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Data Quality Monitor

Proactive Data Quality with Agentic AI – Predict, Prevent, Govern.
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Datagaps Data Quality Monitor

Benefits of DataOps DQ Monitor for Data Quality Monitoring tools

Continuously monitor and improve data quality using AI-driven validation, low-code rules, and visual dashboards – ensuring completeness, integrity, and anomaly detection across data pipelines.

Better Decision Making

Reliable data results in better decisions that impact the bottom line for organizations

Risk Mitigation

Improving Data Quality minimizes the risks associated with Compliance and Reputation

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Cost Reduction

Continuous Data Quality Monitoring reduces operational costs associated with rework in data intensive projects

Key Features

Simplify Data Management with AI-driven autonomous data quality empowering Business Users and Data Owners with Trustable data.

Monitor & Score

Data Quality Dashboard

Displays a trend of Data Quality Scores for the enterprise. Users can drill down to review the scores at Data Model, Table, Data Element, and Data Quality Dimension levels.

Data Quality Score

Computes Data Quality score for data assets and displays trend reports on a Data Quality Dashboard

DQ Dimensions

Categorizes data checks into data quality dimensions such as Data Accuracy, Consistency, Unicity, Conformity, Completeness.

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Data Quality Dashboard
DQ Dimensions

Observe & Detect

Data Observability

Profiles data assets and compiles statistics from the past. Predicts expected values and detects deviations ahead of time.

Semantic Data Types (PII/PHI detection)

AI-enabled detection of Semantic Data Types classifies data (PII, PHI) and applies data type-specific quality rules.

Reference Data Checks

Validate the list of values in categorical data elements with the reference data to ensure that the values are as expected.

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Govern & Control

Data Catalog

Crawls data sources for metadata information about Tables, Columns, and changes to them over time.

Business Data Rules

Data rules can be defined centrally by business users and applied automatically to data elements in multiple data sources.

Common Data Model

A dictionary of enterprise data elements and their definitions that can be mapped to physical tables and datasets.

Data Reconciliation

Check for data integrity by matching data from various sources to ensure that data is consistent across systems.

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Data Catalog Rules

Agentic AI for Data Quality

Predictive Intelligence

Uses historical profiling to anticipate anomalies and data drift early.

Proactive Defect Prevention

Suggests context-aware data quality rules across dimensions like accuracy and completeness.

AI-Powered Test Maintenance

Automatically adjusts data quality validations when schema changes occur.

Business-Friendly Cataloging

Auto-generates clear, human-readable descriptions for datasets and supports cross-domain validation for better business understanding.

Case Studies

Our clients receive great value from our data validation solutions

New Fintech-Industry-Leader

Enhancing AI/ML Outcomes Through Comprehensive Data Validation

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Data Governance and Data Quality Collaboration

Enhancing SIS Data Quality with Automated Collibra Validation

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Health Insurance with APCD Submissions

Scaling APCD Data Quality Across States with Automation

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Customer Testimonial

Ekatmika Khatua tds speaker 2024
Quantumstreet AI

Quantum Street AI Review on Datagaps DQ Monitor

❝ The rule-based verification and automated re-fetching of data have removed human error from our pipeline, ensuring higher data accuracy.❞

— Ekatmika Khatua

Managing Director and India Head
Video
“ The ETL validator tool provides an intuitive interface and a strong Datagaps partnership. It ensures 100% data validation, cost-effectiveness, and boosts productivity with responsive support. “
Michael Lucas
Michael LucasApplications Programmer 3 - CNPRC
“ Building strong customer relationship and implementing innovative solutions is the core value we follow. Here’s what our customers around the global think of Datagaps.handouts.“
Ajit Oak
Ajit OakDirector, Head of Business Intelligence - Broadcom
“ The ETL validator tool provides an intuitive interface and a strong Datagaps partnership. It ensures 100% data validation, cost-effectiveness, and boosts productivity with responsive support. “
Michael Lucas
Michael LucasApplications Programmer 3 - CNPRC
“ Building strong customer relationship and implementing innovative solutions is the core value we follow. Here’s what our customers around the global think of Datagaps.handouts.“
Ajit Oak
Ajit OakDirector, Head of Business Intelligence - Broadcom

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Data Quality Monitor Resources

Try Data Quality Monitor free for 14 days or contact us for a demo.

14 Days Free Trial

Datasheet

Use Cases

Documentation

FAQs

Support

FAQ's about Data Quality Monitor

What is Agentic AI in the context of Data Quality?

Agentic AI refers to AI systems that act proactively and autonomously to improve data quality. Instead of just detecting issues, it predicts, prevents, and adapts to changes in your data environment.

How does Agentic AI help prevent data quality defects?

It uses historical profiling and context-aware intelligence to suggest rules across dimensions like accuracy, completeness, and consistency—helping you stop defects before they occur.

Can Agentic AI handle schema changes automatically?

Yes, With AI-powered test maintenance, it automatically adjusts data quality validations when schema changes happen, reducing manual effort and ensuring continuous compliance.

Does Agentic AI improve business understanding of data?

Absolutely, It auto-generates clear, human-readable descriptions for datasets and supports cross-domain validation, making data easier to interpret for business users.

What is DQM?

Data Quality Management (DQM) refers to the processes and technologies involved in ensuring the accuracy, completeness, reliability, and relevancy of an organization’s data throughout its lifecycle.

Why is DQM important?

DQM is crucial because it ensures that data is accurate and trustworthy, which is essential for making informed decisions, maintaining regulatory compliance, and enhancing customer satisfaction.

What are Data Quality Dimensions?

Data Quality Dimensions include accuracy, completeness, consistency, reliability, and timeliness, which collectively help assess the value and effectiveness of data in supporting business processes.

How does the Data Quality Monitor integrate with existing data systems?

Data Quality Monitor seamlessly integrates with various data systems, offering flexibility and ease of use without disrupting your current data processes.

Can Data Quality Monitor handle complex data quality scenarios?

Absolutely. It’s designed to manage complex data quality scenarios, ensuring accuracy across diverse data sets.

What makes Data Quality Monitor stand out from competitors?

Its user-friendly interface, zero-code deployment, and advanced features like automated reporting and notifications set it apart.

How does Data Quality Monitor improve decision-making?

Providing accurate and reliable data quality assessments ensures that business decisions are based on trustworthy data.

Can non-technical users easily navigate Data Quality Monitor?

Yes, its intuitive design and zero-code deployment make it available to users of all non-technical backgrounds as well.

Blogs/Videos

Data Validation for Regulatory Compliance in ETL Integrating Data Quality Checks into DevOps Workflows

Data Validation for Regulatory Compliance in ETL: Integrating Data Quality Checks into DevOps Workflows

Data Reconciliation for SOX Compliance: Taming the Transaction Tsunami

Monitoring Unknown Data Issues

Monitoring Unknown Data Issues: The Insurance Policy Your Data Needs

Data Quality Monitor Platforms
role of Data data-quality in AI-ML model training
Enterprise Data Quality Monitoring Management

DataOps Suite Data Quality Monitor - 14 Days Free Trial

DataOps Suite – Data Quality Monitor helps define data rules using an easy-to-use web interface and share the results with your business users.

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