Test - Artha Solutions

What makes Ataccama DQ better?

1 DQ capabilities built natively with AI

Quality throughout
Improvements made easy

Continuously ensure data accuracy, credibility, consistency, integrity, and completeness.
Implement automated data validation rules and prevent incomplete or incorrect data from entering business systems.
Integrate DQ checks with your existing ETL, CI/CD pipelines, and analytics platforms.
Standardize, cleanse, validate, enrich and match data with predefined and customizable rules.

Automate data quality remediation at various stages.
Reduce the need for manual intervention with AI-assisted cleansing, standardization, and issue resolution in a collaborative data catalog.
Streamline data quality preparation, remediation and other processes with data quality co-pilots and assistants.

2 Proactive data quality

Monitor
Profile and detect anomalies
Prevent

Implement data quality monitoring. Identify and rectify issues in real-time.
Easily evaluate and monitor data quality from an integrated data catalog. See all data quality metrics and trends such as anomalies in one place.
Address issues promptly thanks to automated alerts and notifications.

Regularly perform data profiling and data cleansing. Identify patterns, distributions and anomalies without writting SQL queries or Excel functions.
Automatically detect data quality issues such as missing values, duplicates, outliers and inconsistencies.
Get alerted about deviations in data loads, data structure, and more.

Prevent data quality issues, and stop bad data from entering your systems with a DQ firewall.
Leverage predefined Python SDK and access data quality as a code.
Use data quality rules in complex projects and expose them as a service for other apps.

3 Integrated Data Governance

Metadata management
Lineage
Stewardship

Implement data quality monitoring. Identify and rectify issues in real-time.
Easily evaluate and monitor data quality from an integrated data catalog. See all data quality metrics and trends such as anomalies in one place.
Address issues promptly thanks to automated alerts and notifications.

Regularly perform data profiling and data cleansing. Identify patterns, distributions and anomalies without writting SQL queries or Excel functions.
Automatically detect data quality issues such as missing values, duplicates, outliers and inconsistencies.
Get alerted about deviations in data loads, data structure, and more.

Prevent data quality issues, and stop bad data from entering your systems with a DQ firewall.
Leverage predefined Python SDK and access data quality as a code.
Use data quality rules in complex projects and expose them as a service for other apps.

4 Platform flexibility

Processing
Data Integrations
Deployment and Scalability

Prevent data quality issues, and stop bad data from entering your systems with a DQ firewall.
Leverage predefined Python SDK and access data quality as a code.
Use data quality rules in complex projects and expose them as a service for other apps.

Regularly perform data profiling and data cleansing. Identify patterns, distributions and anomalies without writting SQL queries or Excel functions.
Automatically detect data quality issues such as missing values, duplicates, outliers and inconsistencies.
Get alerted about deviations in data loads, data structure, and more.

Implement data quality monitoring. Identify and rectify issues in real-time.
Easily evaluate and monitor data quality from an integrated data catalog. See all data quality metrics and trends such as anomalies in one place.
Address issues promptly thanks to automated alerts and notifications.

The solutions your team needs

Build a World Class Data Governance Program

Fix the declining trust in your data with secure data quality: dissolve data inconsistencies, improve accuracy and restore confidence in your organization’s data assets to deliver trustworthy reporting and analytics.

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