 
Snowflake No 

Integrations

Amazon Redshift Yes 
Amazon S3 Yes 
Azure Blob Storage Yes 
Databricks Yes 
Google Cloud BigQuery Yes 
Google Cloud Storage Yes 
Safyr No 
Snowflake Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version 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 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Precisely

Founded

1968

Country

United States

Website

www.precisely.com/product/precisely-data360/data360-dq

Vendor Details

Company Name

Qualytics

Founded

2020

Country

United States

Website

qualytics.co

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 

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