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AI Powered Data Quality Monitoring & Observability Tool

Data Quality Monitor is an advanced data quality tool which leverages Agentic AI for continuous monitoring, automated checks, and anomaly detection ensuring trusted data across your Data Pipelines.
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Datagaps Data Quality Monitor

Benefits of Automating Data Quality Monitoring tools

Automating Data Quality Monitoring helps teams catch issues early, maintain trusted data and reduce manual checks across data pipelines, preventing downstream failures, accelerating analytics and ensuring AI‑ready data.
Data Quality Monitor comes equipped with rule‑based validation, anomaly detection, data profiling and data reconciliation. As a modern data quality tool, it offers dashboards and no‑code automation to ensure reliable data.

Better Decision Making

High‑quality data improves decision‑making across analytics, reporting, and AI initiatives.

Risk Mitigation

Detect issues early with automated Data Quality Monitoring and anomaly detection to reduce compliance and operational risk.
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Cost Reduction

Lower rework and operational costs through continuous Data Quality Monitoring and automated checks.

Why Choose Data Quality Monitor Over Other Data Quality Tools?

Monitor & Score Data Quality

AI‑Driven Rule Generation & Recommendation

Generate and recommend data quality rules from user inputs, metadata, or uploaded documents, reducing manual setup.

Business‑Friendly Rule‑Based Validation

Empower business teams to define validation rules and enable continuous data quality monitoring across datasets and data pipelines.

Multi‑Level Data Quality Scores

Each rule produces a score that rolls up across tables and systems, giving you clear visibility into data health through scorecards.

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Data Quality Dashboard
Observe & Detect Data Issues

Observe & Detect Data Issues

Early Anomaly Detection with Profiling & Data Observability

Monitor patterns, ranges, and trends with ML‑based anomaly detection to identify deviations early and enhance overall data quality monitoring.

Identify Schema and Data Drift

Automatically identify schema changes and data shifts that can impact data quality and downstream systems.

Monitor Quality Thresholds for Critical Data Elements

Set acceptable quality thresholds for key data elements and continuously monitor when data quality falls outside those predefined limits.

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Make your Data AI-Ready

Semantic Data Validation

Validate that key business metrics mean the same thing across systems and reports, catching semantic mismatches that lead to conflicting numbers.

Automated Data Lineage & Impact Analysis

Automatically capture end‑to‑end data lineage to understand origins, transformations and downstream dependencies. View data quality scores within lineage flows to assess impact when issues arise.

Versioned Data Contracts

Enforce consistent schema, types, and rules with versioned data contracts that validate every dataset against the latest approved version.

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Data Catalog Rules
Data Catalog Rules

Accelerate and Scale Data Quality Delivery

Integration with Data Governance and Data Catalog Tools

Integrate with governance and data catalog platforms like Unity Catalog and Collibra to share lineage, metadata, and data quality signals, enabling consistent visibility and stronger cross‑system alignment.

Reusability through Imports, Exports & GIT integration

Reuse and version data quality assets through imports, exports, and Git integration to support consistent deployment across pipelines and environments.

Pipelines & Scheduling

Automate data quality monitoring with scheduled or pipeline‑triggered executions, ensuring continuous validation across ingestion, staging, and warehouse layers.

Track and Manage Data Quality Issues

Continuously track data quality issues as they emerge and evolve, with visibility into issue history, resolution timelines, and outstanding problems.

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Agentic AI - Driven Data Quality Monitoring

Predictive Intelligence

Uses historical profiling to anticipate anomalies and data drift early.

Rule Generation Wizard

Leverages AI to generate and recommend context‑aware data quality rules.

AI-Powered Test Maintenance

Automatically adjusts data quality validations when schema changes occur.

Business-Friendly Cataloging

Uses AI to auto‑generate clear, human‑readable dataset descriptions for better business understanding.

Data Quality Monitor Success stories & Global 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

Kasturi Sen NYU, Data Governance Lead
NYU

NYU Review on Datagaps DQ Monitor

NYU Testimonial For DQ Monitor

❝ Datagaps is a great partner. I love working with them, and we’re excited to incorporate their AI-driven data quality capabilities, so we don’t fall behind.❞

— Kasturi Sen

Senior Data Warehouse Architect, New York University
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.

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Use Cases

Documentation

FAQs

Support

FAQ's about Data Quality Monitor

How does Data Quality Monitor automate data quality monitoring?

Data Quality Monitor automates validation through rule‑based checks, ML‑powered anomaly detection, and continuous monitoring across ingestion, staging, warehouse, and BI layers.

How does Agentic AI improve data quality rule creation in Data Quality Monitor?

Agentic AI generates context-aware validation rules from simple prompts, reduces manual setup, and accelerates the creation of complex business rules without scripting.

What methods does Data Quality Monitor use to ensure accurate and trusted data?

It uses rule‑based validation, data profiling, ML‑based anomaly detection, and source‑to‑target reconciliation to ensure accurate, complete, and consistent data throughout the pipeline.

Can Data Quality Monitor integrate with my existing data governance tools?

Yes. Data Quality Monitor integrates with data governance platforms to share lineage, metadata, and data quality insights for stronger governance and unified visibility.

Why choose Data Quality Monitor over other data quality tools?

Data Quality Monitor offers a more complete approach to data quality than traditional tools by combining automated rule‑based validation, ML‑powered anomaly detection, deep data profiling, and end‑to‑end reconciliation in one unified platform. It supports the full data pipeline from ingestion to BI, provides observability signals to detect issues early, and integrates with governance systems to enhance visibility and control. With dashboards, scorecards, low‑code automation, and Agentic AI for rule creation, Data Quality Monitor helps teams deliver accurate, trusted data at scale while reducing the operational effort typically required to maintain high data quality.

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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