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features
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support

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Description

Collate is a metadata platform powered by AI that equips data teams with automated tools for discovery, observability, quality, and governance, utilizing agent-based workflows for efficiency. It is constructed on the foundation of OpenMetadata and features a cohesive metadata graph, providing over 90 seamless connectors for gathering metadata from various sources like databases, data warehouses, BI tools, and data pipelines. This platform not only offers detailed column-level lineage and data profiling but also implements no-code quality tests to ensure data integrity. The AI agents play a crucial role in streamlining processes such as data discovery, permission-sensitive querying, alert notifications, and incident management workflows on a large scale. Furthermore, the platform includes real-time dashboards, interactive analyses, and a shared business glossary that cater to both technical and non-technical users, facilitating the management of high-quality data assets. Additionally, its continuous monitoring and governance automation help uphold compliance with regulations such as GDPR and CCPA, which significantly minimizes the time taken to resolve data-related issues and reduces the overall cost of ownership. This comprehensive approach to data management not only enhances operational efficiency but also fosters a culture of data stewardship across the organization.

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

Screenshots View All

Screenshots View All

Integrations

AWS Lake Formation
Couchbase
DruID
Google Cloud Platform
Notion
OneLogin
PostgreSQL
SAP HANA Cloud
SQL Compliance Manager
SQL Conta
SQL Enterprise Job Manager
SQL Query Tuner
SQL Secure
SQLTreeo Monitoring
Salesforce Agentforce Communications
Salesforce Agentforce Consumer Goods
Salesforce Marketing Cloud Account Engagement
SqlBak
SqlDBM

Integrations

AWS Lake Formation
Couchbase
DruID
Google Cloud Platform
Notion
OneLogin
PostgreSQL
SAP HANA Cloud
SQL Compliance Manager
SQL Conta
SQL Enterprise Job Manager
SQL Query Tuner
SQL Secure
SQLTreeo Monitoring
Salesforce Agentforce Communications
Salesforce Agentforce Consumer Goods
Salesforce Marketing Cloud Account Engagement
SqlBak
SqlDBM

Pricing Details

Free
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

Collate

Country

United States

Website

www.getcollate.io

Vendor Details

Company Name

Precisely

Founded

1968

Country

United States

Website

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

Product Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

Data Governance

Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Master Data Management

Data Governance
Data Masking
Data Source Integrations
Hierarchy Management
Match & Merge
Metadata Management
Multi-Domain
Process Management
Relationship Mapping
Visualization

Product Features

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Alternatives

Alternatives

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